Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-77c89778f8-vsgnj Total loading time: 0 Render date: 2024-07-21T09:15:31.573Z Has data issue: false hasContentIssue false

References

Published online by Cambridge University Press:  17 April 2021

Pierre Baldi
Pierre Baldi
Affiliation:
University of California, Irvine
Get access

Summary

A summary is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
Chapter
Information
Deep Learning in Science , pp. 313 - 364
Publisher: Cambridge University Press
Print publication year: 2021

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

[1] Aaboud, Morad, Aad, Georges, Abbott, Brad, Abbott, Dale Charles, Abdinov, Ovsat, Abhayasinghe, Deshan Kavishka, Abidi, Syed Haider, AbouZeid, O.S., Abraham, N.L., Abramowicz, Halina, et al. Constraints on mediator-based dark matter and scalar dark energy model using √s = 13 TeV pp colliion data collected by the ATLAS detector. Journal of High Energy Physics, 5:142, 2019.Google Scholar
[2] Aad, Georges, Butterworth, J.M., Thion, J., Bratzler, U., Ratoff, P.N., Nickerson, R.B., Seixas, J.M., Grabowska-Bold, I., Meisel, F., Lokwitz, S., et al. The ATLAS experiment at the CERN large hadron collider. Journal of Instrumentation, 3:S08003, 2008.Google Scholar
[3] Aad, Georges, Kupco, Alexander, Webb, Samuel, Dreyer, Timo, Wang, Yufeng, Jakobs, Karl, Spousta, Martin, Cobal, Marina, Wang, Peilong, Schmitt, Stefan, et al. dCP-violating phase phi_s in b0s → j/ψϕ decays in ATLAS at 13 tev. Technical report, ATLAS-BPHY-2018-01-003, 2020.Google Scholar
[4] Abad, Enrique, Zenn, Roland K., and Kästner, Johannes. Reaction mechanism of monoamine oxidase from qm/mm calculations. The Journal of Physical Chemistry B, 117(46):14238–14246, 2013. PMID: 24164690.Google Scholar
[5] Abbe, Emmanuel, Shpilka, Amir, and Wigderson, Avi. Reed–Muller codes for random erasures and errors. IEEE Transactions on Information Theory, 61(10):5229– 5252, 2015.CrossRefGoogle Scholar
[6] Abbeel, Pieter and Ng, Andrew Y.. Apprenticeship learning via inverse reinforcement learning. In Proceedings of the Twenty-First International Conference on Machine Learning, page 1. ACM, 2004.Google Scholar
[7] Abdesselam, A. et al. Boosted objects: A Probe of beyond the Standard Model physics. Eur. Phys. J., C71:1661, 2011.Google Scholar
[8] Abu-Mostafa, Yaser and St. Jacques, J.. Information capacity of the Hopfield model. IEEE Transactions on Information Theory, 31(4):461–464, 1985.Google Scholar
[9] Ackley, D.H., Hinton, G.E., and Sejnowski, T.J.. A learning algorithm for Boltzmann machines. Cognitive Science, 9:147–169, 1985.Google Scholar
[10] Adams, D., Arce, A., Asquith, L., Backovic, M., Barillari, T., et al. Towards an understanding of the correlations in jet substructure. Eur. Phys. J. C 75: 409 (2015).CrossRefGoogle Scholar
[11] Aghion, Stefano, Ahlén, O., Amsler, C., Ariga, A., Ariga, T., Belov, A.S., Berggren, Karl, Bonomi, G., Bräunig, P., Bremer, J., et al. A moiré deflectometer for antimatter. Nature Communications, 5(1):1–6, 2014.Google Scholar
[12] Agostinelli, F., Ceglia, N., Shahbaba, B., Sassone-Corsi, P., and Baldi, P.. What time is it? Deep learning approaches for circadian rhythms. Bioinformatics, 32(12):i8– i17, 2016.CrossRefGoogle ScholarPubMed
[13] Agostinelli, Forest, Hoffman, Matthew, Sadowski, Peter, and Baldi, Pierre. Learning activation functions to improve deep neural networks. arXiv:1412.6830, 2014.Google Scholar
[14] Agostinelli, Forest, McAleer, Stephen, Shmakov, Alexander, and Baldi, Pierre. Solving the Rubik’s cube with deep reinforcement learning and search. Nature Machine Intelligence, 1(8):356–363, 2019.Google Scholar
[15] Agrafiotis, D.K., Lobanov, V.S., and Salemme, F.R.. Combinatorial informatics in the post-genomics era. Nature Reviews Drug Discovery, 1:337–346, 2002.Google Scholar
[16] Ajello, M., Albert, A., Atwood, W.B., Barbiellini, G., Bastieri, D., Bechtol, K., Bellazzini, Ronaldo, Bissaldi, E., Blandford, R.D., Bloom, E.D., et al. Fermilat observations of high-energy γ-ray emission toward the galactic center. The Astrophysical Journal, 819(1):44, 2016.Google Scholar
[17] Alipanahi, Babak, Delong, Andrew, Weirauch, Matthew T., and Frey, Brendan J.. Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning. Nat. Biotechnol., 33(8):831–8, Aug 2015.Google Scholar
[18] Alman, Josh, Chan, Timothy M., and Williams, Ryan. Polynomial representations of threshold functions and algorithmic applications. In 2016 IEEE 57th Annual Symposium on Foundations of Computer Science (FOCS), pages 467–476. IEEE, 2016.Google Scholar
[19] Altheimer, A. et al. Jet substructure at the Tevatron and LHC: new results, new tools, new benchmarks. J. Phys., G39:063001, 2012.Google Scholar
[20] Altheimer, A. et al. Boosted objects and jet substructure at the LHC. Report of BOOST2012, held at IFIC Valencia, 23rd-27th of July 2012. Eur. Phys. J., C74(3):2792, 2014.Google Scholar
[21] Alwall, Johan et al. MadGraph 5: Going Beyond. JHEP, 1106:128, 2011.Google Scholar
[22] Amari, Shun-Ichi. Characteristics of random nets of analog neuron-like elements. IEEE Transactions on Systems, Man, and Cybernetics, (5):643–657, 1972.Google Scholar
[23] Amari, Shun-Ichi. Topographic organization of nerve fields. Bulletin of Mathematical Biology, 42(3):339–364, 1980.Google Scholar
[24] Amari, Shun-ichi. Information Geometry and Its applications, Springer, 2016.Google Scholar
[25] Daniel, J. Amit, Hanoch Gutfreund, and Haim Sompolinsky. Spin-glass models of neural networks. Physical Review A, 32(2):1007, 1985.Google Scholar
[26] Amit, Daniel J., Gutfreund, Hanoch, and Sompolinsky, Haim. Storing infinite numbers of patterns in a spin-glass model of neural networks. Physical Review Letters, 55(14):1530, 1985.Google Scholar
[27] Amit, Daniel J., Gutfreund, Hanoch, and Sompolinsky, Haim. Information storage in neural networks with low levels of activity. Physical Review A, 35(5):2293, 1987.Google Scholar
[28] Amit, Daniel J., Gutfreund, Hanoch, and Sompolinsky, Haim. Statistical mechanics of neural networks near saturation. Annals of physics, 173(1):30–67, 1987.Google Scholar
[29] Amole, C. et al. Description and first application of a new technique to measure the gravitational mass of antihydrogen. Nature Communications, 4:1785, 2013.Google Scholar
[30] Amole, C. et al. The alpha antihydrogen trapping apparatus. Nucl. Instr. Meth. A, 735:319–340, 2014.Google Scholar
[31] Amoretti, M. et al. The athena antihydrogen apparatus. Nucl. Instr. Meth. A, 518:679–711, 2004.Google Scholar
[32] Anderson, Charles W.. Learning to control an inverted pendulum using neural networks. Control Systems Magazine, IEEE, 9(3):31–37, 1989.Google Scholar
[33] Andre, David and Russell, Stuart J.. State abstraction for programmable reinforcement learning agents. In AAAI/IAAI, pages 119–125, 2002.Google Scholar
[34] Andrejić, Milica and Mata, Ricardo A.. Local hybrid qm/qm calculations of reaction pathways in metallobiosites. Journal of Chemical Theory and Computation, 10(12):5397–5404, 2014. PMID: 26583223.Google Scholar
[35] Andresen, G.B. et al. Confinement of antihydrogen for 1,000 seconds. Nat. Phys., 7:558–564, 2011.Google Scholar
[36] Andresen, G.B., Ashkezari, M.D., Baquero-Ruiz, M., Bertsche, W., Bowe, Paul David, Butler, E., Cesar, C.L., Chapman, S., Charlton, M., Deller, A., et al. Trapped antihydrogen. Nature, 468(7324):673–676, 2010.Google Scholar
[37] Andronico, Alessio, Randall, Arlo, Benz, Ryan W., and Baldi, Pierre. Data-Driven High-Throughput prediction of the 3-D structure of small molecules: Review and progress. Journal of Chemical Information and Modeling, 51(4):760–776, April 2011.Google Scholar
[38] Angermueller, Christof, Lee, Heather J., Reik, Wolf, and Stegle, Oliver. Deepcpg: accurate prediction of single-cell DNA methylation states using deep learning. Genome Biol., 18(1):67, Apr 2017.Google Scholar
[39] Anthony, Martin. Classification by polynomial surfaces. Discrete Applied Mathematics, 61(2):91–103, 1995.Google Scholar
[40] Anthony, Martin. Discrete Mathematics of Neural Networks: Selected Topics. SIAM, 2001.Google Scholar
[41] Aprile, Elena, Aalbers, J., Agostini, F., Alfonsi, M., Amaro, F.D., Anthony, M., Antunes, B., Arneodo, F., Balata, M., Barrow, P., et al. The XENON1T dark matter experiment. The European Physical Journal C, 77(12):881, 2017.Google Scholar
[42] Arabshahi, Forough, Singh, Sameer, and Anandkumar, Animashree. Combining symbolic expressions and black-box function evaluations for training neural programs. In International Conference on Learning Representations (ICLR), 2018.Google Scholar
[43] Arabshahi, Forough, Singh, Sameer, and Anandkumar, Animashree. Towards solving differential equations through neural programming. In ICML Workshop on Neural Abstract Machines and Program Induction (NAMPI), 2018.Google Scholar
[44] Arimoto, S.. An algorithm for computing the capacity of arbitrary discrete memoryless channels. Information Theory, IEEE Transactions on, 18(1):14–20, 1972.CrossRefGoogle Scholar
[45] Arnold, Vladimir I.. On functions of three variables. In Dokl. Akad. Nauk SSSR, volume 114, pages 679–681, 1957.Google Scholar
[46] Arora, Sanjeev and Barak, Boaz. Computational Complexity: A Modern Approach. Cambridge University Press, 2009.Google Scholar
[47] Aschbacher, M., Baldi, P., Baum, E.B., and Wilson, R.M.. Embeddings of ultrametric spaces in finite-dimensional structures. SIAM Journal of Algebraic and Discrete Methods, 8(4):564–577, 1987.Google Scholar
[48] Asgari, E. and Mofrad, M.R.K.. Continuous distributed representation of biological sequences for deep proteomics and genomics. PLoS ONE, 10(11):e0141287, 2015.Google Scholar
[49] Aspnes, James, Beigel, Richard, Furst, Merrick, and Rudich, Steven. The expressive power of voting polynomials. Combinatorica, 14(2):135–148, 1994.Google Scholar
[50] Aszodi, A., Gradwell, M.J., and Taylor, W.R.. Global fold determination from a small number of distance restraints. J. Mol. Biol., 251:308–326, 1995.Google Scholar
[51] Atkeson, Christopher G. and Santamaria, Juan Carlos. A comparison of direct and model-based reinforcement learning. In Proceedings of International Conference on Robotics and Automation, volume 4, pages 3557–3564. IEEE, 1997.Google Scholar
[52] Collaboration, ATLAS. ATLAS experiment at the CERN Large Hadron Collider. JINST, 3:S08003, 2008.Google Scholar
[53] ATLAS Collaboration. Luminosity determination in pp collisions at s = 7 TeV using the ATLAS Detector at the LHC. Eur. Phys. J, C73:2518, 2013.Google Scholar
[54] Aurisano, A., Radovic, A., Rocco, D., Himmel, A., Messier, M.D., Niner, E., Pawloski, G., Psihas, F., Sousa, A., and Vahle, P.. A convolutional neural network neutrino event classifier. Journal of Instrumentation, 11(09):P09001, 2016.Google Scholar
[55] Azencott, C., Ksikes, A., Swamidass, S.J., Chen, J., Ralaivola, L., and Baldi, P.. One-to four- dimensional kernels for small molecules and predictive regression of physical, chemical, and biological properties. Journal of Chemical Information and Modeling, 47(3):965–974, 2007.Google Scholar
[56] Ba, Jimmy Lei, Kiros, Jamie Ryan, and Hinton, Geoffrey E.. Layer normalization. arXiv:1607.06450, 2016.Google Scholar
[57] Baars, Bernard J. et al. In the Theater of Consciousness: The Workspace of the Mind. Oxford University Press, USA, 1997.Google Scholar
[58] Baars, Bernard J. and Gage, Nicole M.. Cognition, Brain, and Consciousness: Introduction to Cognitive Neuroscience. Academic Press, 2010.Google Scholar
[59] Babadi, Baktash and Sompolinsky, Haim. Sparseness and expansion in sensory representations. Neuron, 83(5):1213–1226, 2014.Google Scholar
[60] Bach, Sebastian, Binder, Alexander, Montavon, Grégoire, Klauschen, Frederick, Müller, Klaus-Robert, and Samek, Wojciech. On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PloS One, 10(7):e0130140, 2015.Google Scholar
[61] Bahdanau, Dzmitry, Cho, Kyunghyun, and Bengio, Yoshua. Neural machine translation by jointly learning to align and translate. arXiv:1409.0473, 2014.Google Scholar
[62] Bahr, M. et al. Herwig++ Physics and Manual. Eur. Phys. J., C58:639–707, 2008.Google Scholar
[63] Bajusz, Dávid, Rácz, Anita, and Héberger, Károly. Why is tanimoto index an appropriate choice for fingerprint-based similarity calculations? Journal of cheminformatics, 7(1):20, 2015.Google Scholar
[64] Baker, D. and Sali, A.. Protein structure prediction and structural genomics. Science, 294:93–96, 2001.Google Scholar
[65] Baldi, P.. Symmetries and learning in neural network models. Physical Review Letters, 59(17):1976–1978, 1987.Google Scholar
[66] Baldi, P.. Group actions and learning for a family of automata. Journal of Computer and System Sciences, 36(2):1–15, 1988.Google Scholar
[67] Baldi, P.. Neural networks, acyclic orientations of the hypercube and sets of orthogonal vectors. SIAM Journal Discrete Mathematics, 1(1):1–13, 1988.Google Scholar
[68] Baldi, P.. Neural networks, orientations of the hypercube and algebraic threshold functions. IEEE Transactions on Information Theory, 34(3):523–530, 1988.Google Scholar
[69] Baldi, P.. Gradient descent learning algorithms overview: A general dynamical systems perspective. IEEE Transactions on Neural Networks, 6(1):182–195, 1995.Google Scholar
[70] Baldi, P.. Boolean autoencoders and hypercube clustering complexity. Designs, Codes, and Cryptography, 65(3):383–403, 2012.Google Scholar
[71] Baldi, P.. The inner and outer approaches for the design of recursive neural networks architectures. Data Mining and Knowledge Discovery, DOI: 10.1007/s10618-017-0531-0:1–13, 2017. Available at: http://link.springer.com/article/10.1007/s10618–017-0531-0.Google Scholar
[72] Baldi, P.. Deep learning in biomedical data science. Annual Review of Biomedical Data Science, 1:181–205, 2018.Google Scholar
[73] Baldi, P., Benz, R. W., Hirschberg, D., and Swamidass, S.J.. Lossless compression of chemical fingerprints using integer entropy codes improves storage and retrieval. Journal of Chemical Information and Modeling, 47(6):2098–2109, 2007.Google Scholar
[74] Baldi, P. and Brunak, S.. Bioinformatics: The Machine Learning Approach. MIT Press, Cambridge, MA, 2001. Second edition.Google Scholar
[75] Baldi, P., Brunak, S., Frasconi, P., Pollastri, G., and Soda, G.. Exploiting the past and the future in protein secondary structure prediction. Bioinformatics, 15:937–946, 1999.Google Scholar
[76] Baldi, P. and Chauvin, Y.. Neural networks for fingerprint recognition. Neural Computation, 5(3):402–418, 1993.CrossRefGoogle Scholar
[77] Baldi, P. and Chauvin, Y.. Smooth on-line learning algorithms for hidden Markov models. Neural Computation, 6(2):305–316, 1994.Google Scholar
[78] Baldi, P. and Chauvin, Y.. Protein modeling with hybrid hidden Markov model/neural networks architectures. In Proceedings of the 1995 Conference on Intelligent Systems for Molecular Biology (ISMB95), in Cambridge (UK). The AAAI Press, Menlo Park, CA, 1995.Google Scholar
[79] Baldi, P., Chauvin, Y., Hunkapillar, T., and McClure, M.. Hidden Markov models of biological primary sequence information. PNAS USA, 91(3):1059–1063, 1994.Google Scholar
[80] Baldi, P. and Hornik, K.. Neural networks and principal component analysis: learning from examples without local minima. Neural Networks, 2(1):53–58, 1988.Google Scholar
[81] Baldi, P. and Hornik, K.. Learning in linear networks: a survey. IEEE Transactions on Neural Networks, 6(4):837–858, 1994. 1995.Google Scholar
[82] Baldi, P. and Itti, L.. Of bits and wows: A Bayesian theory of surprise with applications to attention. Neural Networks, 23(5):649–666, 2010.Google Scholar
[83] Baldi, P. and Lu, Z.. Complex-valued autoencoders. Neural Networks, 33:136–147, 2012.Google Scholar
[84] Baldi, P., Lu, Z., and Sadowski, P.. Learning in the machine: the symmetries of the deep learning channel. Neural Networks, 95:110–133, 2017.Google Scholar
[85] Baldi, P., Lu, Z., and Sadowski, P.. Learning in the machine: Random backpropagation and the deep learning channel. Artificial Intelligence, 260:1–35, 2018. Also: arXiv:1612.02734.Google Scholar
[86] Baldi, P. and Pineda, F.. Contrastive learning and neural oscillations. Neural Computation, 3(4):526–545, 1991.Google Scholar
[87] Baldi, P. and Pollastri, G.. A machine learning strategy for protein analysis. IEEE Intelligent Systems. Special Issue on Intelligent Systems in Biology, 17(2), 2002.Google Scholar
[88] Baldi, P. and Pollastri, G.. The principled design of large-scale recursive neural network architectures–DAG-RNNs and the protein structure prediction problem. Journal of Machine Learning Research, 4:575–602, 2003.Google Scholar
[89] Baldi, P. and Rinott, Y.. Asymptotic normality of some graph related statistics. Journal of Applied Probability, 26:171–175, 1989.Google Scholar
[90] Baldi, P. and Rinott, Y.. On normal approximations of distributions in terms of dependency graphs. Annals of Probability, 17(4):1646–1650, 1989.Google Scholar
[91] Baldi, P. and Sadowski, P.. The dropout learning algorithm. Artificial Intelligence, 210C:78–122, 2014.Google Scholar
[92] Baldi, P. and Sadowski, P.. Learning in the machine: Recirculation is random backpropagation. Neural Networks, 108:479–494, 2018.Google Scholar
[93] Baldi, P., Sadowski, P., and Whiteson, D.. Searching for exotic particles in high-energy physics with deep learning. Nature Communications, 5, 2014.Google Scholar
[94] Baldi, P., Sadowski, P., and Whiteson, D.. Enhanced Higgs boson to τ τ search with deep learning. Phys. Rev. Letters, 114:111801, 2015.Google Scholar
[95] Baldi, Pierre. Linear learning: Landscapes and algorithms. In Advances in Neural Information Processing Systems, pages 65–72, 1989.Google Scholar
[96] Baldi, Pierre. Data-driven high-throughput prediction of the 3-d structure of small molecules: review and progress. a response to the letter by the Cambridge Crystallographic Data Centre. Journal of Chemical Information and Modeling, 51(12):3029–3029, 2011.Google Scholar
[97] Baldi, Pierre, Bauer, Kevin, Eng, Clara, Sadowski, Peter, and Whiteson, Daniel. Jet substructure classification in high-energy physics with deep neural networks. Phys. Rev. D, 93:094034, May 2016.Google Scholar
[98] Baldi, Pierre, Bian, Jianming, Hertel, Lars, and Li, Lingge. Improved energy reconstruction in nova with regression convolutional neural networks. Physical Review D, 99(1):012011, 2019.Google Scholar
[99] Baldi, Pierre and Chauvin, Yves. Hybrid modeling, HMM/NN architectures, and protein applications. Neural Computation, 8(7):1541–1565, 1996.Google Scholar
[100] Baldi, Pierre, Cranmer, Kyle, Faucett, Taylor, Sadowski, Peter, and Whiteson, Daniel. Parameterized neural networks for high-energy physics. The European Physical Journal C, 76(5):235, Apr 2016.Google Scholar
[101] Baldi, Pierre, Rinott, Yosef, and Stein, Charles. A normal approximation for the number of local maxima of a random function on a graph. In Probability, Statistics, and Mathematics, pages 59–81. Elsevier, 1989.Google Scholar
[102] Baldi, Pierre and Sadowski, Peter. A theory of local learning, the learning channel, and the optimality of backpropagation. Neural Networks, 83:61–74, 2016.Google Scholar
[103] Baldi, Pierre and Venkatesh, Santosh S.. Number of stable points for spin-glasses and neural networks of higher orders. Physical Review Letters, 58(9):913, 1987.Google Scholar
[104] Baldi, Pierre and Venkatesh, Santosh S.. Random interactions in higher order neural networks. IEEE Transactions on Information Theory, 39(1):274–283, 1993.Google Scholar
[105] Baldi, Pierre and Vershynin, Roman. The capacity of feedforward neural networks. Neural Networks, 1(4):699–729, 2019. Also: arXiv preprint arXiv:1901.00434.Google Scholar
[106] Baldi, Pierre and Vershynin, Roman. Polynomial threshold functions, hyperplane arrangements, and random tensors. SIAM Journal on Mathematics of Data Science, 1(4):699–729, 2019. Also: arXiv preprint arXiv:1803.10868.Google Scholar
[107] Bar-Sinai, Yohai, Hoyer, Stephan, Hickey, Jason, and Brenner, Michael P.. Learning data-driven discretizations for partial differential equations. Proceedings of the National Academy of Sciences, 116(31):15344–15349, Jul 2019.Google Scholar
[108] Barahona, Francisco. On the computational complexity of Ising spin glass models. Journal of Physics A: Mathematical and General, 15(10):3241, 1982.Google Scholar
[109] Barron, Andrew, Rissanen, Jorma, and Yu, Bin. The minimum description length principle in coding and modeling. IEEE Transactions on Information Theory, 44(6):2743–2760, 1998.Google Scholar
[110] Peter, L Bartlett, Nick Harvey, Christopher Liaw, and Abbas Mehrabian. Nearly-tight VC-dimension and pseudodimension bounds for piecewise linear neural networks. J. Mach. Learn. Res., 20:63–1, 2019.Google Scholar
[111] Bartol, Thomas M. Jr., Bromer, Cailey, Kinney, Justin, Chirillo, Michael A., Bourne, Jennifer N., Harris, Kristen M., and Sejnowski, Terrence J.. Nanoconnectomic upper bound on the variability of synaptic plasticity. eLife, 4, 2015.Google Scholar
[112] Baum, Eric B. and Haussler, David. What size net gives valid generalization? In Advances in Neural Information Processing Systems, pages 81–90, 1989.Google Scholar
[113] Beaulieu-Jones, Brett K., Greene, Casey S., et al. Semi-supervised learning of the electronic health record for phenotype stratification. Journal of Biomedical Informatics, 64:168–178, 2016.Google Scholar
[114] Beigel, Richard, Reingold, Nick, and Spielman, Daniel. Pp is closed under intersection. Journal of Computer and System Sciences, 50(2):191–202, 1995.Google Scholar
[115] Bell, Anthony J. and Sejnowski, Terrence J.. The “independent components” of natural scenes are edge filters. Vision Research, 37(23):3327–3338, 1997.Google Scholar
[116] Bellemare, Marc G., Ostrovski, Georg, Guez, Arthur, Thomas, Philip S., and Munos, Rémi. Increasing the action gap: New operators for reinforcement learning. In AAAI, pages 1476–1483, 2016.Google Scholar
[117] Bellman, Richard. The Theory of Dynamic Programming. Technical report, DTIC Document, 1954.Google Scholar
[118] Benedetti, Erica, Simonneau, Antoine, Hours, Alexandra, Amouri, Hani, Penoni, Andrea, Palmisano, Giovanni, Malacria, Max, Goddard, Jean-Philippe, and Fensterbank, Louis. (pentamethylcyclopentadienyl) iridium dichloride dimer {[IrCp* Cl2] 2}: A novel efficient catalyst for the cycloisomerizations of homopropargylic diols and n-tethered enynes. Advanced Synthesis & Catalysis, 353 (11-12):1908–1912, 2011.Google Scholar
[119] Benkö, G., Flamm, C., and Stadler, P.F.. A graph-based toy model of chemistry. Journal of Chemical Information and Computer Sciences, 43(4):1085–1093, May 2003.Google Scholar
[120] Bergen, Karianne J., Johnson, Paul A., De Hoop, Maarten V., and Beroza, Gregory C.. Machine learning for data-driven discovery in solid-earth geoscience, Science 363(6433):eaau0323, 2019. DOI: 10.1126/science.aau0323Google Scholar
[121] Beringer, J. et al. Review of particle physics. Phys. Rev. D, 86:010001, Jul 2012.Google Scholar
[122] Berman, Helen, Henrick, Kim, Nakamura, Haruki, and Markley, John L.. The Worldwide Protein Data Bank (WWPDB): ensuring a single, uniform archive of PDB data. Nucleic Acids Research, 35(suppl_1):D301–D303, 2007.Google Scholar
[123] Bernardo, J., Berger, J., Dawid, A., and Smith, A.. Some Bayesian numerical analysis. Bayesian Statistics, 4:345–363, 1992.Google Scholar
[124] Bernardo, José M. and Smith, Adrian F.M.. Bayesian Theory. IOP Publishing, 2001.Google Scholar
[125] Bertone, Gianfranco and Merritt, David. Dark matter dynamics and indirect detection. Modern Physics Letters A, 20(14):1021–1036, 2005.Google Scholar
[126] Bertsekas, Dimitri P. and Tsitsiklis, John N.. Neuro-dynamic Programming. Athena Scientific Belmont, MA, 1996.Google Scholar
[127] Beucler, Tom, Pritchard, Michael, Rasp, Stephan, Ott, Jordan, Baldi, Pierre, and Gentine, Pierre. Enforcing analytic constraints in neural-networks emulating physical systems. Phys. Rev. Lett., 2021. In press. Also: http://arxiv.org/abs/1909.00912.Google Scholar
[128] Blahut, R.E.. Computation of channel capacity and rate-distortion functions. Information Theory, IEEE Transactions on, 18(4):460–473, 1972.Google Scholar
[129] Blahut, R.E.. Principles and Practice of Information Theory. Addison-Wesley, Reading, MA, 1987.Google Scholar
[130] Block, H.D., and Levin, S.A.. On the boundedness of an iterative procedure for solving a system of linear inequalities. Proceedings of the American Mathematical Society, 26:229–235, 1970.Google Scholar
[131] Blom, Nikolaj, Sicheritz-Pontén, Thomas, Gupta, Ramneek, Gammeltoft, Steen, and Brunak, Søren. Prediction of post-translational glycosylation and phosphorylation of proteins from the amino acid sequence. Proteomics, 4(6):1633–1649, 2004.Google Scholar
[132] Blum, A.L. and Rivest, R.L.. Training a 3-node neural network is NP-complete. Neural Networks, 5(1):117–127, 1992.Google Scholar
[133] Blum, Lorenz C. and Reymond, Jean-Louis. 970 million druglike small molecules for virtual screening in the chemical universe database GDB-13. Journal of the American Chemical Society, 131(25):8732–8733, 2009.Google Scholar
[134] Blundell, Charles, Uria, Benigno, Pritzel, Alexander, Li, Yazhe, Ruder-man, Avraham, Leibo, Joel Z., Rae, Jack, Wierstra, Daan, and Hassabis, Demis. Model-free episodic control. arXiv:1606.04460, 2016.Google Scholar
[135] Blurock, Edward. Reaction: system for modeling chemical reactions. Journal of Chemical Information and Computer Sciences, 35(3):607–616, 1995.Google Scholar
[136] Bogojeski, Mihail, Vogt-Maranto, Leslie, Tuckerman, Mark E., Mueller, Klaus-Robert, Kieron Burke Density functionals with quantum chemical accuracy: From machine learning to molecular dynamics. DOI: 10.26434/chem-rxiv.8079917.Google Scholar
[137] Bohacek, R.S., McMartin, C., and Guida, W.C.. The art and practice of structure-based drug design: a molecular modeling perspective. Medicinal Research Reviews, 16(1):3–50, 1996.Google Scholar
[138] Bolton, Thomas and Zanna, Laure. Applications of deep learning to ocean data inference and dubgrid parameterization. Journal of Advances in Modeling Earth Systems, 11(1):376–399, 2019.Google Scholar
[139] Bony, Sandrine and Dufresne, Jean-Louis. Marine boundary layer clouds at the heart of tropical cloud feedback uncertainties in climate models. Geophysical Research Letters, 32(20), 2005.CrossRefGoogle Scholar
[140] Bovier, Anton and Gayrard, Véronique. Hopfield models as generalized random mean field models. In A. Bovier and P. Picco, editors, Mathematical Aspects of Spin Glasses and Neural Networks. Birkhäuser, pages 3–89, 1998.Google Scholar
[141] Bower, James M. and Beeman, David. The Book of GENESIS: Exploring Realistic Neural Models with the GEneral NEural SImulation System. Electronic Library of Science, 1995.Google Scholar
[142] Bower, James M., Beeman, David, and Hucka, Michael. The GENESIS simulation system. In: The Handbook of Brain Theory and Neural Networks. MIT Press, pages 475–478, 2003.Google Scholar
[143] Boyan, Justin and Moore, Andrew W.. Generalization in reinforcement learning: safely approximating the value function. Advances in Neural Information Processing Systems, pages 369–376, 1995.Google Scholar
[144] Boyan, Justin A., Littman, Michael L., et al. Packet routing in dynamically changing networks: A reinforcement learning approach. Advances in Neural Information Processing Systems, pages 671–671, 1994.Google Scholar
[145] Boyd, Stephen and Vandenberghe, Lieven. Convex Optimization. Cambridge University Press, 2004.Google Scholar
[146] Boža, Vladimír, Brejová, Broňa, and Vinař, Tomáš. Deepnano: Deep recurrent neural networks for base calling in minion nanopore reads. PloS one, 12(6):e0178751, 2017.Google Scholar
[147] Brafman, Ronen I. and Tennenholtz, Moshe. R-max – a general polynomial time algorithm for near-optimal reinforcement learning. The Journal of Machine Learning Research, 3:213–231, 2003.Google Scholar
[148] Braiding, Catherine, Wong, Graeme F., Maxted, Nigel I., Romano, Donatella, Burton, Michael G., Blackwell, Rebecca, Filipović, M.D., Freeman, M.S.R., Indermuehle, B., Lau, J., et al. The Mopra Southern Galactic Plane CO Survey– Data Release 3. Publications of the Astronomical Society of Australia, 35, 2018.Google Scholar
[149] Bray, Alan J. and Dean, David S.. Statistics of critical points of Gaussian fields on large-dimensional spaces. Physical Review Letters, 98(15):150201, 2007.Google Scholar
[150] Breiman, Leo. Bagging predictors. Machine Learning, 24(2):123–140, 1996.Google Scholar
[151] Bretherton, Christopher S. and Khairoutdinov, Marat F.. Convective self-aggregation feedbacks in near-global cloud-resolving simulations of an aqua-planet. Journal of Advances in Modeling Earth Systems, 7(4):1765–1787, 2015.Google Scholar
[152] Brown, Gavin, Wyatt, Jeremy, Harris, Rachel, and Yao, Xin. Diversity creation methods: a survey and categorisation. Information Fusion, 6(1):5–20, 2005.Google Scholar
[153] Brown, L.D.. Fundamentals of Statistical Exponential Families. Institute of Mathematical Statistics, Hayward, CA, 1986.Google Scholar
[154] Brown, T.E., LeMay, H.E., Bursten, B.E., and Murphy, C.. Chemistry: The Central Science. Prentice Hall, 2008. 11th Edition.Google Scholar
[155] Bruck, Jehoshua. Harmonic analysis of polynomial threshold functions. SIAM Journal on Discrete Mathematics, 3(2):168–177, 1990.Google Scholar
[156] Bruck, Jehoshua and Blaum, Mario. Neural networks, error-correcting codes, and polynomials over the binary n-cube. IEEE Transactions on Information Theory, 35(5):976–987, 1989.Google Scholar
[157] Brunak, Søfren, Engelbrecht, Jacob, and Knudsen, Steen. Neural network detects errors in the assignment of mRNA splice sites. Nucleic Acids Research, 18(16):4797–4801, 1990.Google Scholar
[158] Brunak, Søren, Engelbrecht, Jacob, and Knudsen, Steen. Prediction of human mRNA donor and acceptor sites from the DNA sequence. Journal of Molecular Biology, 220(1):49–65, 1991.Google Scholar
[159] Bucilua, Cristian, Caruana, Rich, and Niculescu-Mizil, Alexandru. Model compression. In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 535–541. ACM, 2006.Google Scholar
[160] Buck, Robert Creighton. Partition of space. The American Mathematical Monthly, 50(9):541–544, 1943.Google Scholar
[161] Burton, Michael G., Braiding, Catherine, Glueck, Christian, Goldsmith, Paul, Hawkes, Jarryd, Hollenbach, David J., Kulesa, Craig, Martin, Christopher L., Pineda, Jorge L, Rowell, Gavin, et al. The MOPRA southern galactic plane CO survey. Publications of the Astronomical Society of Australia, 30, 2013.Google Scholar
[162] Busoniu, Lucian, Babuska, Robert, and Schutter, Bart De. A comprehensive survey of multiagent reinforcement learning. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, 38(2):156–172, 2008.Google Scholar
[163] Busoniu, Lucian, Babuska, Robert, Bart De Schutter, and Damien Ernst. Reinforcement Learning and Dynamic Programming using Function Approximators. CRC Press, 2010.Google Scholar
[164] Cabitza, Federico, Rasoini, Raffaele, and Gensini, Gian Franco. Unintended consequences of machine learning in medicine. JAMA, 318(6):517–518, 2017.Google Scholar
[165] Cajal, S Ramóny. La fine structure des centres nerveux. The Croonian lecture. Proc. R. Soc. Lond, 55:444–468, 1894.Google Scholar
[166] Cambria, Erik, Liu, Qiang, Li, Kuan, Leung, Victor C.M., Feng, Liang, Ong, Yew-Soon, Lim, Meng-Hiot, Akusok, Anton, Lendasse, Amaury, Francesco Corona, et al. Extreme learning machines. IEEE Intelligent Systems, (6):30–59, 2013.Google Scholar
[167] Canas, G., Poggio, T., and Rosasco, L.. Learning manifolds with K-means and K-flats. In Proceedings of the 2012 Neural Information Processing Conference (NIPS 2012), 2012.Google Scholar
[168] Canetti, Laurent, Drewes, Marco, and Shaposhnikov, Mikhail. Matter and antimatter in the universe. New Journal of Physics, 14(9):095012, 2012.Google Scholar
[169] Cao, Renzhi, Bhattacharya, Debswapna, Hou, Jie, and Cheng, Jianlin. Deepqa: improving the estimation of single protein model quality with deep belief networks. BMC bioinformatics, 17(1):495, 2016.Google Scholar
[170] Nicholas, T. Carnevale and Michael L. Hines. The NEURON book. Cambridge University Press, 2006.Google Scholar
[171] Carreira-Perpinan, Miguel A. and Hinton, Geoffrey E.. On contrastive divergence learning. In AISTATS, volume 10, pages 33–40. Citeseer, 2005.Google Scholar
[172] Carvalho, Carlos M., Polson, Nicholas G., and Scott, James G.. Handling sparsity via the horseshoe. In Artificial Intelligence and Statistics, pages 73–80, 2009.Google Scholar
[173] Carvalho, Carlos M., Polson, Nicholas G., and Scott, James G.. The horseshoe estimator for sparse signals. Biometrika, 97(2):465–480, 2010.Google Scholar
[174] Cassandra, Anthony R., Kaelbling, Leslie Pack, and Littman, Michael L.. Acting optimally in partially observable stochastic domains. In AAAI, volume 94, pages 1023–1028, 1994.Google Scholar
[175] Cazaux, S., Lerch, T., and Aune, S.. Detecteur courbe de particules gazeux, April 30 2014. Patent App. EP20,130,188,550.Google Scholar
[176] Ceglia, Nicholas, Yu, Liu, Chen, Siwei, Agostinelli, Forest, Eckel-Mahan, Kristin, Sassone-Corsi, Paolo, and Baldi, Pierre. Circadiomics: circadian omic web portal. Nucleic Acids Research, 46(W1):W157–W162, 2018.Google Scholar
[177] Chang, P., Grinband, J., Weinberg, B.D., Bardis, M., Khy, M., Cadena, G., Su, M.-Y., Cha, S., Filippi, C.G., Bota, D., et al. Deep-learning convolutional neural networks accurately classify genetic mutations in gliomas. American Journal of Neuroradiology, 39(7):1201–1207, 2018.Google Scholar
[178] Chen, J. and Baldi, P.. No electron left behind: a rule-based expert system to predict chemical reactions and reaction mechanisms. Journal of Chemical Information and Modeling, 49(9):2034–43, 2009. PMID: 19719121.Google Scholar
[179] Chen, J., Swamidass, S.J., Dou, Y., Bruand, J., and Baldi, P.. ChemDB: a public database of small molecules and related chemoinformatics resources. Bioinformatics, 21:4133–4139, 2005.Google Scholar
[180] Chen, J.H. and Baldi, P.. Synthesis explorer: a chemical reaction tutorial system for organic synthesis design and mechanism prediction. Journal of Chemical Education, 85(12):1699, December 2008.Google Scholar
[181] Chen, Yifei, Li, Yi, Narayan, Rajiv, Subramanian, Aravind, and Xie, Xiaohui. Gene expression inference with deep learning. Bioinformatics, 32(12):1832–9, Jun 2016.Google Scholar
[182] Cheng, J. and Baldi, P.. A machine learning information retrieval approach to protein fold recognition. Bioinformatics, 22(12):1456–1463, 2006.Google Scholar
[183] Cheng, J. and Baldi, P.. Improved residue contact prediction using support vector machines and a large feature set. BMC Bioinformatics, 8(1):113, 2007.Google Scholar
[184] Cheng, J., Randall, A., and Baldi, P.. Prediction of protein stability changes for single-site mutations using support vector machines. Proteins: Structure, Function, Bioinformatics, 62(4):1125–1132, 2006.Google Scholar
[185] Cheng, J., Randall, A.Z., Sweredoski, M., and Baldi, P.. Scratch: a protein structure and structural feature prediction server. Nucleic Acids Research, 33:W72–W76, 2005. Web Servers issue.Google Scholar
[186] Cheng, J., Saigo, H., and Baldi, P.. Large-scale prediction of disulphide bridges using kernel methods two-dimensional recursive neural networks, and weighted graph matching. Proteins, 62(3):617–629, 2006.Google Scholar
[187] Cheng, J., Sweredoski, M., and Baldi, P.. Accurate prediction of protein disordered regions by mining protein structure data. Data Mining and Knowledge Discovery, 11(3):213–222, 2005.Google Scholar
[188] Cheng, J., Sweredoski, M.J., and Baldi, P.. Dompro: Protein domain prediction using profiles, secondary structure, relative solvent accessibility, and recursive neural networks. Data Mining and Knowledge Discovery, 13(1):1–10, 2006.Google Scholar
[189] Cheng, J., Tegge, A.N., and Baldi, P.. Machine learning methods for protein structure prediction. IEEE Reviews in Biomedical Engineering, 1:41–49, 2008.Google Scholar
[190] Chiappa, Silvia, Racaniere, Sébastien, Wierstra, Daan, and Mohamed, Shakir. Recurrent environment simulators. arXiv:1704.02254, 2017.Google Scholar
[191] Chicco, Davide, Sadowski, Peter, and Baldi, Pierre. Deep autoencoder neural networks for gene ontology annotation predictions. In Proceedings of the 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, pages 533–540. ACM, 2014.Google Scholar
[192] Ching, Travers, Himmelstein, Daniel S., Beaulieu-Jones, Brett K., Kalinin, Alexandr A., Do, Brian T., Way, Gregory P., Ferrero, Enrico, Agapow, Paul-Michael, Xie, Wei, Rosen, Gail L., et al. Opportunities and obstacles for deep learning in biology and medicine. bioRxiv, page 142760, 2017.Google Scholar
[193] Chmiela, Stefan, Tkatchenko, Alexandre, Sauceda, Huziel E., Poltavsky, Igor, Schütt, Kristof T., and Müller, Klaus-Robert. Machine learning of accurate energy-conserving molecular force fields. Science Advances, 3(5):e1603015, 2017.Google Scholar
[194] Choi, Edward, Bahadori, Mohammad Taha, Schuetz, Andy, Stewart, Walter F., and Sun, Jimeng. Doctor AI: predicting clinical events via recurrent neural networks. In Machine Learning for Healthcare Conference, pages 301–318, 2016.Google Scholar
[195] Chollet, Francois. Keras. GitHub, 2015.Google Scholar
[196] Churchland, Patricia S.. Braintrust: What Neuroscience Tells us about Morality. Princeton University Press, 2018.Google Scholar
[197] Ciresan, D.C., Giusti, A., Gambardella, L.M., and Schmidhuber, J.. Deep neural networks segment neuronal membranes in electron microscopy images. In Advances in Neural Information Processing Systems (NIPS), pages 2852–2860, 2012.Google Scholar
[198] Ciresan, D.C., Meier, U., and Schmidhuber, J.. Multi-column deep neural networks for image classification. In IEEE Conference on Computer Vision and Pattern Recognition CVPR 2012, June 2012. Long preprint arXiv:1202.2745v1 [cs.CV], Feb 2012.Google Scholar
[199] CireşAn, Dan, Meier, Ueli, Masci, Jonathan, and Schmidhuber, Jürgen. Multi-column deep neural network for traffic sign classification. Neural networks, 32:333–338, 2012.Google Scholar
[200] Ciresan, Dan Claudiu, Giusti, Alessandro, Gambardella, Luca Maria, and Schmidhuber, Jürgen. Mitosis detection in breast cancer histology images with deep neural networks. In Proc. MICCAI, volume 2, pages 411–418, 2013.Google Scholar
[201] Clote, P. and Kranakis, E.. Boolean Functions and Computation Models. Springer Verlag, 2002.Google Scholar
[202] CMS Collaboration. Search for light vector resonances decaying to quarks at 13 TeV. CMS-PAS-EXO-16-030, 2016.Google Scholar
[203] Coates, Adam, Lee, Honglak, and Andrew, Y. Ng. An analysis of single-layer networks in unsupervised feature learning. Ann Arbor, 1001:48109, 2010.Google Scholar
[204] Cohen, Michael A. and Grossberg, Stephen. Absolute stability of global pattern formation and parallel memory storage by competitive neural networks. IEEE Transactions on Systems, Man, and Cybernetics, (5):815–826, 1983.Google Scholar
[205] Cohen, Taco and Welling, Max. Group equivariant convolutional networks. In International Conference on Machine Learning, pages 2990–2999, 2016.Google Scholar
[206] Cohen, Taco S., Weiler, Maurice, Kicanaoglu, Berkay, and Welling, Max. Gauge equivariant convolutional networks and the icosahedral cnn. arXiv:1902.04615, 2019.Google Scholar
[207] Coley, Connor W., Jin, Wengong, Rogers, Luke, Jamison, Timothy F., Jaakkola, Tommi S., Green, William H., Barzilay, Regina, and Jensen, Klavs F.. A graph-convolutional neural network model for the prediction of chemical reactivity. Chemical Science, 10(2):370–377, 2019.Google Scholar
[208] ATLAS collaboration et al. Search for new resonances in mass distributions of jet pairs using 139 fb- 1 of pp collisions atv s= 13 tev with the atlas detector. Journal of High Energy Physics, 2020(3):145, 2020.Google Scholar
[209] Corradini, M. et al. Experimental apparatus for annihilation cross-section measurements of low energy antiprotons. Nucl. Instr. Meth. A, 711:12–20, 2013.CrossRefGoogle Scholar
[210] Corradini, M. et al. Scintillating bar detector for antiproton annihilations measurements. Hyperfine Interactions, 233:53–58, 2015.CrossRefGoogle Scholar
[211] Cortes, Corinna and Vapnik, Vladimir. Support-vector networks. Machine Learning, 20(3):273–297, 1995.Google Scholar
[212] Coulom, Rémi. Efficient selectivity and backup operators in Monte-Carlo tree search. In International Conference on Computers and Games, pages 72–83. Springer, 2006.Google Scholar
[213] Cover, T.M. and Thomas, J.A.. Elements of Information Theory. John Wiley, New York, 1991.Google Scholar
[214] Cover, Thomas M.. Geometrical and statistical properties of systems of linear inequalities with applications in pattern recognition. IEEE Transactions on Electronic Computers, (3):326–334, 1965.Google Scholar
[215] Cox, R.T.. Probability, frequency and reasonable expectation. American Journal of Physics, 14:1–13, 1964.Google Scholar
[216] Cranmer, Kyle, Pavez, Juan, and Louppe, Gilles. Approximating likelihood ratios with calibrated discriminative classifiers. arXiv:1506.02169, 2015.Google Scholar
[217] Cristianini, N. and Shawe-Taylor, J.. An Introduction to Support Vector Machines and other Kernel-Based Learning Methods. Cambridge University Press, Cambridge, 2000.Google Scholar
[218] Crites, Robert and Barto, Andrew. Improving elevator performance using reinforcement learning. In Advances in Neural Information Processing Systems 8. Citeseer, 1996.Google Scholar
[219] Le Cun, Y., Boser, B., Denker, J., Henderson, D., Howard, R., Hubbard, W., and Jackel, L.. Handwritten digit recognition with a back-propagation network. In Touretzky, D., editor, Advances in Neural Information Processing Systems, pages 396–404. Morgan Kaufmann, San Mateo, CA, 1990.Google Scholar
[220] Cybenko, George. Approximation by superpositions of a sigmoidal function. Mathematics of Control, Signals, and Systems (MCSS), 2(4):303–314, 1989.Google Scholar
[221] Thaler, J. Krohn, D. and Wang, L.-T.. Jet trimming. JHEP, 1002:084, 2010.Google Scholar
[222] Dame, Thomas M., Hartmann, Dap, and Thaddeus, P.. The Milky Way in molecular clouds: a new complete CO survey. The Astrophysical Journal, 547(2):792, 2001.Google Scholar
[223] Dasgupta, Mrinal, Fregoso, Alessandro, Marzani, Simone, and Powling, Alexander. Jet substructure with analytical methods. Eur. Phys. J., C73(11):2623, 2013.Google Scholar
[224] Dasgupta, Mrinal, Powling, Alexander, and Siodmok, Andrzej. On jet substructure methods for signal jets. JHEP, 08:079, 2015.Google Scholar
[225] Dasgupta, Sanjoy and Gupta, Anupam. An elementary proof of a theorem of Johnson and Lindenstrauss. Random Structures & Algorithms, 22(1):60–65, 2003.Google Scholar
[226] Gutman, Eugene, Tavakoli, Amin, Urban, Gregor, Liu, Frances, Huynh, Nancy, Vranken, David Van, Fooshee, David, Mood, Aaron, and Baldi, Pierre. Deep learning for chemical reaction prediction. Molecular Systems Design & Engineering, 3:442–452, 2018. DOI: 10.1039/c7me00107j.Google Scholar
[227] Davidson, Thomas J., Kloosterman, Fabian, and Wilson, Matthew A.. Hippocampal replay of extended experience. Neuron, 63(4):497–507, 2009.Google Scholar
[228] Day, Nick, Downing, Jim, Adams, Sam, England, N.W., and Murray-Rust, Peter. Crystaleye: automated aggregation, semantification and dissemination of the world’s open crystallographic data. Journal of Applied Crystallography, 45(2):316–323, 2012.Google Scholar
[229] de Bezenac, Emmanuel, Pajot, Arthur, and Gallinari, Patrick. Deep Learning for Physical Processes: Incorporating Prior Scientific Knowledge. Journal of Statistical Mechanics, 124009, 2019.Google Scholar
[230] Farias, Daniela Pucci de and Roy, Benjamin Van. The linear programming approach to approximate dynamic programming. Operations Research, 51(6):850–865, 2003.Google Scholar
[231] de Oliveira, Luke, Kagan, Michael, Mackey, Lester, Nachman, Benjamin, and Schwartzman, Ariel. Jet-images – deep learning edition. Journal of High Energy Physics, 2016(7):69, 2016.Google Scholar
[232] de Oliveira, Luke, Paganini, Michela, and Nachman, Benjamin. Learning particle physics by example: location-aware generative adversarial networks for physics synthesis. Comput. Softw. Big Sci., 2017.Google Scholar
[233] Dearden, Richard, Friedman, Nir, and Andre, David. Model based Bayesian exploration. In Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, pages 150–159. Morgan Kaufmann Publishers Inc., 1999.Google Scholar
[234] Dechter, Rina. Reasoning with probabilistic and deterministic graphical models: exact algorithms. Synthesis Lectures on Artificial Intelligence and Machine Learning, 13(1):1–199, 2019.Google Scholar
[235] Delaney, John S.. Esol: estimating aqueous solubility directly from molecular structure. Journal of Chemical Information and Computer Sciences, 44(3):1000– 1005, 2004.Google Scholar
[236] Devslin, Jacob, Lee, Ming-Wei Kenton, and Toutanova, Kristina. Bert: Pre-training of deep bidirectional transformers for language understanding, 2018. Preprint arXiv:1810.04805.Google Scholar
[237] Lena, P. Di, Nagata, K., and Baldi, P.. Deep architectures for protein contact map prediction. Bioinformatics, 28:2449–2457, 2012. DOI: 10.1093/bioinfor-matics/bts475. First published online: July 30, 2012.Google Scholar
[238] Diaconis, Persi. Bayesian numerical analysis. Statistical Decision Theory and Related Topics IV, 1:163–175, 1988.Google Scholar
[239] Diakonikolas, Ilias, O’Donnell, Ryan, Servedio, Rocco A., and Wu, Yi. Hardness results for agnostically learning low-degree polynomial threshold functions. In Proceedings of the Twenty-Second Annual ACM–SIAM Symposium on Discrete Algorithms, pages 1590–1606. SIAM, 2011.Google Scholar
[240] Dietterich, Thomas G. Ensemble methods in machine learning. In International Workshop on Multiple Classifier Systems, pages 1–15. Springer, 2000.Google Scholar
[241] Dietterich, Thomas G.. An overview of MAXQ hierarchical reinforcement learning. In Abstraction, Reformulation, and Approximation, pages 26–44. Springer, 2000.Google Scholar
[242] Dinh, Laurent, Sohl-Dickstein, Jascha, and Bengio, Samy. Density estimation using real nvp. arXiv:1605.08803, 2016.Google Scholar
[243] Dobson, C. M.. Chemical space and biology. Nature, 432:824–828, 2004.Google Scholar
[244] Dolen, James, Harris, Philip, Marzani, Simone, Rappoccio, Salvatore, and Tran, Nhan. Thinking outside the ROCs: Designing Decorrelated Taggers (DDT) for jet substructure. JHEP, 05:156, 2016.Google Scholar
[245] Dong, Daoyi, Chen, Chunlin, Li, Hanxiong, and Tarn, Tzyh-Jong. Quantum reinforcement learning. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, 38(5):1207–1220, 2008.Google Scholar
[246] Dorigo, Marco and Gambardella, LM. Ant-Q: A reinforcement learning approach to the traveling salesman problem. In Proceedings of ML-95, Twelfth Intern. Conf. on Machine Learning, pages 252–260, 2014.Google Scholar
[247] Drake, Alvin W. Observation of a Markov process through a noisy channel. PhD thesis, Massachusetts Institute of Technology, 1962.Google Scholar
[248] Duvenaud, David, Maclaurin, Dougal, Aguilera-Iparraguirre, Jorge, Gomez-Bombarelli, Rafael, Hirzel, Timothy, Aspuru-Guzik, Alán, and Adams, Ryan P.. Convolutional networks on graphs for learning molecular fingerprints. In Neural Information Processing Systems, 2015.Google Scholar
[249] Džeroski, Sašo, De Raedt, Luc, and Driessens, Kurt. Relational reinforcement learning. Machine Learning, 43(1-2):7–52, 2001.Google Scholar
[250] Edwards, Harrison and Storkey, Amos J.. Censoring Representations with an Adversary. 2016.Google Scholar
[251] Efron, B.. Bootstrap methods: Another look at the jacknife. The Annals of Statistics, 7(1):1–26, 1979.Google Scholar
[252] Efron, Bradley and Tibshirani, Robert. Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy. Statistical science, pages 54–75, 1986.Google Scholar
[253] Eickholt, Jesse and Cheng, Jianlin. Predicting protein residue–residue contacts using deep networks and boosting. Bioinformatics, 28(23):3066–3072, 2012.Google Scholar
[254] Eickholt, Jesse and Cheng, Jianlin. DNdisorder: predicting protein disorder using boosting and deep networks. BMC Bioinformatics, 14(1):88, 2013.Google Scholar
[255] Ellias, Samuel A and Grossberg, Stephen. Pattern formation, contrast control, and oscillations in the short term memory of shunting on-center off-surround networks. Biological Cybernetics, 20(2):69–98, 1975.Google Scholar
[256] Erdmann, Martin, Geiser, Erik, Rath, Yannik, and Rieger, Marcel. Lorentz boost networks: autonomous physics-inspired feature engineering. Journal of Instrumentation, 14(06):P06006, 2019.Google Scholar
[257] Erhan, Dumitru, Bengio, Yoshua, Courville, Aaron, Manzagol, Pierre-Antoine, Vincent, Pascal, and Bengio, Samy. Why does unsupervised pre-training help deep learning? Journal of Machine Learning Research, 11:625–660, February 2010.Google Scholar
[258] Ershoff, B., Lee, C., Wray, C., Agopian, V., Urban, G., Baldi, P., and Cannesson, M.. The Training and Validation of Deep Neural Networks for the Prediction of 90-Day Post-Liver Transplant Mortality Using UNOS Registry Data. Transplantation Proceedings, 52(1):246–258, 2020.Google Scholar
[259] Esteva, Andre, Kuprel, Brett, Novoa, Roberto A, Ko, Justin, Swetter, Susan M, Blau, Helen M, and Thrun, Sebastian. Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639):115–118, 2017.Google Scholar
[260] Acero, M. A. et al. [NOvA Collaboration]. First Measurement of Neutrino Oscillation Parameters using Neutrinos and Antineutrinos by NOvA. Physical Review Letters, 123(15):151803, 2019. Also: arXiv:1906.04907.CrossRefGoogle ScholarPubMed
[261] Ewing, Brent, Hillier, LaDeana, Wendl, Michael C, and Green, Phil. Base-calling of automated sequencer traces usingphred. i. accuracy assessment. Genome Research, 8(3):175–185, 1998.Google Scholar
[262] Singh, S. Agostinelli, F., Hocquet, G. and Baldi, P.. From reinforcement learning to deep reinforcement learning: An overview. In Ilya Muchnik, editor, Key Ideas in Learning Theory from Inception to Current State: Emmanuel Braverman’s Legacy, pages 298–328. Springer, 2018.Google Scholar
[263] Fariselli, P. and Casadio, R.. Neural network based predictor of residue contacts in proteins. Protein Engineering, 12:15–21, 1999.Google Scholar
[264] Fariselli, P. and Casadio, R.. Prediction of the number of residue contacts in proteins. In Proceedings of the 2000 Conference on Intelligent Systems for Molecular Biology (ISMB00), La Jolla, CA, pages 146–151. AAAI Press, Menlo Park, CA, 2000.Google Scholar
[265] Fariselli, P., Olmea, O., Valencia, A., and Casadio, R.. Prediction of contact maps with neural networks and correlated mutations. Protein Engineering, 14:835–843, 2001.CrossRefGoogle ScholarPubMed
[266] Fariselli, Piero, Pazos, Florencio, Valencia, Alfonso, and Casadio, Rita. Prediction of protein–protein interaction sites in heterocomplexes with neural networks. The FEBS Journal, 269(5):1356–1361, 2002.Google Scholar
[267] Faust, Oliver, Hagiwara, Yuki, Hong, Tan Jen, Lih, Oh Shu, and Acharya, U Rajendra. Deep learning for healthcare applications based on physiological signals: A review. Computer Methods and Programs in Biomedicine, 161:1–13, 2018.Google Scholar
[268] Fehr, Thorsten, Weber, Jochen, Willmes, Klaus, and Herrmann, Manfred. Neural correlates in exceptional mental arithmetic–about the neural architecture of prodigious skills. Neuropsychologia, 48(5):1407–1416, 2010.Google Scholar
[269] Felleman, Daniel J and Van Essen, David C. Distributed hierarchical processing in the primate cerebral cortex. Cerebral cortex (New York, NY: 1991), 1(1):1–47, 1991.Google Scholar
[270] Feng, Jonathan L. Dark matter candidates from particle physics and methods of detection. Ann. Rev. of Astron. and Astrophys., 48:495–545, 2010.Google Scholar
[271] Feng, Jonathan L.. Dark Matter Candidates from Particle Physics and Methods of Detection. Ann. Rev. Astron. Astrophys., 48:495–545, 2010.Google Scholar
[272] Feng, Zhengzhu and Zilberstein, Shlomo. Region-based incremental pruning for POMDPs. In Proceedings of the 20th conference on Uncertainty in artificial intelligence, pages 146–153. AUAI Press, 2004.Google Scholar
[273] Fenton, M., Shmakov, A., Ho, T., Hsu, S., Whiteson, D., and Baldi, P.. Permutation-less many-jet event reconstruction with symmetry preserving attention networks. Physical Review Letters, 2020. Submitted. Also arXiv:2010.0920.Google Scholar
[274] Fischer, Andre, Sananbenesi, Farahnaz, Wang, Xinyu, Dobbin, Matthew, and Tsai, Li-Huei. Recovery of learning and memory is associated with chromatin remodelling. Nature, 447(7141):178–182, 2007.Google Scholar
[275] FitzHugh, Richard. Impulses and physiological states in theoretical models of nerve membrane. Biophysical Journal, 1(6):445, 1961.Google Scholar
[276] Fligner, M. A., Verducci, J. S., and Blower, P. E.. A Modification of the Jaccard/Tanimoto Similarity Index for Diverse Selection of Chemical Compounds Using Binary Strings. Technometrics, 44(2):110–119, 2002.Google Scholar
[277] Flower, D. R.. On the properties of bit string-based measures of chemical similarity. J. of Chemical Information and Computer Science, 38:378–386, 1998.Google Scholar
[278] Fooshee, David, Andronico, Alessio, and Baldi, Pierre. Reactionmap: An efficient atom-mapping algorithm for chemical reactions. Journal of Chemical information and Modeling, 53(11):2812–2819, 2013.Google Scholar
[279] Frankl, Peter and Maehara, Hiroshi. The Johnson–Lindenstrauss lemma and the sphericity of some graphs. Journal of Combinatorial Theory, Series B, 44(3):355– 362, 1988.Google Scholar
[280] Frasconi, Paolo, Gori, Marco, and Sperduti, Alessandro. A general framework for adaptive processing of data structures. Neural Networks, IEEE Transactions on, 9(5):768–786, 1998.Google Scholar
[281] Freund, Yoav. An adaptive version of the boost by majority algorithm. Machine Learning, 43(3):293–318, 2001.Google Scholar
[282] Frey, B.. Graphical Models for Machine Learning and Digital Communication. MIT Press, Cambridge, MA, 1998.Google Scholar
[283] Frey, B.J. and Dueck, D.. Clustering by passing messages between data points. Science, 315(5814):972, 2007.Google Scholar
[284] Froggatt, Colin D. and Nielsen, Holger Bech. Hierarchy of quark masses, Cabibbo angles and CP violation. Nuclear Physics B, 147(3-4):277–298, 1979.Google Scholar
[285] Fujimoto, Scott, Hoof, Herke van, and Meger, David. Addressing function approximation error in actor–critic methods. arXiv:1802.09477, 2018.Google Scholar
[286] Fukushima, Kunihiko. Visual feature extraction by a multilayered network of analog threshold elements. IEEE Transactions on Systems Science and Cybernetics, 5(4):322–333, 1969.Google Scholar
[287] Fukushima, Kunihiko. A feature extractor for curvilinear patterns: A design suggested by the mammalian visual system. Kybernetik, 7(4):153–160, 1970.CrossRefGoogle ScholarPubMed
[288] Fukushima, Kunihiko. Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological Cybernetics, 36(4):193–202, 1980.Google Scholar
[289] Fukushima, Kunihiko, Yamaguchi, Yoko, Yasuda, Mitsuru, and Nagata, Shigemi. An electronic model of the retina. Proceedings of the IEEE, 58(12):1950–1951, 1970.Google Scholar
[290] Fyodorov, Yan V and Williams, Ian. Replica symmetry breaking condition exposed by random matrix calculation of landscape complexity. Journal of Statistical Physics, 129(5-6):1081–1116, 2007.Google Scholar
[291] Gabrielse, G., Kalra, R., Kolthammer, W. S., McConnell, R., Richerme, P., Grzonka, D., Oelert, W., Sefzick, T., Zielinski, M., Fitzakerley, D. W., George, M. C., Hessels, E. A., Storry, C. H., Weel, M., Mullers, A., and Walz, J.. Trapped antihydrogen in its ground state. Phys. Rev. Lett., 108:113002, Mar 2012.CrossRefGoogle ScholarPubMed
[292] Ganin, Yaroslav, Ustinova, Evgeniya, Ajakan, Hana, Germain, Pascal, Larochelle, Hugo, Laviolette, François, Marchand, Mario, and Lempitsky, Victor. Domain-adversarial training of neural networks. J. Mach. Learn. Res., 17(1):2096–2030, January 2016.Google Scholar
[293] Garey, M.R. and Johnson, D.S.. Computers and Intractability. Freeman San Francisco, 1979.Google Scholar
[294] Gasteiger, Johann and Jochum, Clemens. EROS A computer program for generating sequences of reactions. Organic Compunds, pages 93–126, 1978.Google Scholar
[295] Gelfand, A., van der Maaten, L. Y. Chen, , and Welling, M.. On herding and the perceptron cycling theorem. Advances of Neural Information Processing Systems (NIPS), 23:694–702, 2010.Google Scholar
[296] Gelman, A., Carlin, J. B., Stern, H. S., and Rubin, D. B.. Bayesian Data Analysis. Chapman and Hall, London, 1995.Google Scholar
[297] George, Daniel and Huerta, EA. Deep learning for real-time gravitational wave detection and parameter estimation: Results with advanced ligo data. Physics Letters B, 778:64–70, 2018.Google Scholar
[298] George, Edward I and McCulloch, Robert E. Variable selection via Gibbs sampling. Journal of the American Statistical Association, 88(423):881–889, 1993.Google Scholar
[299] Gers, Felix A, Schmidhuber, Jürgen, and Cummins, Fred. Learning to forget: Continual prediction with LSTM. Neural Computation, 12(10):2451–2471, 2000.Google Scholar
[300] Gilks, Walter R, Richardson, Sylvia, and Spiegelhalter, David. Markov chain Monte Carlo in Practice. CRC press, 1995.Google Scholar
[301] Giomataris, Y., Rebourgeard, Ph., Robert, J. P., and Charpak, G.. Micromegas: A high-granularity position-sensitive gaseous detector for high particle-flux environments. Nucl. Instr. Meth. A, 376, 1996.Google Scholar
[302] Glen, Robert C, Bender, Andreas, Arnby, Catrin H, Carlsson, Lars, Boyer, Scott, and Smith, James. Circular fingerprints: flexible molecular descriptors with applications from physical chemistry to ADME. IDrugs, 9(3):199, 2006.Google Scholar
[303] Glorot, Xavier and Bengio, Yoshua. Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, pages 249–256, 2010.Google Scholar
[304] Glorot, Xavier and Bengio, Yoshua. Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS10). Society for Artificial Intelligence and Statistics, 2010.Google Scholar
[305] Golden, J., Garcia, E., and Tibbetts, C.. Evolutionary optimization of a neural network-based signal processor for photometric data from an automated DNA sequencer. In Evolutionary Programming IV. Proceedings of the Fourth Annual Conference on Evolutionary Programming., pages 579–601. MIT Press, 1995.Google Scholar
[306] Goller, Christoph and Kuchler, Andreas. Learning task-dependent distributed representations by backpropagation through structure. In Neural Networks, 1996., IEEE International Conference on, volume 1, pages 347–352. IEEE, 1996.Google Scholar
[307] Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. Deep Learning. MIT Press, 2016. http://www.deeplearningbook.org.Google Scholar
[308] Goodfellow, Ian, Pouget-Abadie, Jean, Mirza, Mehdi, Xu, Bing, Warde-Farley, David, Ozair, Sherjil, Courville, Aaron, and Bengio, Yoshua. Generative adversarial nets. In Advances in Neural Information Processing Systems, pages 2672–2680, 2014.Google Scholar
[309] Gorodkin, J., Lund, O., Andersen, C. A., and Brunak, S.. Using sequence motifs for enhanced neural network prediction of protein distance constraints. In Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology (ISMB99), La Jolla, CA, pages 95–105. AAAI Press, Menlo Park, CA, 1999.Google Scholar
[310] Gosavi, Abhijit. Reinforcement learning: A tutorial survey and recent advances. INFORMS Journal on Computing, 21(2):178–192, 2009.Google Scholar
[311] Gräff, Johannes, Joseph, Nadine F, Horn, Meryl E, Samiei, Alireza, Meng, Jia, Seo, Jinsoo, Rei, Damien, Bero, Adam W, Phan, Trongha X, Wagner, Florence, et al. Epigenetic priming of memory updating during reconsolidation to attenuate remote fear memories. Cell, 156(1-2):261–276, 2014.Google Scholar
[312] Gregor, Karol, Danihelka, Ivo, Mnih, Andriy, Blundell, Charles, and Wierstra, Daan. Deep autoregressive networks. arXiv:1310.8499, 2013.Google Scholar
[313] Grossberg, Stephen. Some networks that can learn, remember, and reproduce any number of complicated space-time patterns, i. Journal of Mathematics and Mechanics, 19(1):53–91, 1969.Google Scholar
[314] Grossberg, Stephen. Neural pattern discrimination. Journal of Theoretical Biology, 27(2):291–337, 1970.Google Scholar
[315] Grossberg, Stephen. On the development of feature detectors in the visual cortex with applications to learning and reaction-diffusion systems. Biological Cybernetics, 21(3):145–159, 1976.Google Scholar
[316] Grossman, Robert B and Grossman, Robert. The Art of Writing Reasonable Organic Reaction Mechanisms. Springer, 2003.Google Scholar
[317] Guan, Zhonghui, Giustetto, Maurizio, Lomvardas, Stavros, Kim, Joung-Hun, Miniaci, Maria Concetta, Schwartz, James H, Thanos, Dimitris, and Kandel, Eric R. Integration of long-term-memory-related synaptic plasticity involves bidirectional regulation of gene expression and chromatin structure. Cell, 111(4):483–493, 2002.Google Scholar
[318] Guest, Dan, Cranmer, Kyle, and Whiteson, Daniel. Deep learning and its application to LHC physics. Annual Review of Nuclear and Particle Science, 68:161–181, 2018.Google Scholar
[319] Guest, Daniel, Collado, Julian, Baldi, Pierre, Hsu, Shih-Chieh, Urban, Gregor, and Whiteson, Daniel. Jet flavor classification in high-energy physics with deep neural networks. Phys. Rev. D, 94:112002, Dec 2016.Google Scholar
[320] Guestrin, Carlos, Koller, Daphne, Parr, Ronald, and Venkataraman, Shobha. Efficient solution algorithms for factored MDPs. Journal of Artificial Intelligence Research, pages 399–468, 2003.Google Scholar
[321] Guestrin, Carlos, Lagoudakis, Michail, and Parr, Ronald. Coordinated reinforcement learning. In ICML, volume 2, pages 227–234, 2002.Google Scholar
[322] Gulshan, Varun, Peng, Lily, Coram, Marc, Stumpe, Martin C, Wu, Derek, Narayanaswamy, Arunachalam, Venugopalan, Subhashini, Widner, Kasumi, Tom Madams, Jorge Cuadros, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. Jama, 316(22):2402–2410, 2016.Google Scholar
[323] Guzowski, J.F., Lyford, G.L., Stevenson, G.D., Houston, F.P., McGaugh, J.L., Worley, P.F., and Barnes, C.A.. Inhibition of activity-dependent arc protein expression in the rat hippocampus impairs the maintenance of long-term potentiation and the consolidation of long-term memory. Journal of Neuroscience, 20(11):3993, 2000.Google Scholar
[324] Guzowski, J.F. and McGaugh, J.L.. Antisense oligodeoxynucleotide-mediated disruption of hippocampal cAMP response element binding protein levels impairs consolidation of memory for water maze training. Proceedings of the National Academy of Sciences of the United States of America, 94(6):2693, 1997.Google Scholar
[325] Ha, David and Schmidhuber, Jürgen. Recurrent world models facilitate policy evolution. In Advances in Neural Information Processing Systems, pages 2450– 2462, 2018.Google Scholar
[326] Haarnoja, Tuomas, Zhou, Aurick, Abbeel, Pieter, and Levine, Sergey. Soft actor– critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor. arXiv:1801.01290, 2018.Google Scholar
[327] Hameroff, Stuart and Penrose, Roger. Consciousness in the universe: A review of the ‘Orch OR’ theory. Physics of Life Reviews, 11(1):39–78, 2014.Google Scholar
[328] Hansen, Katja, Montavon, Grégoire, Biegler, Franziska, Fazli, Siamac, Rupp, Matthias, Scheffler, Matthias, Lilienfeld, O. Anatole von, Tkatchenko, Alexandre, and Müller, Klaus-Robert. Assessment and validation of machine learning methods for predicting molecular atomization energies. Journal of Chemical Theory and Computation, 9(8):3404–3419, 2013. PMID: 26584096.Google Scholar
[329] Hart, Peter E., Nilsson, Nils J., and Raphael, Bertram. A formal basis for the heuristic determination of minimum cost paths. IEEE Transactions on Systems Science and Cybernetics, 4(2):100–107, 1968.Google Scholar
[330] Hasselt, Hado V.. Double Q-learning. In Advances in Neural Information Processing Systems, pages 2613–2621, 2010.Google Scholar
[331] Havel, I. and Morávek, J.. B-valuations of graphs. Czechoslovak Mathematical Journal, 22(2):338–351, 1972.Google Scholar
[332] He, Kaiming, Gkioxari, Georgia, Dollár, Piotr, and Girshick, Ross. Mask r-cnn. In Proceedings of the IEEE International Conference on Computer Vision, pages 2961–2969, 2017.Google Scholar
[333] He, Kaiming, Zhang, Xiangyu, Ren, Shaoqing, and Sun, Jian. Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In The IEEE International Conference on Computer Vision (ICCV), December 2015.Google Scholar
[334] He, Kaiming, Zhang, Xiangyu, Ren, Shaoqing, and Sun, Jian. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 770–778, 2016.Google Scholar
[335] Hebb, Donald Olding. The Organization of Behavior: A Neuropsychological Theory. Wiley, 1949.Google Scholar
[336] Hebsgaard, Stefan M, Korning, Peter G., Tolstrup, Niels, Engelbrecht, Jacob, Rouzé, Pierre, and Brunak, Søren. Splice site prediction in arabidopsis thaliana pre-mRNA by combining local and global sequence information. Nucleic Acids Research, 24(17):3439–3452, 1996.Google Scholar
[337] Hermann, Jan, Schätzle, Zeno, and Noé, Frank. Deep-neural-network solution of the electronic Schrödinger equation. Nature Chemistry, 12(10):891–897, 2020.Google Scholar
[338] Herrero, Javier, Valencia, Alfonso, and Dopazo, Joaquın. A hierarchical unsupervised growing neural network for clustering gene expression patterns. Bioinformatics, 17(2):126–136, 2001.Google Scholar
[339] Hertel, Lars, Li, Lingge, Baldi, Pierre, and Bian, Jianming. Convolutional neural networks for electron neutrino and electron shower energy reconstruction in the NOVA detectors. In Deep Learning for Physical Sciences Workshop at Neural Information Processing Systems, 2017.Google Scholar
[340] Hertela, Lars, Collado, Julian, Sadowski, Peter, Ott, Jordan, and Baldi, Pierre. Sherpa: Robust hyperparameter optimization for machine learning. SoftwareX, 2020. Also arXiv:2005.04048. Software available at: https://github.com/sherpa-ai/sherpa.Google Scholar
[341] Hie, Brian, Zhong, Ellen, Berger, Bonnie, and Bryson, Bryan. Learning the language of viral evolution and escape. Science, 371(6526):284–288, 2021.Google Scholar
[342] Hines, Michael L and Carnevale, Nicholas T. The neuron simulation environment. Neural Computation, 9(6):1179–1209, 1997.Google Scholar
[343] Hinton, G.E., Osindero, S., and Teh, Y.W.. A fast learning algorithm for deep belief nets. Neural Computation, 18(7):1527–1554, 2006.Google Scholar
[344] Hinton, Geoffrey E and McClelland, James L. Learning representations by recirculation. In Neural information processing systems, pages 358–366. New York: American Institute of Physics, 1988.Google Scholar
[345] Hinton, Geoffrey E., Osindero, Simon, and Teh, Yee-Whye. A fast learning algorithm for deep belief nets. Neural Computation, 18(7):1527–1554, 2006.Google Scholar
[346] Hinton, Geoffrey E., Srivastava, Nitish, Krizhevsky, Alex, Sutskever, Ilya, and Salakhutdinov, Ruslan R.. Improving neural networks by preventing co-adaptation of feature detectors. arXiv:1207.0580, July 2012.Google Scholar
[347] Hinton, Geoffrey E, Vinyals, Oriol, and Dean, Jeff. Distilling the knowledge in a neural network. In NIPS 2014 Deep Learning Workshop, 2014.Google Scholar
[348] Hochreiter, S. and Schmidhuber, J.. Long Short-Term Memory. Neural Computation, 9(8):1735–1780, 1997. Based on TR FKI-207-95, TUM (1995).Google Scholar
[349] Hochreiter, S. and Schmidhuber, J.. Long Short-Term Memory. Neural Computation, 9(8):1735–1780, 1997.Google Scholar
[350] Hoffman, Donald. The Case Against Reality: Why Evolution Hid the Truth from Our Eyes. W.W. Norton & Company, 2019.Google Scholar
[351] Holland, John H.. Genetic algorithms and the optimal allocation of trials. SIAM Journal on Computing, 2(2):88–105, 1973.Google Scholar
[352] Hollering, R., Gasteiger, J., Steinhauer, L., Schulz, K.-P., and Herwig, A.. Simulation of organic reactions: from the degradation of chemicals to combinatorial synthesis. Journal of Chemical Information and Computer Sciences, 40(2):482–494, January 2000.Google Scholar
[353] Holley, L. Howard and Karplus, Martin. Protein secondary structure prediction with a neural network. Proceedings of the National Academy of Sciences, 86(1):152–156, 1989.Google Scholar
[354] Holton, Thérèse A., Pollastri, Gianluca, Shields, Denis C., and Mooney, Catherine. CPPpred: prediction of cell penetrating peptides. Bioinformatics, 29(23):3094– 3096, 2013.Google Scholar
[355] Hopfield, J.J.. Neural networks and physical systems with emergent collective computational abilities. Proc. of the National Academy of Sciences, 79:2554– 2558, 1982.Google Scholar
[356] Hopfield, J.J. and Tank, David W.. Neural computation of decisions in optimization problems. Biological Cybernetics, 52:141–152, 1985.Google Scholar
[357] Hopfield, John and Tank, David. Computing with neural circuits: a model. Science, 233(4764):625–633, 1986.Google Scholar
[358] Hori, M., Yamashita, K., Hayano, R.S., and Yamazaki, T.. Analog cherenkov detectors used in laser spectroscopy experiments on antiprotonic helium. Nucl. Instr. Meth. A, 496:102–122, 2003.Google Scholar
[359] Hornik, K., Stinchcombe, M., and White, H.. Multilayer feedforward networks are universal approximators. Neural Netw., 2(5):359–366, July 1989.Google Scholar
[360] Hou, Jie, Adhikari, Badri, and Cheng, Jianlin. Deepsf: deep convolutional neural network for mapping protein sequences to folds. arXiv:1706.01010, 2017.Google Scholar
[361] Houghten, R.A.. Parallel array and mixture-based synthetic combinatorial chemistry: tools for the next millennium. Annual Review of Pharmacology and Toxicology, 40:273–282, 2000.Google Scholar
[362] Hourdin, Frederic, Mauritsen, Thorsten, Gettelman, Andrew, Golaz, Jean-Christophe, Balaji, Venkatramani, Duan, Qingyun, Folini, Doris, Ji, Duoying, Klocke, Daniel, Qian, Yun, et al. The art and science of climate model tuning. Bulletin of the American Meteorological Society, 98(3):589–602, 2017.Google Scholar
[363] Ronald, A. Howard. Dynamic Programming and Markov Processes. MIT Press, 1960.Google Scholar
[364] Huang, Guang-Bin, Wang, Dian Hui, and Lan, Yuan. Extreme learning machines: a survey. International Journal of Machine Learning and Cybernetics, 2(2):107– 122, 2011.CrossRefGoogle Scholar
[365] Hubel, David H. and Wiesel, Torsten N.. Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. The Journal of Physiology, 160(1):106, 1962.Google Scholar
[366] Hutter, Marcus. Feature reinforcement learning: Part I. unstructured MDPs. Journal of Artificial General Intelligence, 1(1):3–24, 2009.Google Scholar
[367] Ioffe, Sergey and Szegedy, Christian. Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv:1502.03167, 2015.Google Scholar
[368] Irwin, J.J. and Shoichet, B.K.. ZINC – a free database of commercially available compounds for virtual screening. Journal of Chemical Information and Computer Sciences, 45:177–182, 2005.Google Scholar
[369] Itti, L. and Baldi, P.. A principled approach to detecting surprising events in video. In Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, volume 1, pages 631–637. IEEE, 2005.Google Scholar
[370] Iuculano, Teresa, Rosenberg-Lee, Miriam, Supekar, Kaustubh, Lynch, Charles J., Khouzam, Amirah, Phillips, Jennifer, Uddin, Lucina Q., and Menon, Vinod. Brain organization underlying superior mathematical abilities in children with autism. Biological Psychiatry, 75(3):223–230, 2014.Google Scholar
[371] Ivakhnenko, A.G.. The group method of data handling – a rival of the method of stochastic approximation. Soviet Automatic Control, 13(3):43–55, 1968.Google Scholar
[372] Ivakhnenko, A.G.. Polynomial theory of complex systems. Systems, Man and Cybernetics, IEEE Transactions on, (4):364–378, 1971.Google Scholar
[373] Izhikevich, Eugene M.. Simple model of spiking neurons. IEEE Transactions on Neural Networks, 14(6):1569–1572, 2003.Google Scholar
[374] James, C.A., Weininger, D., and Delany, J.. Daylight Theory Manual, 2004. Available at http://www.daylight.com/dayhtml/doc/theory/theory.toc.html.Google Scholar
[375] Janet, Jon Paul and Kulik, Heather J.. Predicting electronic structure properties of transition metal complexes with neural networks. Chem. Sci., 8:5137–5152, 2017.Google Scholar
[376] Jaynes, E.T.. Probability Theory. The Logic of Science. Cambridge University Press, 2003.Google Scholar
[377] Jensen, Anders Boeck, Moseley, Pope L., Oprea, Tudor I., Ellesøe, Sabrina Gade, Eriksson, Robert, Schmock, Henriette, Bjødstrup Jensen, Peter, Jensen, Lars Juhl, and Brunak, Søren. Temporal disease trajectories condensed from population-wide registry data covering 6.2 million patients. Nature Communications, 5, 2014.Google Scholar
[378] Jensen, L. Juhl, Gupta, Ramneek, Blom, Nikolaj, Devos, D., Tamames, J., Kesmir, Can, Nielsen, Henrik, Stærfeldt, Hans Henrik, Rapacki, Krzysztof, Workman, Christopher, et al. Prediction of human protein function from post-translational modifications and localization features. Journal of molecular biology, 319(5):1257–1265, 2002.Google Scholar
[379] Jia, Xiaowei, Willard, Jared, Karpatne, Anuj, Read, Jordan, Zwart, Jacob, Steinbach, Michael, and Kumar, Vipin. Physics guided RNNs for modeling dynamical systems: A case study in simulating lake temperature profiles. In SIAM International Conference on Data Mining, SDM 2019, pages 558–566, 2019.Google Scholar
[380] Jin, Wengong, Coley, Connor, Barzilay, Regina, and Jaakkola, Tommi. Predicting organic reaction outcomes with Weisfeiler–Lehman network. In Advances in Neural Information Processing Systems, pages 2607–2616, 2017.Google Scholar
[381] Jo, Taeho, Hou, Jie, Eickholt, Jesse, and Cheng, Jianlin. Improving protein fold recognition by deep learning networks. Scientific reports, 5:srep17573, 2015.Google Scholar
[382] Johnson, William B. and Lindenstrauss, Joram. Extensions of Lipschitz mappings into a Hilbert space. Contemporary Mathematics, 26(189-206):1, 1984.Google Scholar
[383] Jones, Catherine R.G., Happé, Francesca, Golden, Hannah, Marsden, Anita J.S., Tregay, Jenifer, Simonoff, Emily, Pickles, Andrew, Baird, Gillian, and Charman, Tony. Reading and arithmetic in adolescents with autism spectrum disorders: Peaks and dips in attainment. Neuropsychology, 23(6):718, 2009.Google Scholar
[384] Jones, David T., Buchan, Daniel W.A., Cozzetto, Domenico, and Pontil, Massimiliano. Psicov: precise structural contact prediction using sparse inverse covariance estimation on large multiple sequence alignments. Bioinformatics, 28(2):184– 190, 2011.Google Scholar
[385] Jonsdottir, S.O., Jorgensen, F.S., and Brunak, S.. Prediction methods and databases within chemoinformatics: Emphasis on drugs and drug candidates. Bioinformatics, 21:2145–2160, 2005.Google Scholar
[386] Jordan, M.I., editor. Learning in Graphical Models. MIT Press, Cambridge, MA, 1999.Google Scholar
[387] Jorgensen, W.L., Laird, E.R., Gushurst, A.J., Fleischer, J.M., Gothe, S.A., Helson, H.E., Paderes, G.D., and Sinclair, S.. CAMEO: a program from the logical prediction of the products of organic reactions. Pure and Applied Chemistry, 62:1921–1932, July 1990.Google Scholar
[388] Kaelbling, Leslie Pack, Littman, Michael L., and Moore, Andrew W.. Reinforcement learning: A survey. Journal of Artificial Intelligence Research, 4:237–285, 1996.Google Scholar
[389] Kahn, Jeff, Komlós, János, and Szemerédi, Endre. On the probability that a random ±1-matrix is singular. Journal of the American Mathematical Society, 8(1):223– 240, 1995.Google Scholar
[390] Kaiser, Lukasz, Babaeizadeh, Mohammad, Milos, Piotr, Osinski, Blazej, Campbell, Roy H., Czechowski, Konrad, Erhan, Dumitru, Finn, Chelsea, Kozakowski, Piotr, Levine, Sergey, et al. Model-based reinforcement learning for atari. arXiv:1903.00374, 2019.Google Scholar
[391] Kandel, Eric R, Schwartz, James H, Jessell, Thomas M, et al. Principles of Neural Science. McGraw-hill New York, 2000.Google Scholar
[392] Kane, Daniel et al. A structure theorem for poorly anticoncentrated polynomials of Gaussians and applications to the study of polynomial threshold functions. The Annals of Probability, 45(3):1612–1679, 2017.Google Scholar
[393] Kaplan, David E., Rehermann, Keith, Schwartz, Matthew D., and Tweedie, Brock. Top Tagging: A Method for Identifying Boosted Hadronically Decaying Top Quarks. Phys. Rev. Lett., 101:142001, 2008.Google Scholar
[394] Karpatne, Anuj, Atluri, Gowtham, Faghmous, James H., Steinbach, Michael, Banerjee, Arindam, Ganguly, Auroop, Shekhar, Shashi, Samatova, Nagiza, and Kumar, Vipin. Theory-guided data science: A new paradigm for scientific discovery from data. IEEE Transactions on Knowledge and Data Engineering, 29(10):2318–2331, oct 2017.Google Scholar
[395] Karpatne, Anuj, Watkins, William, Read, Jordan, and Kumar, Vipin. Physics-guided Neural Networks (PGNN): An Application in Lake Temperature Modeling. 2017.Google Scholar
[396] Kayala, M.A., Azencott, C.A., Chen, J.H., and Baldi, P.. Learning to predict chemical reactions. Journal of chemical information and modeling, 51(9):2209–2222, 2011.Google Scholar
[397] Kayala, M.A. and Baldi, P.. Reactionpredictor: Prediction of complex chemical reactions at the mechanistic level using machine learning. Journal of Chemical Information and Modeling, 52(10):2526–2540, 2012.Google Scholar
[398] Kayala, Matthew A., Azencott, Chloé-Agathe, Chen, Jonathan H., and Baldi, Pierre. Learning to predict chemical reactions. Journal of Chemical Information and Modeling, 51(9):2209–2222, 2011. PMID: 21819139.Google Scholar
[399] Kayala, Matthew A. and Baldi, Pierre. Reactionpredictor: Prediction of complex chemical reactions at the mechanistic level using machine learning. Journal of Chemical Information and Modeling, 52(10):2526–2540, 2012. PMID: 22978639.Google Scholar
[400] Kearns, Michael, Mansour, Yishay, and Ng, Andrew Y. A sparse sampling algorithm for near-optimal planning in large Markov decision processes. Machine Learning, 49(2-3):193–208, 2002.Google Scholar
[401] Michael, J. Kearns and Umesh Vazirani. An Introduction to Computational Learning Theory. MIT Press, 1994.Google Scholar
[402] Sathiya Keerthi, S. and Ravindran, B.. A tutorial survey of reinforcement learning. Sadhana, 19(6):851–889, 1994.Google Scholar
[403] Kelley, David R., Snoek, Jasper, and Rinn, John L.. Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks. Genome Res., 26(7):990–9, 07 2016.Google Scholar
[404] Khan, Waqasuddin, Duffy, Fergal, Pollastri, Gianluca, Shields, Denis C., and Mooney, Catherine. Predicting binding within disordered protein regions to structurally characterised peptide-binding domains. PLoS One, 8(9):e72838, 2013.Google Scholar
[405] Kim, Sunghwan, Paul A. Thiessen, Evan E. Bolton, Jie Chen, Gang Fu, Asta Gindulyte, Lianyi Han, Jane He, Siqian He, Benjamin A. Shoemaker, et al. Pubchem substance and compound databases. Nucleic Acids Research, 44(D1):D1202– D1213, 2015.Google Scholar
[406] Kingma, Diederik P. and Jimmy Ba. Adam: A method for stochastic optimization. In Proceedings of the 3rd International Conference on Learning Representations (ICLR), 2014.Google Scholar
[407] Kingma, Diederik P. and Max Welling. Auto-encoding variational bayes. arXiv:1312.6114, 2013.Google Scholar
[408] Kingma, Durk P and Prafulla Dhariwal. Glow: Generative flow with invertible 1x1 convolutions. In Advances in Neural Information Processing Systems, pages 10215–10224, 2018.Google Scholar
[409] Kirkpatrick, Scott, C Daniel Gelatt, and Mario P Vecchi. Optimization by simulated annealing. Science, 220(4598):671–680, 1983.Google Scholar
[410] Bas R., Jan Koutnik Steunebrink Jurgen Schmidhuber Klaus Greff, Rupesh Kumar Srivastava. LSTM: A search space odyssey. Arxiv, arXiv:1503.04069, 2015.Google Scholar
[411] Klivans, Adam R, O’Donnell, Ryan, and Servedio, Rocco A. Learning intersections and thresholds of halfspaces. Journal of Computer and System Sciences, 68(4):808–840, 2004.Google Scholar
[412] Klivans, Adam R and Servedio, Rocco A. Learning DNF in time 2O(n1/3) . Journal of Computer and System Sciences, 68(2):303–318, 2004.Google Scholar
[413] Klocke, Daniel, Brueck, Matthias, Hohenegger, Cathy, and Stevens, Bjorn. Rediscovery of the doldrums in storm-resolving simulations over the tropical Atlantic. Nature Geoscience, 10(12):891–896, 2017.Google Scholar
[414] Kober, Jens, J Andrew Bagnell, and Jan Peters. Reinforcement learning in robotics: A survey. The International Journal of Robotics Research, page 0278364913495721, 2013.Google Scholar
[415] Koch, Christof, Poggio, Tomaso, and Torre, Vincent. Nonlinear interactions in a dendritic tree: localization, timing, and role in information processing. Proceedings of the National Academy of Sciences, 80(9):2799–2802, 1983.Google Scholar
[416] Kocsis, Levente and Szepesvári, Csaba. Bandit based monte-carlo planning. In European conference on machine learning, pages 282–293. Springer, 2006.Google Scholar
[417] Koller, D. and Friedman, N.. Probabilistic Graphical Models: Principles and Techniques. The MIT Press, 2009.Google Scholar
[418] Kolmogorov, Andreĭ Nikolaevich. The representation of continuous functions of several variables by superpositions of continuous functions of a smaller number of variables. Doklady Akademii Nauk SSSR, 108(2):179–182, 1956.Google Scholar
[419] Komlós, János and Paturi, Ramamohan. Convergence results in an associative memory model. Neural Networks, 1(3):239–250, 1988.Google Scholar
[420] Krause, Matthias and Pudlák, Pavel. Computing Boolean functions by polynomials and threshold circuits. Computational Complexity, 7(4):346–370, 1998.Google Scholar
[421] Krizhevsky, Alex and Hinton, Geoffrey. Learning multiple layers of features from tiny images. 2009.Google Scholar
[422] Krizhevsky, Alex, Sutskever, Ilya, and Hinton, Geoffrey E.. Imagenet classification with deep convolutional neural networks. In Pereira, F., Burges, C.J.C., Bottou, L., and Weinberger, K.Q., editors, Advances in Neural Information Processing Systems 25, pages 1097–1105. Curran Associates, Inc., 2012.Google Scholar
[423] Krogh, A., Brown, M., Mian, I.S., Sjölander, K., and Haussler, D.. Hidden Markov models in computational biology: Applications to protein modeling. J. Mol. Biol., 235:1501–1531, 1994.Google Scholar
[424] Krogh, Anders, Larsson, BjoÈrn, Heijne, Gunnar Von, and Erik, L.L. Sonnhammer. Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. Journal of molecular biology, 305(3):567–580, 2001.Google Scholar
[425] Kukic, Predrag, Mirabello, Claudio, Tradigo, Giuseppe, Walsh, Ian, Veltri, Pierangelo, and Gianluca, Pollastri. Toward an accurate prediction of inter-residue distances in proteins using 2D recursive neural networks. BMC bioinformatics, 15(1):6, 2014.Google Scholar
[426] Kuncheva, Ludmila I. and Whitaker, Christopher J.. Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy. Machine Learning, 51(2):181–207, 2003.Google Scholar
[427] Kuroda, N. et al. A source of antihydrogen for in-flight hyperfine spectroscopy. Nature Communications, 5:3089, 2014.Google Scholar
[428] Kwok, T. and Yeung, D.. Constructive Algorithms for Structure Learning in Feedforward Neural Networks for Regression Problems. IEEE Transactions on Neural Networks, 8:630–645, 1997.Google Scholar
[429] Lai, Matthew. Giraffe: Using deep reinforcement learning to play chess. arXiv:1509.01549, 2015.Google Scholar
[430] Lample, Guillaume and Charton, François. Deep learning for symbolic mathematics. arXiv:1912.01412, 2019.Google Scholar
[431] Lang, S.. Algebra. Addison-Wesley, Reading, MA, 1984.Google Scholar
[432] Larkoski, Andrew J., Marzani, Simone, Soyez, Gregory, and Thaler, Jesse. Soft Drop. JHEP, 1405:146, 2014.Google Scholar
[433] Larkoski, Andrew J., Moult, Ian, and Neill, Duff. Power Counting to Better Jet Observables. JHEP, 12:009, 2014.Google Scholar
[434] Larkoski, Andrew J., Salam, Gavin P., and Thaler, Jesse. Energy Correlation Functions for Jet Substructure. JHEP, 1306:108, 2013.Google Scholar
[435] Lasko, Thomas A., Denny, Joshua C., and Levy, Mia A.. Computational phenotype discovery using unsupervised feature learning over noisy, sparse, and irregular clinical data. PloS one, 8(6):e66341, 2013.Google Scholar
[436] Leach, A.R. and Gillet, V.J.. An Introduction to Chemoinformatics. Springer, Dordrecht, The Netherlands, 2005.Google Scholar
[437] Leane, Rebecca K. and Slatyer, Tracy R.. Dark matter strikes back at the Galactic Center. arXiv:1904.08430, 2019.Google Scholar
[438] Lee, Christine K., Hofer, Ira, Gabel, Eilon, Baldi, Pierre, and Cannesson, Maxime. Development and validation of a deep neural network model for prediction of postoperative in-hospital mortality. Anesthesiology: The Journal of the American Society of Anesthesiologists, 129(4):649–662, 2018.Google Scholar
[439] Leibfried, Felix, Kushman, Nate, and Hofmann, Katja. A deep learning approach for joint video frame and reward prediction in atari games. arXiv:1611.07078, 2016.Google Scholar
[440] Lena, P. Di and Baldi, P.. Fold recognition by scoring protein map similarities using the congruence coefficient. Bioinformatics, 2021. In press. Also: bioRxiv: doi: https://protect-eu.mimecast.com/s/5xMNC83N3IQNy6Jt24dLc?domain=doi.orgGoogle Scholar
[441] LePort, Aurora KR, Mattfeld, Aaron T, Dickinson-Anson, Heather, Fallon, James H, Stark, Craig EL, Frithjof Kruggel, Larry Cahill, and James L McGaugh. Behavioral and neuroanatomical investigation of highly superior autobiographical memory (HSAM). Neurobiology of learning and memory, 98(1):78–92, 2012.Google Scholar
[442] Leung, Michael K K, Xiong, Hui Yuan, Leo J Lee, and Brendan J Frey. Deep learning of the tissue-regulated splicing code. Bioinformatics, 30(12):i121–9, Jun 2014.Google Scholar
[443] Levin, Esther, Pieraccini, Roberto, and Eckert, Wieland. A stochastic model of human-machine interaction for learning dialog strategies. IEEE Transactions on speech and audio processing, 8(1):11–23, 2000.Google Scholar
[444] Levine, Sergey, Finn, Chelsea, Darrell, Trevor, and Abbeel, Pieter. End-to-end training of deep visuomotor policies. Journal of Machine Learning Research, 17(39):1–40, 2016.Google Scholar
[445] Li, Lingge, Holbrook, Andrew, Shahbaba, Babak, and Baldi, Pierre. Neural network gradient hamiltonian monte carlo. Computational statistics, 34(1):281–299, 2019.Google Scholar
[446] Li, Lingge, Nayak, Nitish, Bian, Jianming, and Pierre Baldi. Efficient neutrino oscillation parameter inference using Gaussian processes. Physical Review D, 101(1):012001, 2020.Google Scholar
[447] Li, Yi, Quang, Daniel, and Xie, Xiaohui. Understanding sequence conservation with deep learning. bioRxiv, page 103929, 2017.Google Scholar
[448] Liao, Rong-Zhen and Thiel, Walter. Comparison of QM-only and QM/MM models for the mechanism of tungsten-dependent acetylene hydratase. Journal of Chemical Theory and Computation, 8(10):3793–3803, 2012. PMID: 26593020.Google Scholar
[449] Lillicrap, Timothy P, Cownden, Daniel, Tweed, Douglas B, and Akerman, Colin J. Random synaptic feedback weights support error backpropagation for deep learning. Nature Communications, 7, 2016.Google Scholar
[450] Lillicrap, Timothy P, Hunt, Jonathan J, Pritzel, Alexander, Heess, Nicolas, Erez, Tom, Tassa, Yuval, Silver, David, and Daan Wierstra. Continuous control with deep reinforcement learning. 2016.Google Scholar
[451] Lin, Chin-Teng and Lee, CS George. Reinforcement structure/parameter learning for neural-network-based fuzzy logic control systems. Fuzzy Systems, IEEE Transactions on, 2(1):46–63, 1994.Google Scholar
[452] Ling, Julia, Kurzawski, Andrew, and Templeton, Jeremy. Reynolds averaged turbulence modelling using deep neural networks with embedded invariance. Journal of Fluid Mechanics, 807:155–166, nov 2016.Google Scholar
[453] Lipinski, C. and Hopkins, A.. Navigating chemical space for biology and medicine. Nature, 432:855–861, 2004.Google Scholar
[454] Littman, Michael L. Markov games as a framework for multi-agent reinforcement learning. In Proceedings of the Eleventh International Conference on Machine Learning, volume 157, pages 157–163, 1994.Google Scholar
[455] Michael Lederman Littman. Algorithms for sequential decision making. PhD thesis, Brown University, 1996.Google Scholar
[456] Liu, Feng, Ren, Chao, Li, Hao, Zhou, Pingkun, Bo, Xiaochen, and Shu, Wenjie. De novo identification of replication-timing domains in the human genome by deep learning. Bioinformatics, 32(5):641–649, 2015.Google Scholar
[457] Sijia Liu, Haiming Chen, Scott Ronquist, Laura Seaman, Nicholas Ceglia, Walter Meixner, Pin-Yu Chen, Gerald Higgins, Pierre Baldi, Steve Smale, et al. Genome architecture mediates transcriptional control of human myogenic reprogramming. iScience, 6:232–246, 2018.Google Scholar
[458] Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander C Berg. Ssd: Single shot multibox detector. In European conference on computer vision, pages 21–37. Springer, 2016.Google Scholar
[459] Gilles Louppe, Kyunghyun Cho, Cyril Becot, and Kyle Cranmer. QCD-Aware Recursive Neural Networks for Jet Physics. 2017. arXiv:1702.00748.Google Scholar
[460] Louppe, Gilles, Cho, Kyunghyun, Becot, Cyril, and Cranmer, Kyle. QCD-Aware Recursive Neural Networks for Jet Physics. Journal of High Energy Physics, 01:057, 2019.Google Scholar
[461] Louppe, Gilles, Kagan, Michael, and Cranmer, Kyle. Learning to Pivot with Adversarial Networks. 2016.Google Scholar
[462] Lowe, Daniel. Chemical reactions from US patents (1976-Sep2016). https://figshare.com/articles/Chemical_reactions_from_US_patents_1976-Sep2016_/5104873, 2017.Google Scholar
[463] Lu, Yadong, Collado, Julian, Whiteson, Daniel, and Baldi, Pierre. SARM: SARM: Sparse Auto-Regressive Models for Scalable Generation of Sparse Images in Particle Physics. Physical Review D, 2021. In press. Also arXiv:2009.14017.Google Scholar
[464] Lund, O., Frimand, K., Gorodkin, J., Bohr, H., Bohr, J., Hansen, J., and Brunak, S.. Protein distance constraints predicted by neural networks and probability density functions. Prot. Eng., 10:11:1241–1248, 1997.Google Scholar
[465] Lund, O., Nielsen, M., Lundegaard, C., Kesmir, C., and Brunak, S.. Immunological Bioinformatics. MIT press, 2005.Google Scholar
[466] Luong, Minh-Thang, Hieu Pham, and Christopher D Manning. Effective approaches to attention-based neural machine translation. arXiv:1508.04025, 2015.Google Scholar
[467] Lusci, Alessandro, Fooshee, David, Michael Browning, Joshua Swamidass, and Pierre Baldi. Accurate and efficient target prediction using a potency-sensitive influence-relevance voter. Journal of cheminformatics, 7(1):63, 2015.Google Scholar
[468] Lusci, Alessandro, Pollastri, Gianluca, and Baldi, Pierre. Deep architectures and deep learning in chemoinformatics: the prediction of aqueous solubility for druglike molecules. Journal of Chemical Information and Modeling, 53(7):1563–1575, 2013.Google Scholar
[469] Lyko, Frank, Foret, Sylvain, Kucharski, Robert, Wolf, Stephan, Falckenhayn, Cassandra, and Maleszka, Ryszard. The honey bee epigenomes: differential methylation of brain DNA in queens and workers. PLoS Biol, 8(11):e1000506, 2010.Google Scholar
[470] Lyon, Richard and Mead, Carver. An analog electronic cochlea. IEEE Transactions on Acoustics, Speech, and Signal Processing, 36(7):1119–1134, 1988.Google Scholar
[471] Lyons, James, Dehzangi, Abdollah, Heffernan, Rhys, Sharma, Alok, Paliwal, Kuldip, Sattar, Abdul, Zhou, Yaoqi, and Yang, Yuedong. Predicting backbone cα angles and dihedrals from protein sequences by stacked sparse auto-encoder deep neural network. Journal of Computational Chemistry, 35(28):2040–2046, 2014.Google Scholar
[472] Marzani, S. Dasgupta, M., Fregoso, A. and Salam, G. P.. Towards an understanding of jet substructure. JHEP, 1309:029„ 2013.Google Scholar
[473] MacKay, David J.C.. The evidence framework applied to classification networks. Neural Computation, 4(5):720–736, 1992.Google Scholar
[474] MacKay, David J.C.. A practical Bayesian framework for back-propagation networks. Neural Computation, 4(3):448–472, 1992.Google Scholar
[475] Magnan, Christophe N. and Baldi, Pierre. SSpro/ACCpro 5: Almost perfect prediction of protein secondary structure and relative solvent accessibility using profiles, machine learning, and structural similarity. Bioinformatics, 30(18):2592–2597, 2014.Google Scholar
[476] Magnan, C.N., Randall, A., and Baldi, P.. SOLpro: accurate sequence-based prediction of protein solubility. Bioinformatics, 25(17):2200–2207, 2009.Google Scholar
[477] Magnan, C.N., Zeller, M., Kayala, M.A., Vigil, A., Randall, A., Felgner, P.L., and Baldi, P.. High-throughput prediction of protein antigenicity using protein microarray data. Bioinformatics, 26(23):2936–2943, 2010.Google Scholar
[478] Mahajan, M., Nimbhorkar, P., and Varadarajan, K.. The planar k-means problem is NP-hard. WALCOM: Algorithms and Computation, pages 274–285, 2009.Google Scholar
[479] Maki, Ziro, Nakagawa, Masami, and Sakata, Shoichi. Remarks on the unified model of elementary particles. Progress of Theoretical Physics, 28(5):870–880, 1962.Google Scholar
[480] Mallat, Stéphane. Group invariant scattering. Communications on Pure and Applied Mathematics, 65(10):1331–1398, 2012.Google Scholar
[481] Mallat, Stéphane. Understanding deep convolutional networks. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374(2065):20150203, 2016.Google Scholar
[482] Mandt, Stephan, Hoffman, Matthew, and Blei, David. A variational analysis of stochastic gradient algorithms. In International Conference on Machine Learning, pages 354–363, 2016.Google Scholar
[483] Ranzato, Y Marc’Aurelio, Boureau, Lan, and LeCun, Yann. Sparse feature learning for deep belief networks. Advances in Neural Information Processing Systems, 20:1185–1192, 2007.Google Scholar
[484] Marks, Debora S., Hopf, Thomas A., and Sander, Chris. Protein structure prediction from sequence variation. Nature Biotechnology, 30(11):1072–1080, 2012.Google Scholar
[485] Márquez-Neila, Pablo, Salzmann, Mathieu, and Fua, Pascal. Imposing Hard Constraints on Deep Networks: Promises and Limitations. arXiv:1706.02025, 2017.Google Scholar
[486] Marris, E.. Chemistry society goes head to head with NIH in fight over public database. Nature, 435(7043):718–719, 2005.Google Scholar
[487] Masci, Jonathan, Giusti, Alessandro, Ciresan, Dan C., Fricout, Gabriel, and Schmidhuber, Jürgen. A fast learning algorithm for image segmentation with max-pooling convolutional networks. In International Conference on Image Processing (ICIP13), pages 2713–2717, 2013.Google Scholar
[488] Mayford, Mark, Siegelbaum, Steven A., and Kandel, Eric R.. Synapses and memory storage. Cold Spring Harbor Perspectives in Biology, 4(6):a005751, 2012.Google Scholar
[489] McAleer, Stephen, Agostinelli, Forest, Shmakov, Alexander, and Baldi, Pierre. Solving the Rubik’s cube with approximate policy iteration. International Conference on Learning Representations (ICLR), 2019.Google Scholar
[490] McClelland, James L., Rumelhart, David E., and the PDP Research Group. Parallel Distributed Processing, volumes 1 and 2. MIT Press, Cambridge, MA, 1987.Google Scholar
[491] McEliece, Robert J.. The Theory of Information and Coding. Cambridge University Press, 2002.Google Scholar
[492] McEliece, Robert J., Edward Posner, Eugene Rodemich, and Santosh S. Venkatesh. The capacity of the Hopfield associative memory. IEEE Transactions on Information Theory, 33(4):461–482, 1987.Google Scholar
[493] Amy, McGovern and Andrew G. Barto. Automatic discovery of subgoals in reinforcement learning using diverse density. In ICML’01: Proceedings of the Eighteenth International Conference on Machine Learning, page 8, 2001.Google Scholar
[494] McNaughton, Bruce L. Cortical hierarchies, sleep, and the extraction of knowledge from memory. Artificial Intelligence, 174(2):205–214, 2010.Google Scholar
[495] Mead, Carver and Mohammed Ismail (editors). Analog VLSI Implementation of Neural Systems. Springer Science & Business Media, 2012.Google Scholar
[496] Mead, Carver and Mahowald, Misha. A silicon model of early visual processing. Neural Networks, 1(1):91–97, 1988.Google Scholar
[497] Megiddo, N. and Supowit, K.J.. On the complexity of some common geometric location problems. SIAM J. Comput., 13(1):182–196, 1984.Google Scholar
[498] Mel, Bartlett W.. Information processing in dendritic trees. Neural Computation, 6(6):1031–1085, 1994.Google Scholar
[499] Meyer, C.D.. Matrix Analysis and Applied Linear Algebra. SIAM, 2000.Google Scholar
[500] Michie, Donald. Trial and error. Science Survey, Part, 2:129–145, 1961.Google Scholar
[501] Michie, Donald. Experiments on the mechanization of game-learning. Part I. Characterization of the model and its parameters. The Computer Journal, 6(3):232– 236, 1963.Google Scholar
[502] Michie, Donald and Chambers, Roger A.. Boxes: An experiment in adaptive control. Machine intelligence, 2(2):137–152, 1968.Google Scholar
[503] Minsky, M. and Papert, S.. Perceptrons. MIT Press, Cambridge, MA, 1969.Google Scholar
[504] Minsky, Marvin. Steps toward artificial intelligence. Proceedings of the IRE, 49(1):8–30, 1961.Google Scholar
[505] Miotto, Riccardo, Li Li, Brian A Kidd, and Joel T Dudley. Deep patient: An unsupervised representation to predict the future of patients from the electronic health records. Scientific reports, 6:26094, 2016.Google Scholar
[506] Mirabello, Claudio and Pollastri, Gianluca. Porter, paleale 4.0: high-accuracy prediction of protein secondary structure and relative solvent accessibility. Bioinformatics, 29(16):2056–2058, 2013.Google Scholar
[507] Mitchell, Toby J and Beauchamp, John J. Bayesian variable selection in linear regression. Journal of the American Statistical Association, 83(404):1023–1032, 1988.Google Scholar
[508] Miyamoto, Yoshiaki, Kajikawa, Yoshiyuki, Yoshida, Ryuji, Yamaura, Tsuyoshi, Yashiro, Hisashi, and Tomita, Hirofumi. Deep moist atmospheric convection in a subkilometer global simulation. Geophysical Research Letters, 40(18):4922– 4926, 2013.Google Scholar
[509] Mjolsness, Eric, Sharp, David H, and Reinitz, John. A connectionist model of development. Journal of theoretical Biology, 152(4):429–453, 1991.Google Scholar
[510] Mnih, Volodymyr, Badia, Adria Puigdomenech, Mirza, Mehdi, Graves, Alex, Lillicrap, Timothy P, Harley, Tim, Silver, David, and Koray Kavukcuoglu. Asynchronous methods for deep reinforcement learning. In International Conference on Machine Learning (ICML), 2016.Google Scholar
[511] Mnih, Volodymyr, Kavukcuoglu, Koray, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al. Human-level control through deep reinforcement learning. Nature, 518(7540):529–533, 2015.Google Scholar
[512] Moody, John and Saffell, Matthew. Reinforcement learning for trading. Advances in Neural Information Processing Systems, pages 917–923, 1999.Google Scholar
[513] Mooney, Catherine, Haslam, Niall J, Holton, Thérèse A, Pollastri, Gianluca, and Shields, Denis C. Peptidelocator: prediction of bioactive peptides in protein sequences. Bioinformatics, 29(9):1120–1126, 2013.Google Scholar
[514] Mooney, Catherine, Haslam, Niall J, Pollastri, Gianluca, and Shields, Denis C. Towards the improved discovery and design of functional peptides: common features of diverse classes permit generalized prediction of bioactivity. PloS one, 7(10):e45012, 2012.Google Scholar
[515] Mooney, Catherine and Pollastri, Gianluca. Beyond the twilight zone: Automated prediction of structural properties of proteins by recursive neural networks and remote homology information. Proteins: Structure, Function, and Bioinformatics, 77(1):181–190, 2009.Google Scholar
[516] Mooney, Catherine, Pollastri, Gianluca, Shields, Denis C, and Haslam, Niall J. Prediction of short linear protein binding regions. Journal of molecular biology, 415(1):193–204, 2012.Google Scholar
[517] Mooney, Catherine, Wang, Yong-Hong, and Pollastri, Gianluca. SCLpred: protein subcellular localization prediction by N-to-1 neural networks. Bioinformatics, 27(20):2812–2819, 2011.Google Scholar
[518] Moriarty, David E., Schultz, Alan C., and Grefenstette, John J.. Evolutionary algorithms for reinforcement learning. J. Artif. Intell. Res.(JAIR), 11:241–276, 1999.Google Scholar
[519] Movellan, Javier R.. Contrastive Hebbian learning in the continuous Hopfield model. In Connectionist Models, pages 10–17. Elsevier, 1991.Google Scholar
[520] Muggleton, Stephen and De Raedt, Luc. Inductive logic programming: Theory and methods. The Journal of Logic Programming, 19:629–679, 1994.Google Scholar
[521] Muroga, Saburo. Lower bounds of the number of threshold functions and a maximum weight. IEEE Transactions on Electronic Computers, (2):136–148, 1965.Google Scholar
[522] Murphy, Kevin P.. Machine Learning: a Probabilistic Perspective. MIT press, 2012.Google Scholar
[523] Mütter, Andreas, Parr, Erik, and Vaudrevange, Patrick K. S.. Deep learning in the heterotic orbifold landscape. Nuclear Physics B, 940:113–129, 2019.Google Scholar
[524] Tishby, N.N., Pereira, F., and Bialek, W.. The information bottleneck method. In Proceedings of the 37th Annual Allerton Conference on Communcation, Control, and Computing, pages 368–377. University of Illinois, 1999. Also: arXiv preprint physics/0004057.Google Scholar
[525] Nagata, K., Randall, A., and Baldi, P.. SIDEpro: A Novel Machine Learning Approach for the Accurate Prediction of Protein Side Chains. 2011. Server available. Manuscript under preparation.Google Scholar
[526] Nagata, Ken, Randall, Arlo, and Baldi, Pierre. Incorporating post-translational modifications and unnatural amino acids into high-throughput modeling of protein structures. Bioinformatics, 30(12):1681–1689, 2014.Google Scholar
[527] Nagumo, Jinichi, Arimoto, Suguru, and Yoshizawa, Shuji. An active pulse transmission line simulating nerve axon. Proceedings of the IRE, 50(10):2061–2070, 1962.Google Scholar
[528] Nair, Arun, Srinivasan, Praveen, Blackwell, Sam, Cagdas Alcicek, Rory Fearon, Alessandro De Maria, Vedavyas Panneershelvam, Mustafa Suleyman, Charles Beattie, Stig Petersen, et al. Massively parallel methods for deep reinforcement learning. arXiv:1507.04296, 2015.Google Scholar
[529] Nair, Vinod and Hinton, Geoffrey E.. Rectified Linear Units Improve Restricted Boltzmann Machines. In Johannes Furnkranz and Thorsten Joachims, editors, Proceedings of the 27th International Conference on Machine Learning (ICML-10), pages 807–814. Omnipress, 2010.Google Scholar
[530] Nasr, R., Hirschberg, D.S., and Baldi, P.. Hashing Algorithms and Data Structures for Rapid Searches of Fingerprint Vectors. Journal of Chemical Information and Modeling, 50(8):1358–1368, 2011.Google Scholar
[531] Nasr, Ramzi, Vernica, Rares, Li, Chen, and Baldi, Pierre. Speeding up chemical searches using the inverted index: The convergence of chemoinformatics and text search methods. Journal of chemical information and modeling, 52(4):891–900, 2012.Google Scholar
[532] Neal, Radford M. Bayesian Learning for Neural Networks. Springer Verlag 1996. Reissued in 2012.Google Scholar
[533] Newman, Charles M. Memory capacity in neural network models: Rigorous lower bounds. Neural Networks, 1(3):223–238, 1988.Google Scholar
[534] Newman, James and Baars, Bernard J. A neural attentional model for access to consciousness: A global workspace perspective. Concepts in Neuroscience, 4(2):255–290, 1993.Google Scholar
[535] Ng, Andrew Y, Coates, Adam, Diel, Mark, Varun Ganapathi, Jamie Schulte, Ben Tse, Eric Berger, and Eric Liang. Autonomous inverted helicopter flight via reinforcement learning. In Experimental Robotics IX, pages 363–372. Springer, 2006.Google Scholar
[536] Ng, Andrew Y, Russell, Stuart J, et al. Algorithms for inverse reinforcement learning. In Icml, pages 663–670, 2000.Google Scholar
[537] Nielsen, Henrik, Engelbrecht, Jacob, Brunak, Søren, and von Heijne, Gunnar. Identification of prokaryotic and eukaryotic signal peptides and prediction of their cleavage sites. Protein engineering, 10(1):1–6, 1997.Google Scholar
[538] Nielsen, Morten, Lundegaard, Claus, Worning, Peder, Lauemøller, Sanne Lise, Lamberth, Kasper, Buus, Søren, Brunak, Søren, and Lund, Ole. Reliable prediction of t-cell epitopes using neural networks with novel sequence representations. Protein Science, 12(5):1007–1017, 2003.Google Scholar
[539] Nilges, M., Clore, G. M., and Gronenborn, A. M.. Determination of three-dimensional structures of proteins from interproton distance data by dynamical simulated annealing from a random array of atoms. FEBS Lett., 239:129–136, 1988.Google Scholar
[540] Nilges, M., Clore, G. M., and Gronenborn, A. M.. Determination of three-dimensional structures of proteins from interproton distance data by hybrid distance geometry-dynamical simulated annealing calculations. FEBS Lett., 229:317–324, 1988.Google Scholar
[541] Nix, D. A. and Weigend, A. S.. Estimating the mean and variance of the target probability distribution. In Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on, volume 1, pages 55–60 vol.1, June 1994.Google Scholar
[542] Novikoff, A. B.. On convergence proofs for perceptrons. In Proceedings of the Symposium on the Mathematical Theory of Automata, volume 12, pages 615– 622. Polytechnic Institute of Brooklyn, 1962.Google Scholar
[543] Obermeyer, Ziad and Emanuel, Ezekiel J. Predicting the future–big data, machine learning, and clinical medicine. The New England journal of medicine, 375(13):1216, 2016.Google Scholar
[544] Odlyzko, Andrew M. On subspaces spanned by random selections of±1 vectors. journal of combinatorial theory, Series A, 47(1):124–133, 1988.Google Scholar
[545] O’Donnell, Ryan. Analysis of Boolean Functions. Cambridge University Press, 2014.Google Scholar
[546] O’Donnell, Ryan and Servedio, Rocco A. Extremal properties of polynomial threshold functions. Journal of Computer and System Sciences, 74(3):298–312, 2008.Google Scholar
[547] O’Donnell, Ryan and Servedio, Rocco A. New degree bounds for polynomial threshold functions. Combinatorica, 30(3):327–358, 2010.Google Scholar
[548] Oh, Junhyuk, Guo, Xiaoxiao, Lee, Honglak, Lewis, Richard L, and Singh, Satinder. Action-conditional video prediction using deep networks in atari games. In Advances in Neural Information Processing Systems, pages 2863–2871, 2015.Google Scholar
[549] Junhyuk, Oh, Singh, Satinder, and Lee, Honglak. Value prediction network. In Advances in Neural Information Processing Systems, pages 6120–6130, 2017.Google Scholar
[550] Oja, E.. Simplified neuron model as a principal component analyzer. Journal of mathematical biology, 15(3):267–273, 1982.Google Scholar
[551] Oksendal, Bernt. Stochastic differential equations: an introduction with applications. Springer Science & Business Media, 2013.Google Scholar
[552] Olmea, O. and Valencia, A.. Improving contact predictions by the combination of correlated mutations and other sources of sequence information. Fold. Des., 2:S25–32, 1997.Google Scholar
[553] Olshausen, Bruno A and Field, David J. Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature, 381(6583):607, 1996.Google Scholar
[554] Van Oord, Aaron Nal Kalchbrenner, , and Kavukcuoglu, Koray. Pixel recurrent neural networks. In Maria Florina Balcan and Kilian Q. Weinberger, editors, Proceedings of The 33rd International Conference on Machine Learning, volume 48 of Proceedings of Machine Learning Research, pages 1747–1756, New York, New York, USA, 20–22 Jun 2016. PMLR.Google Scholar
[555] Ormoneit, Dirk and Sen, Śaunak. Kernel-based reinforcement learning. Machine Learning, 49(2-3):161–178, 2002.Google Scholar
[556] Ostrand, Phillip A. Dimension of metric spaces and Hilbert’s problem 13. Bulletin of the American Mathematical Society, 71(4):619–622, 1965.Google Scholar
[557] Ott, J., Linstead, E., LaHaye, N., and Baldi, P.. Learning in the machine: To share or not to share? Neural Networks, 126:235–249, 2020.Google Scholar
[558] Ott, Jordan, Pritchard, Mike, Best, Natalie, Linstead, Erik, Curcic, Milan, and Baldi, Pierre. A Fortran-Keras Deep Learning Bridge for Scientific Computing. Scientific Programming, 2020. In press. Also: arXiv:2005.04048.Google Scholar
[559] Ovyn, S., Rouby, X., and Lemaitre, V.. DELPHES, a framework for fast simulation of a generic collider experiment. arXiv:0903.2225, 2009.Google Scholar
[560] Christos, H. Papadimitriou. Computational Complexity. Wiley, 2003.Google Scholar
[561] Papadimitriou, Christos H. and Tsitsiklis, John N.. The complexity of Markov decision processes. Mathematics of Operations Research, 12(3):441–450, 1987.Google Scholar
[562] Papamakarios, George, Pavlakou, Theo, and Murray, Iain. Masked autoregressive flow for density estimation. In Advances in Neural Information Processing Systems, pages 2338–2347, 2017.Google Scholar
[563] Parr, Ronald and Russell, Stuart. Reinforcement learning with hierarchies of machines. Advances in Neural Information Processing Systems, pages 1043– 1049, 1998.Google Scholar
[564] Pasa, Luca and Sperduti, Alessandro. Pre-training of recurrent neural networks via linear autoencoders. In Advances in Neural Information Processing Systems, pages 3572–3580, 2014.Google Scholar
[565] Pascanu, Razvan, Li, Yujia, Vinyals, Oriol, Heess, Nicolas, Buesing, Lars, Racanière, Sebastien, Reichert, David, Weber, Théophane, Wierstra, Daan, and Peter Battaglia, . Learning model-based planning from scratch. arXiv:1707.06170, 2017.Google Scholar
[566] Pashenkova, Elena, Rish, Irina, and Dechter, Rina. Value iteration and policy iteration algorithms for Markov decision problem. In AAAI 96: Workshop on Structural Issues in Planning and Temporal Reasoning. Citeseer, 1996.Google Scholar
[567] Pearl, J.. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Mateo, CA., 1988.Google Scholar
[568] Peixoto, Lucia and Abel, Ted. The role of histone acetylation in memory formation and cognitive impairments. Neuropsychopharmacology, 38(1):62–76, 2013.Google Scholar
[569] Perez, Patrice, Banerjee, D, Biraben, François, Brook-Roberge, D., Charlton, M., Cladé, Pierre, Pauline Comini, Paolo Crivelli, Oleg Dalkarov, Pascal Debu, et al. The GBAR antimatter gravity experiment. Hyperfine Interactions, 233(1):21–27, 2015.Google Scholar
[570] Petersen, Thomas Nordahl, Lundegaard, Claus, Morten Nielsen, Henrik Bohr, Jakob Bohr, Søren Brunak, Garry P. Gippert, and Ole Lund. Prediction of protein secondary structure at 80% accuracy. Proteins: Structure, Function, and Bioinformatics, 41(1):17–20, 2000.Google Scholar
[571] Pham, Trang, Tran, Truyen, Phung, Dinh, and Venkatesh, Svetha. Deepcare: A deep dynamic memory model for predictive medicine. In Pacific-Asia Conference on Knowledge Discovery and Data Mining, pages 30–41. Springer, 2016.Google Scholar
[572] Piergiovanni, A.J., Wu, Alan, and Ryoo, Michael S.. Learning real-world robot policies by dreaming. arXiv:1805.07813, 2018.Google Scholar
[573] Planck Collaboration. Planck 2013 results. XVI. Cosmological parameters., 2013.Google Scholar
[574] Plehn, Tilman, Spannowsky, Michael, Takeuchi, Michihisa, and Zerwas, Dirk. Stop Reconstruction with Tagged Tops. JHEP, 1010:078, 2010.Google Scholar
[575] Poggio, Tomaso and Anselmi, Fabio. Visual cortex and deep networks: learning invariant representations. MIT Press, 2016.Google Scholar
[576] Pollastri, G. and Baldi, P.. Predition of contact maps by GIOHMMs and recurrent neural networks using lateral propagation from all four cardinal corners. Bioinformatics, 18 Supplement 1:S62–S70, 2002. Proceedings of the ISMB 2002 Conference.Google Scholar
[577] Pollastri, G., Baldi, P., Fariselli, P., and Casadio, R.. Improved prediction of the number of residue contacts in proteins by recurrent neural networks. Bioinformatics, 17:S234–S242, 2001. Proceedings of the ISMB 2001 Conference.Google Scholar
[578] Pollastri, G., Baldi, P., Fariselli, P., and Casadio, R.. Prediction of coordination number and relative solvent accessibility in proteins. Proteins, 47:142–153, 2001.Google Scholar
[579] Pollastri, G., Przybylski, D., Rost, B., and Baldi, P.. Improving the prediction of protein secondary strucure in three and eight classes using recurrent neural networks and profiles. Proteins, 47:228–235, 2001.Google Scholar
[580] Pollastri, G., Vullo, A., Frasconi, P., and Baldi, P.. Modular DAG-RNN architectures for assembling coarse protein structures. Journal of Computational Biology, 13(3):631–650, 2006.Google Scholar
[581] Pollastri, Gianluca, Baldi, Pierre, Vullo, Alessandro, and Frasconi, Paolo. Prediction of protein topologies using generalized IOHMMs and RNNs. In Thrun, S. Becker, S. and Obermayer, K., editors, Advances in Neural Information Processing Systems 15, pages 1449–1456. MIT Press, Cambridge, MA, 2003.Google Scholar
[582] Polsky, Alon, Mel, Bartlett W, and Schiller, Jackie. Computational subunits in thin dendrites of pyramidal cells. Nature neuroscience, 7(6):621–627, 2004.Google Scholar
[583] Polyak, Iakov, Reetz, Manfred T., and Thiel, Walter. Quantum mechanical/molecular mechanical study on the mechanism of the enzymatic Baeyer– Villiger reaction. Journal of the American Chemical Society, 134(5):2732–2741, 2012. PMID: 22239272.Google Scholar
[584] Pontecorvo, Bruno. Mesonium and antimesonium. JETP, 6:429, 1958.Google Scholar
[585] Pontecorvo, Bruno. Neutrino experiments and the problem of conservation of leptonic charge. Sov. Phys. JETP, 26(984-988):165, 1968.Google Scholar
[586] Poupart, Pascal and Boutilier, Craig. VDCBPI: an approximate scalable algorithm for large POMDPs. In Advances in Neural Information Processing Systems, pages 1081–1088, 2004.Google Scholar
[587] Powers, Rob and Shoham, Yoav. New criteria and a new algorithm for learning in multi-agent systems. In Advances in Neural Information Processing Systems, pages 1089–1096, 2004.Google Scholar
[588] Qian, N. and Sejnowski, T. J.. Predicting the secondary structure of globular proteins using neural network models. J. Mol. Biol., 202:865–884, 1988.Google Scholar
[589] Qin, Qian and Feng, Jianxing. Imputation for transcription factor binding predictions based on deep learning. PLoS Comput Biol, 13(2):e1005403, Feb 2017.Google Scholar
[590] Quang, Daniel, Chen, Yifei, and Xie, Xiaohui. Dann: a deep learning approach for annotating the pathogenicity of genetic variants. Bioinformatics, 31(5):761–3, Mar 2015.Google Scholar
[591] Quang, Daniel and Xie, Xiaohui. Danq: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences. Nucleic Acids Research, 44(11):e107–e107, 2016.Google Scholar
[592] Quang, Daniel and Xie, Xiaohui. Factornet: a deep learning framework for predicting cell type specific transcription factor binding from nucleotide-resolution sequential data. bioRxiv, page 151274, 2017.Google Scholar
[593] Racah, E., Ko, S., Sadowski, P., Bhimji, W., Tull, C., Oh, S.Y., Baldi, P., and Prabhat. Revealing fundamental physics from the Daya Bay Neutrino Experiment using deep neural networks. In 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA), pages 892–897, Dec 2016.Google Scholar
[594] Racanière, Sébastien, Weber, Théophane, Reichert, David, Buesing, Lars, Guez, Arthur, Danilo Jimenez Rezende, Adria Puigdomènech Badia, Oriol Vinyals, Nicolas Heess, Yujia Li, et al. Imagination-augmented agents for deep reinforcement learning. In Advances in Neural Information Processing Systems, pages 5690– 5701, 2017.Google Scholar
[595] Radics, B. et al. The ASACUSA micromegas tracker: A cylindrical, bulk micromegas detector for antimatter research. Review of Scientific Instruments, 86, 2015.Google Scholar
[596] Radics, B., Murtagh, D.J., Yamazaki, Y., and Robicheaux, F.. Scaling behavior of the ground-state antihydrogen yield as a function of positron density and temperature from classical-trajectory Monte Carlo simulations. Physical Review A, 90(3):032704, September 2014.Google Scholar
[597] Raissi, Maziar, Perdikaris, Paris, and Karniadakis, George Em. Physics informed deep learning (Part I): data-driven solutions of nonlinear partial differential equations. arXiv:1711.10561, 2017.Google Scholar
[598] Raissi, Maziar, Yazdani, Alireza, and Karniadakis, George Em. Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations. Science, 367(6481):1026–1030, feb 2020.Google Scholar
[599] Rajpurkar, Pranav, Hannun, Awni Y., Haghpanahi, Masoumeh, Bourn, Codie, and Ng, Andrew Y.. Cardiologist-level arrhythmia detection with convolutional neural networks. arXiv:1707.01836, 2017.Google Scholar
[600] Ramakrishnan, Raghunathan, Dral, Pavlo O, Rupp, Matthias, and Lilienfeld, O Anatole Von. Quantum chemistry structures and properties of 134 kilo molecules. Scientific data, 1:140022, 2014.Google Scholar
[601] Ramakrishnan, Raghunathan, Hartmann, Mia, Tapavicza, Enrico, and Lilienfeld, O Anatole Von. Electronic spectra from TDDFT and machine learning in chemical space. The Journal of Chemical Physics, 143(8):084111, 2015.Google Scholar
[602] Randall, Arlo, Cheng, Jianlin, Sweredoski, Michael, and Baldi, Pierre. TMBpro: secondary structure, β-contact and tertiary structure prediction of transmembrane β-barrel proteins. Bioinformatics, 24(4):513–520, 2008.Google Scholar
[603] Randløv, Jette and Alstrøm, Preben. Learning to drive a bicycle using reinforcement learning and shaping. In ICML, volume 98, pages 463–471. Citeseer, 1998.Google Scholar
[604] Rasmussen, Carl Edward. Gaussian processes for machine learning. MIT Press, 2006.Google Scholar
[605] Rasp, Stephan, Pritchard, Michael S., and Gentine, Pierre. Deep learning to represent subgrid processes in climate models. Proceedings of the National Academy of Sciences, 115(39):9684–9689, 2018.Google Scholar
[606] Redmon, Joseph, Divvala, Santosh, Girshick, Ross, and Farhadi, Ali. You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 779–788, 2016.Google Scholar
[607] Redmon, Joseph and Farhadi, Ali. Yolov3: An incremental improvement. arXiv:1804.02767, 2018.Google Scholar
[608] Reed, R.. Pruning algorithms – a survey. Neural Networks, IEEE Transactions on, 4(5):740–747, 1993.Google Scholar
[609] Reichstein, Markus, Camps-Valls, Gustau, Stevens, Bjorn, Jung, Martin, Denzler, Joachim, Carvalhais, Nuno, and Prabhat. Deep learning and process understanding for data-driven Earth system science. Nature, 566(7743):195–204, feb 2019.Google Scholar
[610] Ren, Shaoqing, He, Kaiming, Girshick, Ross, and Sun, Jian. Faster R-CNN: Towards Real-Time Object Detection With Region Proposal Networks. In Advances in Neural Information Processing Systems, pages 91–99, 2015.Google Scholar
[611] Ribeiro, Marco Tulio, Singh, Sameer, and Guestrin, Carlos. “Why should I trust you?”: Explaining the predictions of any classifier. In Knowledge Discovery and Data Mining (KDD), 2016.Google Scholar
[612] Rifai, S., Mesnil, G., Vincent, P., Muller, X., Bengio, Y., Dauphin, Y., and Glorot, X.. Higher order contractive auto-encoder. Machine Learning and Knowledge Discovery in Databases, pages 645–660, 2011.Google Scholar
[613] Riis, S.K. and Krogh, A.. Improving prediction of protein secondary structure using structured neural networks and multiple sequence alignments. J. Comput. Biol., 3:163–183, 1996.Google Scholar
[614] Robbins, Herbert and Monro, Sutton. A stochastic approximation method. The Annals of Mathematical Statistics, pages 400–407, 1951.Google Scholar
[615] Tyrell Rockafellar, R.. Convex Analysis. Princeton University Press, 1997.Google Scholar
[616] Rogers, David and Hahn, Mathew. Extended-connectivity fingerprints. Journal of Chemical Information and Modeling, 50(5):742–754, 2010.Google Scholar
[617] Ronneberger, Olaf, Fischer, Philipp, and Brox, Thomas. U-Net: Convolutional Networks for Biomedical Image Segmentation. In International Conference on Medical Image Computing and Computer-assisted Intervention, pages 234–241. Springer, 2015.Google Scholar
[618] Rosenblatt, F.. The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 65(6):386, 1958.Google Scholar
[619] Ross, Sheldon M.. Introduction to Stochastic Dynamic Programming. Academic press, 2014.Google Scholar
[620] Rost, B. and Sander, C.. Combining evolutionary information and neural networks to predict protein secondary structure. Proteins, 19:55–72, 1994.Google Scholar
[621] Rost, B. and Sander, C.. Prediction of protein secondary structure at better than 70% accuracy. J. Mol. Biol., 232:584–599, 1997.Google Scholar
[622] Rozsa, Andras and Boult, Terrance E.. Improved adversarial robustness by reducing open space risk via tent activations, 2019. Preprint arXiv:1908.02435Google Scholar
[623] Roth, Holger R, Yao, Jianhua, Lu, Le, Stieger, James, Joseph E Burns, and Ronald M Summers. Detection of sclerotic spine metastases via random aggregation of deep convolutional neural network classifications. In Recent Advances in Computational Methods and Clinical Applications for Spine Imaging, pages 3–12. Springer, 2015.Google Scholar
[624] Rubin, Donald B. The Bayesian Bootstrap. The annals of statistics, pages 130–134, 1981.Google Scholar
[625] Ruddigkeit, Lars, Deursen, Ruud Van, Blum, Lorenz C, and Reymond, Jean-Louis. Enumeration of 166 Billion Organic Small Molecules in the Chemical Universe database gdb-17. Journal of chemical information and modeling, 52(11):2864– 2875, 2012.Google Scholar
[626] Ruiz, Francisco R, Michalis Titsias RC AUEB, and David Blei. The generalized reparameterization gradient. In Advances in Neural Information Processing Systems, pages 460–468, 2016.Google Scholar
[627] Rumelhart, D.E., Hintont, G.E., and Williams, R.J.. Learning representations by back-propagating errors. Nature, 323(6088):533–536, 1986.Google Scholar
[628] Rummery, Gavin A and Niranjan, Mahesan. On-line Q-learning using connectionist systems. University of Cambridge, Department of Engineering, 1994.Google Scholar
[629] Rupp, Matthias. Machine learning for quantum mechanics in a nutshell. International Journal of Quantum Chemistry, 115(16):1058–1073, 2015.Google Scholar
[630] Rupp, Matthias, Tkatchenko, Alexandre, Müller, Klaus-Robert, and Lilienfeld, O. Anatole von. Fast and accurate modeling of molecular atomization energies with machine learning. Phys. Rev. Lett., 108:058301, Jan 2012.Google Scholar
[631] Rupp, Matthias, O Anatole Von Lilienfeld, and Kieron Burke. Guest editorial: Special topic on data-enabled theoretical chemistry. The Journal of Chemical Physics, 148(24), 2018.Google Scholar
[632] Rusu, Andrei A, Colmenarejo, Sergio Gomez, Gulcehre, Caglar, Desjardins, Guillaume, Kirkpatrick, James, Pascanu, Razvan, Mnih, Volodymyr, Kavukcuoglu, Koray, and Hadsell, Raia. Policy distillation. In International Conference on Learning Representations (ICLR), 2016.Google Scholar
[633] Vermilion, C.K. Ellis, S.D. and Walsh, J.R.. Recombination algorithms and jet substructure: pruning as a tool for heavy particle searches. Phys.Rev., D81:094023, 2010.Google Scholar
[634] Sadowski, P. and Baldi, P.. Deep learning in the natural sciences: Applications to physics. In Ilya Muchnik, editor, Key Ideas in Learning Theory from Inception to Current State: Emmanuel Braverman’s Legacy, pages 269–297. Springer, 2018.Google Scholar
[635] Sadowski, P., Collado, J., Whiteson, D., and Baldi, P.. Deep learning, dark knowledge, and dark matter. Journal of Machine Learning Research, Workshop and Conference Proceedings, 42:81–97, 2015.Google Scholar
[636] Sadowski, P., Radics, B., Ananya, Y. Yamazaki, , and Baldi, P.. Efficient antihydrogen detection in antimatter physics by deep learning. Journal of Physics Communications, 1(2):025001, 2017.Google Scholar
[637] Sadowski, Peter and Baldi, Pierre. Small-Molecule 3D Structure Prediction Using Open Crystallography Data. Journal of Chemical Information and Modeling, 53(12):3127–3130, 2013.Google Scholar
[638] Sadowski, Peter, Fooshee, David, Subrahmanya, Niranjan, and Baldi, Pierre. Synergies between quantum mechanics and machine learning in reaction prediction. Journal of Chemical Information and Modeling, 56(11):2125–2128, 2016. PMID: 27749058.Google Scholar
[639] Saks, Michael. Slicing the hypercube. Surveys in combinatorics, 1993:211–255, 1993.Google Scholar
[640] Salimans, Tim, Karpathy, Andrej, Chen, Xi, and Kingma, Diederik P.. PixelCNN++: Improving the pixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications. arXiv:1701.05517, 2017.Google Scholar
[641] Salimans, Tim, Karpathy, Andrej, Chen, Xi, and Kingma, Diederik P.. PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications. CoRR, 2017.Google Scholar
[642] Samuel, Arthur L. Some studies in machine learning using the game of checkers. ii. recent progress. IBM Journal of research and development, 11(6):601–617, 1967.Google Scholar
[643] Santamaría, Juan C, Sutton, Richard S, and Ram, Ashwin. Experiments with reinforcement learning in problems with continuous state and action spaces. Adaptive behavior, 6(2):163–217, 1997.Google Scholar
[644] Santangelo, Valerio, Cavallina, Clarissa, Colucci, Paola, Santori, Alessia, Macrì, Simone, James L McGaugh, and Patrizia Campolongo. Enhanced brain activity associated with memory access in highly superior autobiographical memory. Proceedings of the National Academy of Sciences, 115(30):7795–7800, 2018.Google Scholar
[645] Santosa, Fadil and Symes, William W. Linear inversion of band-limited reflection seismograms. SIAM Journal on Scientific and Statistical Computing, 7(4):1307– 1330, 1986.Google Scholar
[646] Sarkar, Subir. Big bang nucleosynthesis and physics beyond the standard model. Reports on Progress in Physics, 59(12):1493, 1996.Google Scholar
[647] Satoh, H. and Funatsu, K.. SOPHIA, a knowledge base-guided reaction prediction system - utilization of a knowledge base derived from a reaction database. Journal of Chemical Information and Modeling, 35(1):34–44, January 1995.Google Scholar
[648] Savage, L. J.. The foundations of statistics. Dover, New York, 1972. (First Edition in 1954).Google Scholar
[649] Schapire, Robert E. The strength of weak learnability. Machine Learning, 5(2):197–227, 1990.Google Scholar
[650] Schapire, Robert E. Explaining adaboost. In Empirical Inference, pages 37–52. Springer, 2013.Google Scholar
[651] Schaul, Tom, Horgan, Daniel, Gregor, Karol, and Silver, David. Universal value function approximators. In International Conference on Machine Learning (ICML), pages 1312–1320, 2015.Google Scholar
[652] Schmidhuber, Jürgen. Learning factorial codes by predictability minimization. Neural Computation, 4:863–879, 1991.Google Scholar
[653] Schmidhuber, Jürgen. Deep learning in neural networks: An overview. Neural Networks, 61:85–117, 2015.Google Scholar
[654] Schmidt, Michael and Lipson, Hod. Distilling free-form natural laws from experimental data. Science, 324(5923):81–85, 2009.Google Scholar
[655] Schneider, Tapio, Teixeira, João, Christopher S. Bretherton, Florent Brient, Kyle G. Pressel, Christoph Schär, and A. Pier Siebesma. Climate goals and computing the future of clouds. Nature Climate Change, 7(1):3–5, 2017.Google Scholar
[656] Schölkopf, B., Burges, C.J.C., and Smola, A.J., editors. Advances in Kernel Methods - Support Vector Learning. MIT Press, Cambridge, MA, 1998.Google Scholar
[657] Scholkopf, B. and Smola, A.J.. Learning with Kernels. MIT Press, Cambridge, MA, 2002.Google Scholar
[658] Schreiber, S.L.. Target-oriented and diversity-oriented organic synthesis in drug discovery. Science, 287:1964–1969, 2000.Google Scholar
[659] Schreiber, S.L.. The small-molecule approach to biology: chemical genetics and diversity-oriented organic synthesis make possible the systematic exploration of biology. Chemical and Engineering News, 81:51–61, 2003.Google Scholar
[660] Schulman, John, Levine, Sergey, Abbeel, Pieter, Jordan, Michael, and Moritz, Philipp. Trust region policy optimization. In International Conference on Machine Learning, pages 1889–1897, 2015.Google Scholar
[661] Schulman, John, Moritz, Philipp, Levine, Sergey, Jordan, Michael, and Abbeel, Pieter. High-dimensional continuous control using generalized advantage estimation. In Proceedings of the International Conference on Learning Representations (ICLR), 2016.Google Scholar
[662] Schulman, John, Wolski, Filip, Dhariwal, Prafulla, Radford, Alec, and Klimov, Oleg. Proximal policy optimization algorithms. arXiv:1707.06347, 2017.Google Scholar
[663] Schütt, Kristof T, Arbabzadah, Farhad, Chmiela, Stefan, Müller, Klaus R, and Tkatchenko, Alexandre. Quantum-chemical insights from deep tensor neural networks. Nature communications, 8:13890, 2017.Google Scholar
[664] Sejnowski, T.J. On the stochastic dynamics of neuronal interaction. Biological cybernetics, 22(4):203–211, 1976.Google Scholar
[665] Sejnowski, T.J. and Rosenberg, C.R.. Parallel networks that learn to pronounce english text. Complex systems, 1(1):145–168, 1987.Google Scholar
[666] Sello, G.. Reaction prediction: the suggestions of the Beppe program. Journal of Chemical Information and Computer Sciences, 32(6):713–717, 1992.Google Scholar
[667] Senior, Andrew W, Evans, Richard, Jumper, John, Kirkpatrick, James, Sifre, Laurent, Green, Tim, Qin, Chongli, Žídek, Augustin, Nelson, Alexander WR, Bridgland, Alex, et al. Improved protein structure prediction using potentials from deep learning. Nature, 577(7792):706–710, 2020.Google Scholar
[668] Shalev-Shwartz, Shai and Ben-David, Shai. Understanding machine learning: From theory to algorithms. Cambridge university press, 2014.Google Scholar
[669] Shannon, C. E.. A mathematical theory of communication. Bell System Technical Journal, 27:379–423, 623–656, 1948.Google Scholar
[670] Shen, Wei, Zhou, Mu, Yang, Feng, Yang, Caiyun, and Tian, Jie. Multi-scale convolutional neural networks for lung nodule classification. In International Conference on Information Processing in Medical Imaging, pages 588–599. Springer, 2015.Google Scholar
[671] Sherstov, Alexander A.. Separating ac0 from depth-2 majority circuits. SIAM Journal on Computing, 38(6):2113–2129, 2009.Google Scholar
[672] Sherstov, Alexander A. and Stone, Peter. On continuous-action Q-learning via tile coding function approximation. Under Review, 2004.Google Scholar
[673] Sherwood, Steven C., Bony, Sandrine, and Dufresne, Jean-Louis. Spread in model climate sensitivity traced to atmospheric convective mixing. Nature, 505(7481):37–42, 2014.Google Scholar
[674] Shimmin, Chase, Sadowski, Peter, Baldi, Pierre, Weik, Edison, Whiteson, Daniel, Goul, Edward, and Søgaard, Andreas. Decorrelated jet substructure tagging using adversarial neural networks. Physical Review D, 96(7):074034, 2017.Google Scholar
[675] Shindyalov, I.N., Kolchanov, N.A., and Sander, C.. Can three-dimensional contacts of proteins be predicted by analysis of correlated mutations? Protein Engineering, 7:349–358, 1994.Google Scholar
[676] Shoji, Mitsuo, Isobe, Hiroshi, and Yamaguchi, Kizashi. QM/MM study of the S 2 to S3 transition reaction in the oxygen-evolving complex of photosystem ii. Chemical Physics Letters, 636:172 – 179, 2015.Google Scholar
[677] Shwartz-Ziv, Ravid and Tishby, Naftali. Opening the black box of deep neural networks via information. arXiv:1703.00810, 2017.Google Scholar
[678] Silver, David, Huang, Aja, Maddison, Chris J, Guez, Arthur, Sifre, Laurent, Den Driessche, George Van, Schrittwieser, Julian, Antonoglou, Ioannis, Panneershelvam, Veda, Lanctot, Marc, et al. Mastering the game of Go with deep neural networks and tree search. Nature, 529(7587):484–489, 2016.Google Scholar
[679] Silver, David, Hubert, Thomas, Schrittwieser, Julian, Antonoglou, Ioannis, Lai, Matthew, Guez, Arthur, Lanctot, Marc, Sifre, Laurent, Kumaran, Dharshan, Graepel, Thore, et al. Mastering chess and shogi by self-play with a general reinforcement learning algorithm. arXiv:1712.01815, 2017.Google Scholar
[680] Silver, David, Lever, Guy, Heess, Nicolas, Degris, Thomas, Wierstra, Daan, and Riedmiller, Martin. Deterministic policy gradient algorithms. In International Conference on Machine Learning (ICML), 2014.Google Scholar
[681] Silver, David, Schrittwieser, Julian, Simonyan, Karen, Antonoglou, Ioannis, Huang, Aja, Guez, Arthur, Hubert, Thomas, Baker, Lucas, Lai, Matthew, Bolton, Adrian, et al. Mastering the game of Go without human knowledge. Nature, 550(7676):354, 2017.Google Scholar
[682] Silver, David, Hasselt, Hado van, Hessel, Matteo, Schaul, Tom, Arthur Guez, Tim Harley, Gabriel Dulac-Arnold, David Reichert, Neil Rabinowitz, Andre Barreto, et al. The Predictron: End-To-End Learning and Planning. arXiv:1612.08810, 2016.Google Scholar
[683] Simonyan, Karen and Zisserman, Andrew. Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556, 2014.Google Scholar
[684] Singh, Satinder and Bertsekas, Dimitri. Reinforcement learning for dynamic channel allocation in cellular telephone systems. Advances in Neural Information Processing Systems, pages 974–980, 1997.Google Scholar
[685] Singh, Satinder P., Jaakkola, Tommi S., and Jordan, Michael I.. Learning Without State-Estimation in Partially Observable Markovian Decision Processes. In ICML, pages 284–292, 1994.Google Scholar
[686] Singh, Satinder P. and Sutton, Richard S.. Reinforcement learning with replacing eligibility traces. Machine Learning, 22(1-3):123–158, 1996.Google Scholar
[687] Sjostrand, T. et al. PYTHIA 6.4 physics and manual. JHEP, 05:026, 2006.Google Scholar
[688] Slagle, J.L., Chang, C.L., and Heller, S.R.. A clustering and data reorganization algorithm. IEEE Transactions on Systems, Man and Cybernetics, 5:121–128, 1975.Google Scholar
[689] Smyth, Padhraic, Heckerman, David, and Jordan, Michael I.. Probabilistic Independence Networks for Hidden Markov Probability Models. Neural Computation, 9(2):227–269, 1997.Google Scholar
[690] Snoek, Jasper, Larochelle, Hugo, and Adams, Ryan P.. Practical Bayesian optimization of machine learning algorithms. In Advances in Neural Information Processing Systems 25, 2951–2959. Curran Associates, Inc., 2012.Google Scholar
[691] Socher, Richard, Perelygin, Alex, Wu, Jean Y., Chuang, Jason, Manning, Christopher D., Ng, Andrew Y., Potts, Christopher, et al. Recursive deep models for semantic compositionality over a sentiment treebank. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 1631–1642. Association for Computational Linguistics, 2013.Google Scholar
[692] Socorro, I.M., Taylor, K., and Goodman, J.M.. ROBIA: a reaction prediction program. Organic Letters, 7(16):3541–3544, 2005.Google Scholar
[693] Sollich, Peter and Krogh, Anders. Learning with ensembles: How overfitting can be useful. In Advances in Neural Information Processing Systems, 190–196, 1996.Google Scholar
[694] Spaan, Matthijs T.J. and Vlassis, Nikos. A point-based POMDP algorithm for robot planning. In Proceedings of IEEE International Conference on Robotics and Automation (ICRA), 2004, volume 3, pages 2399–2404. IEEE, 2004.Google Scholar
[695] Spencer, Matt, Eickholt, Jesse, and Cheng, Jianlin. A deep learning network approach to ab initio protein secondary structure prediction. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 12(1):103–112, 2015.Google Scholar
[696] Spiegelhalter, David J. and Lauritzen, Steffen L.. Sequential updating of conditional probabilities on directed graphical structures. Networks, 20(5):579–605, 1990.Google Scholar
[697] Sprecher, David A.. On the structure of continuous functions of several variables. Transactions of the American Mathematical Society, 115:340–355, 1965.Google Scholar
[698] Srivastava, Nitish, Hinton, Geoffrey, Krizhevsky, Alex, Sutskever, Ilya, and Salakhutdinov, Ruslan. Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15:1929–1958, 2014.Google Scholar
[699] Srivastava, Nitish, Hinton, Geoffrey E, Krizhevsky, Alex, Sutskever, Ilya, and Salakhutdinov, Ruslan. Dropout: a simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15(1):1929–1958, 2014.Google Scholar
[700] Stockwell, B. R.. Exploring biology with small organic molecules. Nature, 432:846–854, 2004.Google Scholar
[701] Stollenga, Marijn, Beyon, Wonmin, Liwicki, Markus, and Schmidhuber, Juergen. Parallel multi-dimensional LSTM, with application to fast biomedical volumetric image segmentation. In Advances in Neural Information Processing Systems (NIPS), 2015. Preprint arXiv:1506.07452 [cs.CV].Google Scholar
[702] Storey, J. et al. Particle tracking at 4 k: The fast annihilation cryogenic tracking (fact) detector for the AEgIS Antimatter gravity experiment. Nucl. Instr. Meth. A, 732:437–441, 2013.Google Scholar
[703] Stormo, Gary D., Schneider, Thomas D., Gold, Larry, and Ehrenfeucht, Andrzej. Use of the “Perceptron” algorithm to distinguish translational initiation sites in E. coli. Nucleic Acids Research, 10(9):2997–3011, 1982.Google Scholar
[704] Strandlie, A. and Frühwirth, R.. Track and vertex reconstruction: From classical to adaptive methods. Rev. Mod. Phys., 82, 2010.Google Scholar
[705] Sutskever, Ilya, Vinyals, Oriol, and Le, Quoc V.. Sequence to sequence learning with neural networks. Advances in Neural Information Processing Systems, 27:3104– 3112, 2014.Google Scholar
[706] Sutton, Richard S.. Learning to predict by the methods of temporal differences. Machine Learning, 3(1):9–44, 1988.Google Scholar
[707] Sutton, Richard S.. Integrated architectures for learning, planning, and reacting based on approximating dynamic programming. In Machine Learning Proceedings 1990, pages 216–224. Elsevier, 1990.Google Scholar
[708] Richard, S. Sutton and Andrew G. Barto. Reinforcement Learning: An Introduction. MIT Press, 1998.Google Scholar
[709] Sutton, Richard S, Precup, Doina, and Singh, Satinder. Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning. Artificial Intelligence, 112(1):181–211, 1999.Google Scholar
[710] Swamidass, S. J., Azencott, C., Gramajo, H., Tsai, S., and Baldi, P.. The influence relevance voter: An accurate and interpretable virtual high-throughput screening method. Journal of Chemical Information and Modeling, 49(4):756–766, 2009.Google Scholar
[711] Swamidass, S. J., Chen, J., Bruand, J., Phung, P., Ralaivola, L., and Baldi, P.. Kernels for small molecules and the prediction of mutagenicity, toxicity, and anti-cancer activity. Bioinformatics, 21(Supplement 1):i359–368, 2005. Proceedings of the 2005 ISMB Conference.Google Scholar
[712] Swamidass, S.J. and Baldi, P.. Bounds and algorithms for exact searches of chemical fingerprints in linear and sub-linear time. Journal of Chemical Information and Modeling, 47(2):302–317, 2007.Google Scholar
[713] David Sweatt, J.. The emerging field of neuroepigenetics. Neuron, 80(3):624–632, 2013.Google Scholar
[714] Sweredoski, M.J. and Baldi, P.. Pepito: Improved discontinuous B-cell epitope prediction using multiple distance thresholds and half-sphere exposure. Bioinformatics, 24(12):1459–1460, 2008.Google Scholar
[715] Sweredoski, M.J. and Baldi, P.. COBEpro: a novel system for predicting continuous B-cell epitopes. Protein Engineering Design and Selection, 22(3):113, 2009.Google Scholar
[716] Szegedy, C., Liu, Wei, Yangqing, Jia, Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A.. Going deeper with convolutions. In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1–9, June 2015.Google Scholar
[717] Szegedy, Christian, Liu, Wei, Jia, Yangqing, Sermanet, Pierre, Reed, Scott, Anguelov, Dragomir, Erhan, Dumitru, Vanhoucke, Vincent, and Rabinovich, Andrew. Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1–9, 2015.Google Scholar
[718] Talagrand, Michel. Self averaging and the space of interactions in neural networks. Random Structures & Algorithms, 14(3):199–213, 1999.Google Scholar
[719] Masaharu, Tanabashi, Hagiwara, K., Hikasa, K., Nakamura, K., Sumino, Y., Taka-hashi, F., Tanaka, J., Agashe, K., Aielli, G., Amsler, C., et al. Review of particle physics. Physical Review D, 98(3):030001, 2018.Google Scholar
[720] Tank, David and Hopfield, John. Neural computation by concentrating information in time. Proceedings of the National Academy of Sciences, 84(7):1896–1900, 1987.Google Scholar
[721] Tavakoli, A., Agostinelli, F., and Baldi, P.. Splash: Learnable activation functions for improving accuracy and adversarial robustness. 2020. arXiv:2006.08947.Google Scholar
[722] Taylor, Matthew E. and Stone, Peter. Cross-domain transfer for reinforcement learning. In Proceedings of the 24th International Conference on Machine Learning, pages 879–886. ACM, 2007.Google Scholar
[723] Tesauro, Gerald. Temporal difference learning and TD-Gammon. Communications of the ACM, 38(3):58–68, 1995.Google Scholar
[724] Thaler, Jesse and Van Tilburg, Ken. Identifying boosted objects with N-subjettiness. JHEP, 1103:015, 2011.Google Scholar
[725] Thaler, Jesse and Van Tilburg, Ken. Maximizing boosted top identification by minimizing N-subjettiness. JHEP, 02:093, 2012.Google Scholar
[726] Theano Development Team. Theano: A Python framework for fast computation of mathematical expressions. abs/1605.02688, May 2016.Google Scholar
[727] Thomas, John, Maszczyk, Tomasz, Sinha, Nishant, Kluge, Tilmann, and Dauwels, Justin. Deep learning-based classification for brain-computer interfaces. In 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pages 234–239. IEEE, 2017.Google Scholar
[728] Thorndike, Edward Lee. Animal Intelligence: Experimental Studies. Transaction Publishers, 1965.Google Scholar
[729] Tibshirani, Robert. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Methodological), 58(1):267–288, 1996.Google Scholar
[730] Tipping, Michael E.. Sparse Bayesian learning and the relevance vector machine. Journal of Machine Learning Research, 1(Jun):211–244, 2001.Google Scholar
[731] Tishby, N., Pereira, F.C., and Bialek, W.. The information bottleneck method. arXiv preprint physics/0004057, 2000.Google Scholar
[732] Tishby, N. and Zaslavsky, N.. Deep learning and the information bottleneck principle. In 2015 IEEE Information Theory Workshop (ITW), pages 1–5, April 2015.Google Scholar
[733] Tishby, Naftali and Zaslavsky, Noga. Deep learning and the information bottleneck principle. In 2015 IEEE Information Theory Workshop (ITW), pages 1–5. IEEE, 2015.Google Scholar
[734] Trieu, Tuan and Cheng, Jianlin. Large-scale reconstruction of 3D Structures of human chromosomes from chromosomal contact data. Nucleic Acids Research, 42(7):e52–e52, 2014.Google Scholar
[735] Tsitsiklis, John N and Roy, Benjamin Van. An analysis of temporal-difference learning with function approximation. Automatic Control, IEEE Transactions on, 42(5):674–690, 1997.Google Scholar
[736] Turing, A.M.. On computable numbers, with an application to the Entschei-dungsproblem. Proceedings of the London Mathematical Society, Series 2, 41:230–267, 1936.Google Scholar
[737] Uberbacher, Edward C. and Mural, Richard J.. Locating protein-coding regions in human DNA sequences by a multiple sensor-neural network approach. Proceedings of the National Academy of Sciences, 88(24):11261–11265, 1991.Google Scholar
[738] Udrescu, Silviu-Marian and Tegmark, Max. AI Feynman a Physics-Inspired Method for Symbolic Regression. arXiv:1905.11481, 2019.Google Scholar
[739] Uhlenbeck, George E and Ornstein, Leonard S. On the Theory of the Brownian Motion. Physical Review, 36(5):823, 1930.Google Scholar
[740] Urban, G., Torrisi, M., Magnan, C., Pollastri, G., and Baldi, P.. Protein profiles: Biases and protocols. Computational and Structural Biotechnology Journal, 18:2281– 2289, 2020. Also BIORXIV/2020/148718.Google Scholar
[741] Urban, Gregor, Subrahmanya, Niranjan, and Baldi, Pierre. Inner and outer recursive neural networks for chemoinformatics applications. Journal of chemical information and modeling, 58(2):207–211, 2018.Google Scholar
[742] Urban, Gregor, Tripathi, Priyam, Alkayali, Talal, Mittal, Mohit, Jalali, Farid, Karnes, William, and Baldi, Pierre. Deep learning localizes and identifies polyps in real time with 96% accuracy in screening colonoscopy. Gastroenterology, 155(4):1069– 1078, 2018.Google Scholar
[743] Valiant, Leslie G. A theory of the learnable. Communications of the ACM, 27(11):1134–1142, 1984.Google Scholar
[744] Valiant, Leslie G. Robust logics. Artificial Intelligence, 117(2):231–253, 2000.Google Scholar
[745] Valiant, Leslie G. The hippocampus as a stable memory allocator for cortex. Neural Computation, 24(11):2873–2899, 2012.Google Scholar
[746] Gucht, Jeffrey van der, Davelaar, Jordy, Hendriks, Luc, Porth, Oliver, Olivares, Hector, Yosuke Mizuno, Christian M Fromm, and Heino Falcke. Deep horizon: A machine learning network that recovers accreting black hole parameters. Astronomy & Astrophysics, 636:A94, 2020.Google Scholar
[747] Maaten, Laurens Van Der, Postma, Eric, and Herik, Jaap Van den. Dimensionality reduction: a comparative. J Mach Learn Res, 10:66–71, 2009.Google Scholar
[748] Hasselt, Hado Van, Guez, Arthur, and Silver, David. Deep reinforcement learning with double q-learning. In AAAI, pages 2094–2100, 2016.Google Scholar
[749] Vang, Yeeleng S and Xie, Xiaohui. Hla class i binding prediction via convolutional neural networks. Bioinformatics, Apr 2017.Google Scholar
[750] Vapnik, V.. Estimation of Dependences Based on Empirical Data. Springer-Verlag, 1982.Google Scholar
[751] Vapnik, V.. The Nature of Statistical Learning Theory. Springer Verlag, New York, 1995.Google Scholar
[752] Vapnik, V. N. and Chervonenkis, A. Y.. On the uniform convergence of relative frequencies of events to their probabilities. Theory of Probability and its Applications, XVI(2):264–280, 1971.Google Scholar
[753] Vapnik, Vladimir N and Chervonenkis, A Ya. On the uniform convergence of relative frequencies of events to their probabilities. In Measures of complexity, pages 11–30. Springer, 2015.Google Scholar
[754] Vapnik, Vladimir Naumovich and Chervonenkis, Aleksei Yakovlevich. The uniform convergence of frequencies of the appearance of events to their probabilities. In Doklady Akademii Nauk, volume 181, pages 781–783. Russian Academy of Sciences, 1968.Google Scholar
[755] Vassura, M., Margara, L., Lena, P. Di, Medri, F., Fariselli, P., and Casadio, R.. FT-COMAR: fault tolerant three-dimensional structure reconstruction from protein contact maps. Bioinformatics, 24(10):1313, 2008.Google Scholar
[756] Vattani, A.. A simpler proof of the hardness of k-means clustering in the plane. UCSD Technical Report, 2010.Google Scholar
[757] Vazquez, RA, Halzen, Francis, and Zas, E. Improving the Čerenkov imaging technique with neural networks. Physical Review D, 45(1):356, 1992.Google Scholar
[758] Vecsey, C.G., Hawk, J.D., Lattal, K.M., Stein, J.M., Fabian, S.A., Attner, M.A., Cabrera, S.M., McDonough, C.B., Brindle, P.K., Abel, T., et al. Histone deacetylase inhibitors enhance memory and synaptic plasticity via CREB: CBP-dependent transcriptional activation. Journal of Neuroscience, 27(23):6128, 2007.Google Scholar
[759] Vendruscolo, M., Kussell, E., and Domany, E.. Recovery of protein structure from contact maps. Folding and Design, 2:295–306, 1997.Google Scholar
[760] Venkatesh, Santosh S and Baldi, Pierre. Programmed interactions in higher-order neural networks: Maximal capacity. Journal of Complexity, 7(3):316–337, 1991.Google Scholar
[761] Venkatesht, Santosh S and Baldi, Pierre. Programmed interactions in higher-order neural networks: The outer-product algorithm. Journal of Complexity, 7(4):443– 479, 1991.Google Scholar
[762] Vershynin, Roman. High-Dimensional Probability: An Introduction with Applications in Data Science. Cambridge University Press, 2018.Google Scholar
[763] Vershynin, Roman. Memory capacity of neural networks with threshold and relu activations. arXiv:2001.06938, 2020.Google Scholar
[764] Vincent, P.. A connection between score matching and denoising autoencoders. Neural Computation, 23(7):1661–1674, 2011.Google Scholar
[765] Vincent, P., Larochelle, H., Bengio, Y., and Manzagol, P.A.. Extracting and composing robust features with denoising autoencoders. In Proceedings of the 25th International Conference on Machine learning, 1096–1103. ACM, 2008.Google Scholar
[766] Vincent, Pascal, Larochelle, Hugo, Lajoie, Isabelle, Bengio, Yoshua, and Manzagol, Pierre-Antoine. Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. The Journal of Machine Learning Research, 11:3371–3408, 2010.Google Scholar
[767] Vogel-Ciernia, A., Barrett, R. M., Matheos, D. P., Kramar, E., Azzawi, S., Chen, Y., Magnan, C. N., Zeller, M., Sylvain, A., Haettig, J., Jia, Y., Tran, A., Dang, R., Post, R. J., Chabrier, M., Babayan, A., Wu, J. I., Crabtree, G. R., Baldi, P., Baram, T. Z., Lynch, G., and Wood, M. A.. The neuron-specific chromatin regulatory subunit BAF53b is necessary for synaptic plasticity and memory. Nature Neuroscience, 16:552–561, 2013.Google Scholar
[768] Volpato, Viola, Adelfio, Alessandro, and Pollastri, Gianluca. Accurate prediction of protein enzymatic class by n-to-1 neural networks. BMC bioinformatics, 14(1):S11, 2013.Google Scholar
[769] Volpato, Viola, Alshomrani, Badr, and Pollastri, Gianluca. Accurate ab initio and template-based prediction of short intrinsically-disordered regions by bidirectional recurrent neural networks trained on large-scale datasets. International Journal of Molecular Sciences, 16(8):19868–19885, 2015.Google Scholar
[770] von Neumann, J.. The Computer and the Brain. Yale University Press, New Haven, CT, 1958.Google Scholar
[771] Wager, Stefan, Wang, Sida, and Liang, Percy. Dropout training as adaptive regularization. In C.J.C. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K.Q. Weinberger, editors, Advances in Neural Information Processing Systems 26, pages 351–359. 2013.Google Scholar
[772] Wang, Chi and Williams, A.C.. The threshold order of a Boolean function. Discrete Applied Mathematics, 31(1):51–69, 1991.Google Scholar
[773] Wang, Dayong, Khosla, Aditya, Gargeya, Rishab, Irshad, Humayun, and Beck, Andrew H.. Deep learning for identifying metastatic breast cancer. arXiv:1606.05718, 2016.Google Scholar
[774] Wang, Juan, Ding, Huanjun, Azamian, FateMeh, Zhou, Brian, Iribarren, Carlos, Molloi, Sabee, and Baldi, Pierre. Detecting cardiovascular disease from mammograms with deep learning. IEEE Transactions on Medical Imaging, 36(5): 1172–1181, 2017.Google Scholar
[775] Wang, Juan, Fang, Zhiyuan, Lang, Ning, Yuan, Huishu, Min-Ying, Su, and Baldi, Pierre. A multi-resolution approach for spinal metastasis detection using deep Siamese neural networks. Computers in Biology and Medicine, 84:137–146, 2017.Google Scholar
[776] Wang, May D. and Hassanzadeh, Hamid Reza. Deeperbind: Enhancing prediction of sequence specificities of DNA binding proteins. bioRxiv, page 099754, 2017.Google Scholar
[777] Wang, Sheng, Sun, Siqi, Li, Zhen, Zhang, Renyu, and Xu, Jinbo. Accurate de novo prediction of protein contact map by ultra-deep learning model. PLoS Computational Biology, 13(1):e1005324, 2017.Google Scholar
[778] Wang, Xiaofeng and Sandholm, Tuomas. Reinforcement learning to play an optimal Nash equilibrium in team Markov games. In Advances in Neural Information Processing Systems, 1571–1578, 2002.Google Scholar
[779] Wang, Y., Xiao, J., Suzek, T.O., Zhang, J., Wang, J., and Bryant, S.H.. PubChem: a public information system for analyzing bioactivities of small molecules. Nucleic Acids Research, 37(Web Server issue):W623, 2009.Google Scholar
[780] Christopher, J.C.H. Watkins and Peter Dayan. Q-learning. Machine Learning, 8(3–4):279–292, 1992.Google Scholar
[781] Watter, Manuel, Springenberg, Jost, Boedecker, Joschka, and Riedmiller, Martin. Embed to control: alocally linear latent dynamics model for control from raw images. In Advances in Neural Information Processing Systems, pages 2746– 2754, 2015.Google Scholar
[782] Wei, Jennifer N., Duvenaud, David, and Aspuru-Guzik, Alán. Neural networks for the prediction of organic chemistry reactions. ACS Central Science, 2(10):725– 732, 2016. PMID: 27800555.Google Scholar
[783] Weininger, David, Weininger, Arthur, and Weininger, Joseph L.. Smiles. 2. Algorithm for generation of unique smiles notation. Journal of Chemical Information and Computer Sciences, 29(2):97–101, 1989.Google Scholar
[784] Welling, Max, Rosen-Zvi, Michal, and Hinton, Geoffrey E.. Exponential family harmoniums with an application to information retrieval. In Advances in Neural Information Processing Systems, 1481–1488, 2005.Google Scholar
[785] Wendell, James G.. A problem in geometric probability. Math. Scand., 11:109– 112, 1962.Google Scholar
[786] Widrow, B. and Hoff, M.E.. Adaptive switching circuits. In Institute of Radio Engineers, Western Electronic Show and Convention, Convention Record, Part 4, pages 96–104, 1960.Google Scholar
[787] Willard, Jared, Jia, Xiaowei, Xu, Shaoming, Steinbach, Michael, and Kumar, Vipin. Integrating physics-based modeling with machine learning: a survey. arXiv:2003.04919, 2020.Google Scholar
[788] Williams, Ronald J.. Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine Learning, 8(3–4):229–256, 1992.Google Scholar
[789] Wood, M.A., Kaplan, M.P., Park, A., Blanchard, E.J., Oliveira, A.M.M., Lombardi, T.L., and Abel, T.. Transgenic mice expressing a truncated form of CREB-binding protein (CBP) exhibit deficits in hippocampal synaptic plasticity and memory storage. Learning & Memory, 12(2):111, 2005.Google Scholar
[790] Wu, Jin Long, Xiao, Heng, and Paterson, Eric. Physics-informed machine learning approach for augmenting turbulence models: A comprehensive framework. Physical Review Fluids, 7(3):074602, jul 2018.Google Scholar
[791] Wu, L. and Baldi, P.. A scalable machine learning approach to GO. Advances in Neural Information Processing Systems, 19:1521, 2007.Google Scholar
[792] Wu, Lin and Baldi, Pierre. Learning to play GO using recursive neural networks. Neural Networks, 21(9):1392–1400, 2008.Google Scholar
[793] XENON Collaboration. First dark matter search results from the XENON1T experiment. Physical review letters, 119(18):181301, 2017.Google Scholar
[794] XENON Collaboration. The XENON1T dark matter experiment. The European Physical Journal C, 77(12):881, Dec 2017.Google Scholar
[795] Xiao, Han, Rasul, Kashif, and Vollgraf, Roland. Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms, arXiv:1708.07747, 2017.Google Scholar
[796] Xie, Tian and Grossman, Jeffrey C. Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties. Physical Review Letters, 120(14):145301, 2018.Google Scholar
[797] Xie, Xiaohui and Seung, H. Sebastian. Spike-based learning rules and stabilization of persistent neural activity. In S.A. Solla, T.K. Leen, and K. Müller, editors, Advances in Neural Information Processing Systems 12, pages 199–208. MIT Press, 2000.Google Scholar
[798] Xie, Xiaohui and Seung, H. Sebastian. Equivalence of backpropagation and contrastive Hebbian learning in a layered network. Neural Computation, 15(2): 441–454, 2003.Google Scholar
[799] Xiong, Hui Y., Alipanahi, Babak, Lee, Leo J., Bretschneider, Hannes, Merico, Daniele, Ryan K.C. Yuen, Yimin Hua, Serge Gueroussov, Hamed S. Najafabadi, Timothy R. Hughes, et al. The human splicing code reveals new insights into the genetic determinants of disease. Science, 347(6218):1254806, 2015.Google Scholar
[800] Xue, L., Godden, J.F., Stahura, F.L., and Bajorath, J.. Profile scaling increases the similarity search performance of molecular fingerprints containing numerical descriptors and structural keys. Journal of Chemical Information and Computer Sciences, 43:1218–1225, 2003.Google Scholar
[801] Xue, L., Stahura, F.L., and Bajorath, J.. Similarity search profiling reveals effects of fingerprint scaling in virtual screening. Journal of Chemical Information and Computer Sciences, 44:2032–2039, 2004.Google Scholar
[802] Yamins, Daniel L.K. and DiCarlo, James J.. Using goal-driven deep learning models to understand sensory cortex. Nature Neuroscience, 19(3):356–365, 2016.Google Scholar
[803] Yashiro, Hisashi, Kajikawa, Yoshiyuki, Miyamoto, Yoshiaki, Yamaura, Tsuyoshi, Yoshida, Ryuji, and Tomita, Hirofumi. Resolution dependence of the diurnal cycle of precipitation simulated by a global cloud-system resolving model. Sola, 12:272–276, 2016.Google Scholar
[804] Yuh, C.H., Bolouri, H., and Davidson, E.H.. Genomic cis-regulatory logic: experimental and computational analysis of a sea urchin gene. Science, 279:1896–1902, 1998.Google Scholar
[805] Yuh, C.H., Bolouri, H., and Davidson, E.H.. Cis-regulatory logic in the endo16 gene: switching from a specification to a differentiation mode of control. Development, 128:617–629, 2001.Google Scholar
[806] Yun, Chulhee, Sra, Suvrit, and Jadbabaie, Ali. Small ReLu networks are powerful memorizers: a tight analysis of memorization capacity. In Advances in Neural Information Processing Systems, 15558–15569, 2019.Google Scholar
[807] Zaslavsky, Thomas. Facing up to Arrangements: Face-Count Formulas for Partitions of Space by Hyperplanes. Memoirs of the American Mathematical Society, volume 154, 1975.Google Scholar
[808] Zhang, Sai, Zhou, Jingtian, Hu, Hailin, Gong, Haipeng, Chen, Ligong, Cheng, Chao, and Zeng, Jianyang. A deep learning framework for modeling structural features of RNA-binding protein targets. Nucleic Acids Research, 44(4):e32–e32, 2015.Google Scholar
[809] Zhang, Wei and Dietterich, Tom G.. High-performance job-shop scheduling with a time-delay TD network. Advances in Neural Information Processing Systems, 8:1024–1030, 1996.Google Scholar
[810] Zhang, Weihong. Algorithms for partially observable Markov decision processes. PhD thesis, Citeseer, 2001.Google Scholar
[811] Zhang, Xiang, Yao, Lina, Wang, Xianzhi, Monaghan, Jessica, and McAlpine, David. A survey on deep learning based brain computer interface: recent advances and new frontiers. arXiv:1905.04149, 2019.Google Scholar
[812] Zhang, Z., Oelert, W., Grzonka, D., and Sefzick, T.. The antiproton annihilation detector system of the ATRAP experiment. Chinese Science Bulletin, 54:189– 195, 2009.Google Scholar
[813] Zhao, Qiyang and Griffin, Lewis D.. Suppressing the unusual: towards robust CNNS using symmetric activation functions, 2016. Preprint arxiv:1603.05145.Google Scholar
[814] Zheng, Wei, Li, Yang, Zhang, Chengxin, Pearce, Robin, Mortuza, S.M., and Zhang, Yang. Deep-learning contact-map guided protein structure prediction in CASP13. Proteins: Structure, Function, and Bioinformatics, 87(12):1149–1164, 2019.Google Scholar
[815] Zhou, Jian and Troyanskaya, Olga G.. Predicting effects of noncoding variants with deep learning-based sequence model. Nature Methods, 12(10):931–4, Oct 2015.Google Scholar
[816] Ziletti, Angelo, Kumar, Devinder, Scheffler, Matthias, and Ghiringhelli, Luca M.. Insightful classification of crystal structures using deep learning. Nature communications, 9(1):2775, 2018.Google Scholar
[817] Zipser, David and Andersen, Richard A.. A back-propagation programmed network that simulates response properties of a subset of posterior parietal neurons. Nature, 331(6158):679–684, 1988.Google Scholar
[818] Zuev, Yu. A.. Asymptotics of the logarithm of the number of threshold functions of the algebra of logic. Soviet Mathematics Doklady, 39(3):512–513, 1989.Google Scholar
[819] Zuev, Yu. A.. Combinatorial-probability and geometric methods in threshold logic. Diskretnaya Matematika, 3(2):47–57, 1991.Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

  • References
  • Pierre Baldi, University of California, Irvine
  • Book: Deep Learning in Science
  • Online publication: 17 April 2021
  • Chapter DOI: https://doi.org/10.1017/9781108955652.018
Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

  • References
  • Pierre Baldi, University of California, Irvine
  • Book: Deep Learning in Science
  • Online publication: 17 April 2021
  • Chapter DOI: https://doi.org/10.1017/9781108955652.018
Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

  • References
  • Pierre Baldi, University of California, Irvine
  • Book: Deep Learning in Science
  • Online publication: 17 April 2021
  • Chapter DOI: https://doi.org/10.1017/9781108955652.018
Available formats
×