Paper list

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First public	First author	Title	Venue	Original	Era	Theme
1943-12	McCulloch & Pitts	A logical calculus of the ideas immanent in nervous activity	The Bulletin of Mathematical Biophysics,	DOI	Ch. 1 Origins 1943–56	Neural network learning
1948-07	Shannon	A Mathematical Theory of Communication	Bell System Technical Journal, 27(3) and	DOI	Ch. 1 Origins 1943–56	Probability, statistics and origins
1948	Wiener	Cybernetics: or Control and Communication in the Animal and the Machine	Book: The Technology Press (Cambridge, M	DOI	Ch. 1 Origins 1943–56	Probability, statistics and origins
1949-03-09	Shannon	Programming a computer for playing chess	The London, Edinburgh, and Dublin Philos	DOI	Ch. 1 Origins 1943–56	Search, planning and knowledge
1949	Hebb	The Organization of Behavior	New York: John Wiley & Sons; London: Cha	DOI	Ch. 1 Origins 1943–56	Neural network learning
1950-10	Turing	Computing Machinery and Intelligence	Mind LIX(236), October 1950	DOI	Ch. 1 Origins 1943–56	Probability, statistics and origins
1956-09	Chomsky	Three models for the description of language	IRE Transactions on Information Theory,	DOI	Ch. 1 Origins 1943–56	Language and speech
1957	Bellman	Dynamic Programming	Princeton University Press (A RAND Corpo	URL	Ch. 2 Symbols and perceptrons 1956–74	Reinforcement learning and games
1958-11	McCarthy	Programs with Common Sense	Mechanisation of Thought Processes: Proc	URL	Ch. 2 Symbols and perceptrons 1956–74	Search, planning and knowledge
1958-11	Rosenblatt	The perceptron: A probabilistic model for information storage and organization in the brain	Psychological Review, 65(6)	DOI	Ch. 2 Symbols and perceptrons 1956–74	Neural network learning
1959-02	Newell et al.	Report on a general problem-solving program	The RAND Corporation, Santa Monica, Pape	URL	Ch. 2 Symbols and perceptrons 1956–74	Search, planning and knowledge
1959-07	Samuel	Some Studies in Machine Learning Using the Game of Checkers	IBM Journal of Research and Development,	DOI	Ch. 2 Symbols and perceptrons 1956–74	Reinforcement learning and games
1960-04	McCarthy	Recursive functions of symbolic expressions and their computation by machine, Part I	Communications of the ACM, 3(4)	DOI	Ch. 2 Symbols and perceptrons 1956–74	Search, planning and knowledge
1960-06-30	Widrow & Hoff	Adaptive switching circuits	IRE WESCON Convention Record, 1960, Part	DOI	Ch. 2 Symbols and perceptrons 1956–74	Neural network learning
1961-01	Minsky	Steps toward Artificial Intelligence	Proceedings of the IRE, 49(1)	DOI	Ch. 2 Symbols and perceptrons 1956–74	Probability, statistics and origins
1965-01	Robinson	A Machine-Oriented Logic Based on the Resolution Principle	Journal of the ACM 12(1), January 1965	DOI	Ch. 2 Symbols and perceptrons 1956–74	Search, planning and knowledge
1965-06	Zadeh	Fuzzy sets	Information and Control 8(3), June 1965	DOI	Ch. 2 Symbols and perceptrons 1956–74	Search, planning and knowledge
1966-01	Weizenbaum	ELIZA—a computer program for the study of natural language communication between man and machine	Communications of the ACM 9(1), January	DOI	Ch. 2 Symbols and perceptrons 1956–74	Language and speech
1968-07	Hart et al.	A Formal Basis for the Heuristic Determination of Minimum Cost Paths	IEEE Transactions on Systems Science and	DOI	Ch. 2 Symbols and perceptrons 1956–74	Search, planning and knowledge
1969	McCarthy & Hayes	Some Philosophical Problems from the Standpoint of Artificial Intelligence	Machine Intelligence 4, ed. B. Meltzer a	DOI	Ch. 2 Symbols and perceptrons 1956–74	Search, planning and knowledge
1969	Minsky & Papert	Perceptrons: An Introduction to Computational Geometry	MIT Press (Cambridge, Mass.)	URL	Ch. 2 Symbols and perceptrons 1956–74	Neural network learning
1970-10	Fikes & Nilsson	STRIPS: A new approach to the application of theorem proving to problem solving	Artificial Intelligence 2(3–4), 1971 (No	DOI	Ch. 2 Symbols and perceptrons 1956–74	Search, planning and knowledge
1971-01	Winograd	Understanding natural language	Cognitive Psychology, 3(1) (journal); re	DOI	Ch. 2 Symbols and perceptrons 1956–74	Language and speech
1973	Lighthill	Artificial Intelligence: A General Survey	Artificial Intelligence: a paper symposi	DOI	Ch. 2 Symbols and perceptrons 1956–74	Probability, statistics and origins
1974-06	Minsky	A Framework for Representing Knowledge	MIT Artificial Intelligence Laboratory,	DOI	Ch. 3 Knowledge systems and connectionism 1974–93	Search, planning and knowledge
1974	Werbos	Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences	Ph.D. thesis, Harvard University	URL	Ch. 2 Symbols and perceptrons 1956–74	Neural network learning
1975	Holland	Adaptation in Natural and Artificial Systems	Ann Arbor: University of Michigan Press,	DOI	Ch. 3 Knowledge systems and connectionism 1974–93	Probability, statistics and origins
1975-10-20	Newell & Simon	Computer science as empirical inquiry: symbols and search	Communications of the ACM 19(3)	DOI	Ch. 3 Knowledge systems and connectionism 1974–93	Probability, statistics and origins
1976-12-08	Dempster et al.	Maximum Likelihood from Incomplete Data Via the EM Algorithm	Journal of the Royal Statistical Society	DOI	Ch. 3 Knowledge systems and connectionism 1974–93	Probability, statistics and origins
1977	Schank & Abelson	Scripts, Plans, Goals and Understanding	Lawrence Erlbaum Associates, Hillsdale,	DOI	Ch. 3 Knowledge systems and connectionism 1974–93	Search, planning and knowledge
1978-08	Buchanan & Feigenbaum	Dendral and Meta-Dendral: Their applications dimension	Artificial Intelligence 11(1-2)	DOI	Ch. 3 Knowledge systems and connectionism 1974–93	Search, planning and knowledge
1980-04	Fukushima	Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position	Biological Cybernetics 36(4)	DOI	Ch. 3 Knowledge systems and connectionism 1974–93	Vision and generative models
1980-04	McDermott	R1: A rule-based configurer of computer systems	Carnegie-Mellon University, Department o	DOI	Ch. 3 Knowledge systems and connectionism 1974–93	Search, planning and knowledge
1980-09	Searle	Minds, brains, and programs	The Behavioral and Brain Sciences 3(3)	DOI	Ch. 3 Knowledge systems and connectionism 1974–93	Probability, statistics and origins
1982-04	Hopfield	Neural networks and physical systems with emergent collective computational abilities	Proceedings of the National Academy of S	DOI	Ch. 3 Knowledge systems and connectionism 1974–93	Neural network learning
1982	Marr	Vision: A Computational Investigation into the Human Representation and Processing of Visual Information	W. H. Freeman and Company, San Francisco	URL	Ch. 3 Knowledge systems and connectionism 1974–93	Vision and generative models
1982	Werbos	Applications of advances in nonlinear sensitivity analysis	R. F. Drenick & F. Kozin (eds.), System	DOI	Ch. 3 Knowledge systems and connectionism 1974–93	Neural network learning
1983-05-13	Kirkpatrick et al.	Optimization by Simulated Annealing	Science 220(4598)	DOI	Ch. 3 Knowledge systems and connectionism 1974–93	Search, planning and knowledge
1983-09	Barto et al.	Neuronlike adaptive elements that can solve difficult learning control problems	IEEE Transactions on Systems, Man, and C	DOI	Ch. 3 Knowledge systems and connectionism 1974–93	Reinforcement learning and games
1984	Valiant	A theory of the learnable	Communications of the ACM 27(11)	DOI	Ch. 3 Knowledge systems and connectionism 1974–93	Neural network learning
1984-11	Geman & Geman	Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images	IEEE Transactions on Pattern Analysis an	DOI	Ch. 3 Knowledge systems and connectionism 1974–93	Probability, statistics and origins
1985-01	Ackley et al.	A learning algorithm for Boltzmann machines	Cognitive Science 9(1)	DOI	Ch. 3 Knowledge systems and connectionism 1974–93	Neural network learning
1986-03	Quinlan	Induction of decision trees	Machine Learning 1(1)	DOI	Ch. 3 Knowledge systems and connectionism 1974–93	Probability, statistics and origins
1986-10-09	Rumelhart et al.	Learning representations by back-propagating errors	Nature 323(6088)	DOI	Ch. 3 Knowledge systems and connectionism 1974–93	Neural network learning
1987-10	Waibel et al.	Phoneme recognition using time-delay neural networks	IEEE Transactions on Acoustics, Speech,	DOI	Ch. 3 Knowledge systems and connectionism 1974–93	Language and speech
1988	Elman	Finding Structure in Time	Cognitive Science 14(2)	DOI	Ch. 3 Knowledge systems and connectionism 1974–93	Neural network learning
1988	Hornik et al.	Multilayer feedforward networks are universal approximators	Neural Networks, 2(5)	DOI	Ch. 3 Knowledge systems and connectionism 1974–93	Neural network learning
1988	Pearl	Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference	Morgan Kaufmann (Morgan Kaufmann Series	URL	Ch. 3 Knowledge systems and connectionism 1974–93	Probability, statistics and origins
1988-08	Sutton	Learning to predict by the methods of temporal differences	Machine Learning 3(1)	DOI	Ch. 3 Knowledge systems and connectionism 1974–93	Reinforcement learning and games
1989-02	Rabiner	A tutorial on hidden Markov models and selected applications in speech recognition	Proceedings of the IEEE 77(2)	DOI	Ch. 3 Knowledge systems and connectionism 1974–93	Language and speech
1989-05	Watkins	Learning from Delayed Rewards	PhD thesis, King's College, University o	DOI	Ch. 3 Knowledge systems and connectionism 1974–93	Reinforcement learning and games
1989	Schapire	The strength of weak learnability	Machine Learning 5(2)	DOI	Ch. 4 Statistical learning 1990–2011	Probability, statistics and origins
1989-12	Cybenko	Approximation by superpositions of a sigmoidal function	Mathematics of Control, Signals, and Sys	DOI	Ch. 3 Knowledge systems and connectionism 1974–93	Neural network learning
1989-12	LeCun et al.	Backpropagation Applied to Handwritten Zip Code Recognition	Neural Computation 1(4)	DOI	Ch. 3 Knowledge systems and connectionism 1974–93	Vision and generative models
1990-06	Brown et al.	A statistical approach to machine translation	Computational Linguistics 16(2)	DOI	Ch. 4 Statistical learning 1990–2011	Language and speech
1991-02	Jacobs et al.	Adaptive Mixtures of Local Experts	Neural Computation 3(1)	DOI	Ch. 4 Statistical learning 1990–2011	Neural network learning
1991-06-15	Hochreiter	Untersuchungen zu dynamischen neuronalen Netzen	Diplomarbeit, Institut für Informatik, T	URL	Ch. 4 Statistical learning 1990–2011	Neural network learning
1992-05	Watkins & Dayan	Q-learning	Machine Learning 8(3-4) (Technical Note)	DOI	Ch. 4 Statistical learning 1990–2011	Reinforcement learning and games
1992-05	Williams	Simple statistical gradient-following algorithms for connectionist reinforcement learning	Machine Learning 8(3-4)	DOI	Ch. 4 Statistical learning 1990–2011	Reinforcement learning and games
1992-07	Boser et al.	A training algorithm for optimal margin classifiers	Proceedings of the Fifth Annual Workshop	DOI	Ch. 4 Statistical learning 1990–2011	Probability, statistics and origins
1993	Quinlan	C4.5: Programs for Machine Learning	Morgan Kaufmann Publishers, San Mateo, C	URL	Ch. 4 Statistical learning 1990–2011	Probability, statistics and origins
1994-03	Bengio et al.	Learning long-term dependencies with gradient descent is difficult	IEEE Transactions on Neural Networks 5(2	DOI	Ch. 4 Statistical learning 1990–2011	Neural network learning
1994-09	Breiman	Bagging predictors	Machine Learning 24(2), Kluwer Academic	DOI	Ch. 4 Statistical learning 1990–2011	Probability, statistics and origins
1995-03	Freund & Schapire	A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting	Journal of Computer and System Sciences	DOI	Ch. 4 Statistical learning 1990–2011	Probability, statistics and origins
1995-03	Tesauro	Temporal difference learning and TD-Gammon	Communications of the ACM, 38(3)	DOI	Ch. 4 Statistical learning 1990–2011	Reinforcement learning and games
1995	Hochreiter & Schmidhuber	Long Short-Term Memory	Neural Computation 9(8)	DOI	Ch. 4 Statistical learning 1990–2011	Neural network learning
1995	Russell & Norvig	Artificial Intelligence: A Modern Approach	Prentice Hall (1st ed.); 4th ed. Pearson	URL	Ch. 4 Statistical learning 1990–2011	Probability, statistics and origins
1995-09	Cortes & Vapnik	Support-vector networks	Machine Learning 20(3)	DOI	Ch. 4 Statistical learning 1990–2011	Probability, statistics and origins
1996	Tibshirani	Regression Shrinkage and Selection Via the Lasso	Journal of the Royal Statistical Society	DOI	Ch. 4 Statistical learning 1990–2011	Probability, statistics and origins
1997-11	Schuster & Paliwal	Bidirectional recurrent neural networks	IEEE Transactions on Signal Processing 4	DOI	Ch. 4 Statistical learning 1990–2011	Neural network learning
1998-05	Kaelbling et al.	Planning and acting in partially observable stochastic domains	Artificial Intelligence 101(1-2)	DOI	Ch. 4 Statistical learning 1990–2011	Reinforcement learning and games
1998	Sutton & Barto	Reinforcement Learning: An Introduction	MIT Press (A Bradford Book), Cambridge,	DOI	Ch. 4 Statistical learning 1990–2011	Reinforcement learning and games
1998-11	LeCun et al.	Gradient-based learning applied to document recognition	Proceedings of the IEEE, 86(11)	DOI	Ch. 4 Statistical learning 1990–2011	Vision and generative models
1999	Sutton et al.	Policy Gradient Methods for Reinforcement Learning with Function Approximation	Advances in Neural Information Processin	URL	Ch. 4 Statistical learning 1990–2011	Reinforcement learning and games
2000	Hinton	Training Products of Experts by Minimizing Contrastive Divergence	Neural Computation, 14(8)	DOI	Ch. 4 Statistical learning 1990–2011	Neural network learning
2001-06-28	Lafferty et al.	Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data	Proceedings of the 18th International Co	URL	Ch. 4 Statistical learning 1990–2011	Language and speech
2001-10	Breiman	Random Forests	Machine Learning, 45(1)	DOI	Ch. 4 Statistical learning 1990–2011	Probability, statistics and origins
2002-01	Campbell et al.	Deep Blue	Artificial Intelligence, 134(1–2)	DOI	Ch. 4 Statistical learning 1990–2011	Search, planning and knowledge
2003-01	Blei et al.	Latent Dirichlet Allocation	Journal of Machine Learning Research, 3	DOI	Ch. 4 Statistical learning 1990–2011	Probability, statistics and origins
2003-02	Bengio et al.	A Neural Probabilistic Language Model	Journal of Machine Learning Research, 3	URL	Ch. 4 Statistical learning 1990–2011	Language and speech
2006-05-29	Coulom	Efficient Selectivity and Backup Operators in Monte-Carlo Tree Search	Computers and Games (CG 2006), Turin; Le	DOI	Ch. 4 Statistical learning 1990–2011	Search, planning and knowledge
2006-07	Hinton et al.	A Fast Learning Algorithm for Deep Belief Nets	Neural Computation, 18(7)	DOI	Ch. 4 Statistical learning 1990–2011	Neural network learning
2006-07-28	Hinton & Salakhutdinov	Reducing the Dimensionality of Data with Neural Networks	Science, 313(5786)	DOI	Ch. 4 Statistical learning 1990–2011	Neural network learning
2006-09-18	Kocsis & Szepesvári	Bandit Based Monte-Carlo Planning	Machine Learning: ECML 2006, Lecture Not	DOI	Ch. 4 Statistical learning 1990–2011	Search, planning and knowledge
2006-09-25	Thrun et al.	Stanley: The robot that won the DARPA Grand Challenge	Journal of Field Robotics 23(9)	DOI	Ch. 4 Statistical learning 1990–2011	Search, planning and knowledge
2006-12	Bengio et al.	Greedy Layer-Wise Training of Deep Networks	Advances in Neural Information Processin	DOI	Ch. 4 Statistical learning 1990–2011	Neural network learning
2007-07-19	Schaeffer et al.	Checkers Is Solved	Science 317(5844)	DOI	Ch. 4 Statistical learning 1990–2011	Search, planning and knowledge
2009-06	Deng et al.	ImageNet: A large-scale hierarchical image database	2009 IEEE Conference on Computer Vision	DOI	Ch. 4 Statistical learning 1990–2011	Vision and generative models
2009-06-14	Raina et al.	Large-scale deep unsupervised learning using graphics processors	Proceedings of the 26th Annual Internati	DOI	Ch. 4 Statistical learning 1990–2011	Neural network learning
2010-03-01	Ciresan et al.	Deep, Big, Simple Neural Nets for Handwritten Digit Recognition	arXiv:1003.0358 [cs.NE]; published in Ne	DOI	Ch. 4 Statistical learning 1990–2011	Vision and generative models
2010-05	Glorot & Bengio	Understanding the difficulty of training deep feedforward neural networks	Proceedings of the Thirteenth Internatio	URL	Ch. 4 Statistical learning 1990–2011	Neural network learning
2010-06	Nair & Hinton	Rectified Linear Units Improve Restricted Boltzmann Machines	Proceedings of the 27th International Co	URL	Ch. 4 Statistical learning 1990–2011	Neural network learning
2010-09-26	Mikolov et al.	Recurrent neural network based language model	Interspeech 2010 (11th Annual Conference	DOI	Ch. 4 Statistical learning 1990–2011	Language and speech
2011-03-02	Collobert et al.	Natural Language Processing (almost) from Scratch	Journal of Machine Learning Research 12(	URL	Ch. 4 Statistical learning 1990–2011	Language and speech
2011-12-29	Le et al.	Building high-level features using large scale unsupervised learning	Proceedings of the 29th International Co	URL	Ch. 5 Deep learning 2012–17	Neural network learning
2012-02-13	Ciresan et al.	Multi-column deep neural networks for image classification	2012 IEEE Conference on Computer Vision	DOI	Ch. 5 Deep learning 2012–17	Vision and generative models
2012-11	Hinton et al.	Deep Neural Networks for Acoustic Modeling in Speech Recognition	IEEE Signal Processing Magazine 29(6)	DOI	Ch. 5 Deep learning 2012–17	Language and speech
2012-12	Krizhevsky et al.	ImageNet Classification with Deep Convolutional Neural Networks	Advances in Neural Information Processin	URL	Ch. 5 Deep learning 2012–17	Vision and generative models
2013-01-16	Mikolov et al.	Efficient Estimation of Word Representations in Vector Space	arXiv:1301.3781 [cs.CL]	arXiv	Ch. 5 Deep learning 2012–17	Language and speech
2013-08-04	Graves	Generating Sequences With Recurrent Neural Networks	arXiv:1308.0850 [cs.NE]	arXiv	Ch. 5 Deep learning 2012–17	Vision and generative models
2013-10-16	Mikolov et al.	Distributed Representations of Words and Phrases and their Compositionality	arXiv:1310.4546 [cs.CL]	arXiv	Ch. 5 Deep learning 2012–17	Language and speech
2013-11-11	Girshick et al.	Rich feature hierarchies for accurate object detection and semantic segmentation	IEEE Conference on Computer Vision and P	DOI	Ch. 5 Deep learning 2012–17	Vision and generative models
2013-12-20	Kingma & Welling	Auto-Encoding Variational Bayes	arXiv:1312.6114 [stat.ML]	arXiv	Ch. 5 Deep learning 2012–17	Vision and generative models
2013-12-19	Mnih et al.	Playing Atari with Deep Reinforcement Learning	arXiv:1312.5602 [cs.LG]; NIPS Deep Learn	arXiv	Ch. 5 Deep learning 2012–17	Reinforcement learning and games
2013-12-21	Szegedy et al.	Intriguing properties of neural networks	arXiv:1312.6199 [cs.CV]	arXiv	Ch. 5 Deep learning 2012–17	Scale, alignment and safety
2014-04-30	Schmidhuber	Deep learning in neural networks: An overview	Neural Networks 61, 85–117 (journal); re	DOI	Ch. 5 Deep learning 2012–17	Probability, statistics and origins
2014-06-03	Cho et al.	Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation	Conference on Empirical Methods in Natur	arXiv	Ch. 5 Deep learning 2012–17	Language and speech
2014-06-10	Goodfellow et al.	Generative Adversarial Nets	arXiv:1406.2661 [stat.ML]	arXiv	Ch. 5 Deep learning 2012–17	Vision and generative models
2014-06	Srivastava et al.	Dropout: A Simple Way to Prevent Neural Networks from Overfitting	Journal of Machine Learning Research 15(	URL	Ch. 5 Deep learning 2012–17	Neural network learning
2014-09-01	Bahdanau et al.	Neural Machine Translation by Jointly Learning to Align and Translate	International Conference on Learning Rep	arXiv	Ch. 5 Deep learning 2012–17	Language and speech
2014-09-04	Simonyan & Zisserman	Very Deep Convolutional Networks for Large-Scale Image Recognition	International Conference on Learning Rep	arXiv	Ch. 5 Deep learning 2012–17	Vision and generative models
2014-09-10	Sutskever et al.	Sequence to Sequence Learning with Neural Networks	arXiv:1409.3215 [cs.CL]; Advances in Neu	arXiv	Ch. 5 Deep learning 2012–17	Language and speech
2014-09-17	Szegedy et al.	Going Deeper with Convolutions	arXiv:1409.4842 [cs.CV]; later published	arXiv	Ch. 5 Deep learning 2012–17	Vision and generative models
2014-10-20	Graves et al.	Neural Turing Machines	arXiv:1410.5401 [cs.NE]	arXiv	Ch. 5 Deep learning 2012–17	Neural network learning
2014-10	Pennington et al.	GloVe: Global Vectors for Word Representation	Proceedings of the 2014 Conference on Em	DOI	Ch. 5 Deep learning 2012–17	Language and speech
2014-10-15	Weston et al.	Memory Networks	International Conference on Learning Rep	arXiv	Ch. 5 Deep learning 2012–17	Neural network learning
2014-12-22	Kingma & Ba	Adam: A Method for Stochastic Optimization	3rd International Conference on Learning	arXiv	Ch. 5 Deep learning 2012–17	Neural network learning
2015-02-11	Ioffe & Szegedy	Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift	arXiv:1502.03167 [cs.LG]; ICML 2015	arXiv	Ch. 5 Deep learning 2012–17	Neural network learning
2015-02-25	Mnih et al.	Human-level control through deep reinforcement learning	Nature 518(7540), Letter, doi:10.1038/na	DOI	Ch. 5 Deep learning 2012–17	Reinforcement learning and games
2015-02-19	Schulman et al.	Trust Region Policy Optimization	ICML 2015, Lille, France, JMLR W&CP vol.	arXiv	Ch. 5 Deep learning 2012–17	Reinforcement learning and games
2015-03-09	Hinton et al.	Distilling the Knowledge in a Neural Network	arXiv:1503.02531 [stat.ML]; NIPS 2014 De	arXiv	Ch. 5 Deep learning 2012–17	Neural network learning
2015-03-12	Sohl-Dickstein et al.	Deep Unsupervised Learning using Nonequilibrium Thermodynamics	ICML 2015, Lille, France, JMLR W&CP vol.	arXiv	Ch. 5 Deep learning 2012–17	Vision and generative models
2015-05-27	LeCun et al.	Deep learning	Nature 521(7553), Review, doi:10.1038/na	DOI	Ch. 5 Deep learning 2012–17	Probability, statistics and origins
2015-08-17	Luong et al.	Effective Approaches to Attention-based Neural Machine Translation	arXiv:1508.04025 [cs.CL]; published in P	arXiv	Ch. 5 Deep learning 2012–17	Language and speech
2015-08-31	Sennrich et al.	Neural Machine Translation of Rare Words with Subword Units	arXiv:1508.07909 [cs.CL]; Proceedings of	DOI	Ch. 5 Deep learning 2012–17	Language and speech
2015-09-09	Lillicrap et al.	Continuous control with deep reinforcement learning	ICLR 2016 (conference paper); arXiv:1509	arXiv	Ch. 5 Deep learning 2012–17	Reinforcement learning and games
2015-11-19	Radford et al.	Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks	arXiv:1511.06434 [cs.LG]; submitted to I	arXiv	Ch. 5 Deep learning 2012–17	Vision and generative models
2015-12-10	He et al.	Deep Residual Learning for Image Recognition	arXiv:1512.03385 [cs.CV], technical repo	arXiv	Ch. 5 Deep learning 2012–17	Vision and generative models
2016-01-27	Silver et al.	Mastering the game of Go with deep neural networks and tree search	Nature 529(7587)	DOI	Ch. 5 Deep learning 2012–17	Reinforcement learning and games
2016-02-04	Mnih et al.	Asynchronous Methods for Deep Reinforcement Learning	Proceedings of the 33rd International Co	arXiv	Ch. 5 Deep learning 2012–17	Reinforcement learning and games
2016-06-21	Amodei et al.	Concrete Problems in AI Safety	arXiv preprint arXiv:1606.06565 [cs.AI]	arXiv	Ch. 5 Deep learning 2012–17	Scale, alignment and safety
2016-07-21	Ba et al.	Layer Normalization	arXiv preprint arXiv:1607.06450 [stat.ML	arXiv	Ch. 5 Deep learning 2012–17	Neural network learning
2016-09-12	van den Oord et al.	WaveNet: A Generative Model for Raw Audio	arXiv preprint arXiv:1609.03499 [cs.SD]	arXiv	Ch. 5 Deep learning 2012–17	Vision and generative models
2016-09-26	Wu et al.	Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation	arXiv preprint arXiv:1609.08144 [cs.CL]	arXiv	Ch. 5 Deep learning 2012–17	Language and speech
2017-01-23	Shazeer et al.	Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer	arXiv preprint arXiv:1701.06538 (cs.LG);	arXiv	Ch. 5 Deep learning 2012–17	Neural network learning
2017-04-16	Jouppi et al.	In-Datacenter Performance Analysis of a Tensor Processing Unit	arXiv:1704.04760 [cs.AR]; published in P	DOI	Ch. 5 Deep learning 2012–17	Neural network learning
2017-06-12	Christiano et al.	Deep reinforcement learning from human preferences	Advances in Neural Information Processin	arXiv	Ch. 5 Deep learning 2012–17	Scale, alignment and safety
2017-06-12	Vaswani et al.	Attention Is All You Need	Advances in Neural Information Processin	arXiv	Ch. 5 Deep learning 2012–17	Language and speech
2017-07-20	Schulman et al.	Proximal Policy Optimization Algorithms	arXiv preprint arXiv:1707.06347 (cs.LG),	arXiv	Ch. 5 Deep learning 2012–17	Reinforcement learning and games
2017-10-19	Silver et al.	Mastering the game of Go without human knowledge	Nature 550(7676)	DOI	Ch. 5 Deep learning 2012–17	Reinforcement learning and games
2017-12-01	Hestness et al.	Deep Learning Scaling is Predictable, Empirically	arXiv:1712.00409 [cs.LG] (Baidu Research	arXiv	Ch. 6 Pretraining and generation 2017–22	Scale, alignment and safety
2018-01-18	Howard & Ruder	Universal Language Model Fine-tuning for Text Classification	Proceedings of the 56th Annual Meeting o	DOI	Ch. 6 Pretraining and generation 2017–22	Language and speech
2018-02-15	Peters et al.	Deep contextualized word representations	Proceedings of NAACL-HLT 2018, Volume 1	DOI	Ch. 6 Pretraining and generation 2017–22	Language and speech
2018-06-11	Radford et al.	Improving Language Understanding by Generative Pre-Training	OpenAI technical report (unpublished pre	URL	Ch. 6 Pretraining and generation 2017–22	Language and speech
2018-10-11	Devlin et al.	BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding	Proceedings of NAACL-HLT 2019, Volume 1	arXiv	Ch. 6 Pretraining and generation 2017–22	Language and speech
2018-12-07	Silver et al.	A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play	Science 362(6419)	DOI	Ch. 6 Pretraining and generation 2017–22	Reinforcement learning and games
2019-02-14	Radford et al.	Language Models are Unsupervised Multitask Learners	OpenAI technical report (not published i	URL	Ch. 6 Pretraining and generation 2017–22	Language and speech
2019-10-23	Raffel et al.	Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer	Journal of Machine Learning Research 21(	arXiv	Ch. 6 Pretraining and generation 2017–22	Language and speech
2019-11-19	Schrittwieser et al.	Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model	arXiv preprint (cs.LG); later Nature 588	DOI	Ch. 6 Pretraining and generation 2017–22	Reinforcement learning and games
2020-01-23	Kaplan et al.	Scaling Laws for Neural Language Models	arXiv:2001.08361 [cs.LG] (preprint; sing	arXiv	Ch. 6 Pretraining and generation 2017–22	Scale, alignment and safety
2020-05-28	Brown et al.	Language Models are Few-Shot Learners	arXiv preprint (cs.CL); later Advances i	arXiv	Ch. 6 Pretraining and generation 2017–22	Language and speech
2020-05-22	Lewis et al.	Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks	arXiv:2005.11401 [cs.CL]; published in A	arXiv	Ch. 6 Pretraining and generation 2017–22	Language and speech
2020-09-02	Stiennon et al.	Learning to summarize from human feedback	Advances in Neural Information Processin	arXiv	Ch. 6 Pretraining and generation 2017–22	Scale, alignment and safety
2020-10-22	Dosovitskiy et al.	An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale	International Conference on Learning Rep	arXiv	Ch. 6 Pretraining and generation 2017–22	Vision and generative models
2021-01-11	Fedus et al.	Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity	Journal of Machine Learning Research 23(	arXiv	Ch. 6 Pretraining and generation 2017–22	Scale, alignment and safety
2021-02-26	Radford et al.	Learning Transferable Visual Models From Natural Language Supervision	Proceedings of the 38th International Co	arXiv	Ch. 6 Pretraining and generation 2017–22	Vision and generative models
2021-03-03	Bender et al.	On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?	Proceedings of the 2021 ACM Conference o	DOI	Ch. 6 Pretraining and generation 2017–22	Scale, alignment and safety
2021-07-07	Chen et al.	Evaluating Large Language Models Trained on Code	arXiv:2107.03374 [cs.LG] (OpenAI preprin	arXiv	Ch. 6 Pretraining and generation 2017–22	Language and speech
2021-07-15	Jumper et al.	Highly accurate protein structure prediction with AlphaFold	Nature 596(7873)	DOI	Ch. 6 Pretraining and generation 2017–22	Probability, statistics and origins
2021-08-16	Bommasani et al.	On the Opportunities and Risks of Foundation Models	arXiv:2108.07258 [cs.LG]; report of the	arXiv	Ch. 6 Pretraining and generation 2017–22	Probability, statistics and origins
2021-12-17	Nakano et al.	WebGPT: Browser-assisted question-answering with human feedback	arXiv preprint (cs.CL)	arXiv	Ch. 7 Agents 2022–	Agents
2022-01-28	Wei et al.	Chain-of-Thought Prompting Elicits Reasoning in Large Language Models	Advances in Neural Information Processin	arXiv	Ch. 6 Pretraining and generation 2017–22	Language and speech
2022-03-29	Hoffmann et al.	Training Compute-Optimal Large Language Models	arXiv preprint (cs.CL); later Advances i	DOI	Ch. 6 Pretraining and generation 2017–22	Scale, alignment and safety
2022-03-04	Ouyang et al.	Training language models to follow instructions with human feedback	arXiv preprint (cs.CL); later Advances i	arXiv	Ch. 6 Pretraining and generation 2017–22	Scale, alignment and safety
2022-03-21	Wang et al.	Self-Consistency Improves Chain of Thought Reasoning in Language Models	International Conference on Learning Rep	arXiv	Ch. 6 Pretraining and generation 2017–22	Language and speech
2022-04-04	Ahn et al.	Do As I Can, Not As I Say: Grounding Language in Robotic Affordances	arXiv:2204.01691 [cs.RO]; published in P	arXiv	Ch. 7 Agents 2022–	Agents
2022-04-05	Chowdhery et al.	PaLM: Scaling Language Modeling with Pathways	arXiv:2204.02311 [cs.CL]; published in J	arXiv	Ch. 6 Pretraining and generation 2017–22	Scale, alignment and safety
2022-05-24	Kojima et al.	Large Language Models are Zero-Shot Reasoners	Advances in Neural Information Processin	DOI	Ch. 6 Pretraining and generation 2017–22	Language and speech
2022-06-15	Wei et al.	Emergent Abilities of Large Language Models	arXiv:2206.07682 [cs.CL]; published in T	arXiv	Ch. 6 Pretraining and generation 2017–22	Scale, alignment and safety
2022-07-12	Huang et al.	Inner Monologue: Embodied Reasoning through Planning with Language Models	arXiv preprint (cs.RO); later PMLR 205 (	arXiv	Ch. 7 Agents 2022–	Agents
2022-10-06	Yao et al.	ReAct: Synergizing Reasoning and Acting in Language Models	International Conference on Learning Rep	arXiv	Ch. 7 Agents 2022–	Agents
2023-02-23	Greshake et al.	Not What You've Signed Up For: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection	arXiv preprint (v2); later Proceedings o	arXiv	Ch. 7 Agents 2022–	Agents
2023-02-09	Schick et al.	Toolformer: Language Models Can Teach Themselves to Use Tools	arXiv preprint (v1); later Advances in N	DOI	Ch. 7 Agents 2022–	Agents
2023-02-27	Touvron et al.	LLaMA: Open and Efficient Foundation Language Models	arXiv:2302.13971 [cs.CL] (Meta AI prepri	arXiv	Ch. 6 Pretraining and generation 2017–22	Language and speech
2023-03-15	OpenAI	GPT-4 Technical Report	arXiv:2303.08774 [cs.CL] (OpenAI technic	arXiv	Ch. 6 Pretraining and generation 2017–22	Language and speech
2023-03-30	Shen et al.	HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face	Advances in Neural Information Processin	DOI	Ch. 7 Agents 2022–	Agents
2023-03-20	Shinn et al.	Reflexion: Language Agents with Verbal Reinforcement Learning	arXiv preprint (v4); later Advances in N	DOI	Ch. 7 Agents 2022–	Agents
2023-04-07	Park et al.	Generative Agents: Interactive Simulacra of Human Behavior	Proceedings of the 36th Annual ACM Sympo	DOI	Ch. 7 Agents 2022–	Agents
2023-05-25	Wang et al.	Voyager: An Open-Ended Embodied Agent with Large Language Models	arXiv preprint (v2); later Transactions	arXiv	Ch. 7 Agents 2022–	Agents
2023-05-17	Yao et al.	Tree of Thoughts: Deliberate Problem Solving with Large Language Models	arXiv:2305.10601 [cs.CL]; published in A	DOI	Ch. 7 Agents 2022–	Agents
2023-07-25	Zhou et al.	WebArena: A Realistic Web Environment for Building Autonomous Agents	arXiv:2307.13854 [cs.AI]; published at t	arXiv	Ch. 7 Agents 2022–	Agents
2023-08-01	Hong et al.	MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework	International Conference on Learning Rep	arXiv	Ch. 7 Agents 2022–	Agents
2023-08-07	Liu et al.	AgentBench: Evaluating LLMs as Agents	International Conference on Learning Rep	arXiv	Ch. 7 Agents 2022–	Agents
2023-10-10	Jimenez et al.	SWE-bench: Can Language Models Resolve Real-World GitHub Issues?	International Conference on Learning Rep	arXiv	Ch. 7 Agents 2022–	Agents
2023-10-12	Packer et al.	MemGPT: Towards LLMs as Operating Systems	arXiv preprint (cs.AI)	arXiv	Ch. 7 Agents 2022–	Agents
2023-11-21	Mialon et al.	GAIA: a benchmark for General AI Assistants	arXiv preprint (v1); later International	arXiv	Ch. 7 Agents 2022–	Agents
2024-04-11	Xie et al.	OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments	arXiv:2404.07972 [cs.AI]; published in A	DOI	Ch. 7 Agents 2022–	Agents
2024-05-06	Yang et al.	SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering	arXiv:2405.15793 [cs.SE]; published in A	DOI	Ch. 7 Agents 2022–	Agents
2024-06-17	Yao et al.	τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains	arXiv:2406.12045 [cs.AI] (preprint; PDF	arXiv	Ch. 7 Agents 2022–	Agents
2024-07-01	Kapoor et al.	AI Agents That Matter	arXiv:2407.01502 [cs.LG]; published in T	arXiv	Ch. 7 Agents 2022–	Agents
2025-01-22	DeepSeek-AI	DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning	arXiv:2501.12948 [cs.CL]; journal versio	DOI	Ch. 7 Agents 2022–	Agents