Building machines that learn and think like people
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Joshua B. Tenenbaum | Brenden M. Lake | Tomer D. Ullman | Samuel Gershman | J. Tenenbaum | B. Lake | T. Ullman | S. Gershman
[1] J. Means. Charts of the Atmosphere , 1911 .
[2] L. L. Thurstone,et al. The learning curve equation , 1919 .
[3] Z. Harris,et al. Foundations of language , 1941 .
[4] H. Harlow,et al. The formation of learning sets. , 1949, Psychological review.
[5] H. Harlow. Learning and satiation of response in intrinsically motivated complex puzzle performance by monkeys. , 1950, Journal of comparative and physiological psychology.
[6] Harlow Hf. Learning and satiation of response in intrinsically motivated complex puzzle performance by monkeys. , 1950 .
[7] F ROSENBLATT,et al. The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.
[8] Kenneth N. Stevens,et al. Speech recognition: A model and a program for research , 1962, IRE Trans. Inf. Theory.
[9] Murray Eden,et al. Handwriting and pattern recognition , 1962, IRE Trans. Inf. Theory.
[10] D. Berlyne. Curiosity and exploration. , 1966, Science.
[11] A M Liberman,et al. Perception of the speech code. , 1967, Psychological review.
[12] Marvin Minsky,et al. Perceptrons: An Introduction to Computational Geometry , 1969 .
[13] Patrick Henry Winston,et al. Learning structural descriptions from examples , 1970 .
[14] Roger C. Schank,et al. Conceptual dependency: A theory of natural language understanding , 1972 .
[15] Terry Winograd,et al. Understanding natural language , 1974 .
[16] Allen Newell,et al. Human Problem Solving. , 1973 .
[17] Marvin Minsky,et al. A framework for representing knowledge , 1974 .
[18] L. Rips. Inductive judgments about natural categories. , 1975 .
[19] G. Miller,et al. Language and Perception , 1976 .
[20] Daniel G. Bobrow,et al. On Overview of KRL, a Knowledge Representation Language , 1976, Cogn. Sci..
[21] Susan Carey,et al. Acquiring a Single New Word , 1978 .
[22] D. Marr,et al. Representation and recognition of the spatial organization of three-dimensional shapes , 1978, Proceedings of the Royal Society of London. Series B. Biological Sciences.
[23] S. Carey. The child as word learner , 1978 .
[24] Barbara Hayes-Roth,et al. A Cognitive Model of Planning , 1979, Cogn. Sci..
[25] R. Passingham. The hippocampus as a cognitive map J. O'Keefe & L. Nadel, Oxford University Press, Oxford (1978). 570 pp., £25.00 , 1979, Neuroscience.
[26] Edward E. Smith,et al. On the adequacy of prototype theory as a theory of concepts , 1981, Cognition.
[27] G. Miller,et al. Linguistic theory and psychological reality , 1982 .
[28] Kunihiko Fukushima,et al. Neocognitron: A Self-Organizing Neural Network Model for a Mechanism of Visual Pattern Recognition , 1982 .
[29] E. Bienenstock,et al. Theory for the development of neuron selectivity: orientation specificity and binocular interaction in visual cortex , 1982, The Journal of neuroscience : the official journal of the Society for Neuroscience.
[30] J. Freyd,et al. Representing the dynamics of a static form , 1983, Memory & cognition.
[31] L. Barsalou,et al. Ad hoc categories , 1983, Memory & cognition.
[32] B. Tversky,et al. Journal of Experimental Psychology : General VOL . 113 , No . 2 JUNE 1984 Objects , Parts , and Categories , 2005 .
[33] Donald D. Hoffman,et al. Parts of recognition , 1984, Cognition.
[34] John R. Anderson,et al. Machine learning - an artificial intelligence approach , 1982, Symbolic computation.
[35] D. Medin,et al. The role of theories in conceptual coherence. , 1985, Psychological review.
[36] D. Hofstadter. Metamagical Themas: Questing for the Essence of Mind and Pattern , 1985 .
[37] R. Racine,et al. The effects of repeated induction of long-term potentiation in the dentate gyrus , 1985, Brain Research.
[38] Geoffrey E. Hinton,et al. Learning representations by back-propagating errors , 1986, Nature.
[39] James L. McClelland,et al. Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .
[40] Earl B. Hunt,et al. Machine learning: An artificial intelligence approach (vol. 2): R. S. Michalski, J. G. Carbonell, and T. M. Mitchell (Eds.). Los Alton, CA: Morgan Kaufmann, 1986. Pp. x + 738. $39.95 , 1987 .
[41] J. Freyd. Dynamic mental representations. , 1987, Psychological review.
[42] A.A. Mullin,et al. Metamagical themas: Questing for the essence of mind and patterns , 1987, Proceedings of the IEEE.
[43] I. Biederman. Recognition-by-components: a theory of human image understanding. , 1987, Psychological review.
[44] Allen Newell,et al. GPS, a program that simulates human thought , 1995 .
[45] S. Pinker,et al. On language and connectionism: Analysis of a parallel distributed processing model of language acquisition , 1988, Cognition.
[46] James L. McClelland. Parallel Distributed Processing: Implications for Cognition and Development , 1988 .
[47] Linda B. Smith,et al. The importance of shape in early lexical learning , 1988 .
[48] Gregory L. Murphy,et al. Comprehending Complex Concepts , 1988, Cogn. Sci..
[49] Martin Stacey,et al. Scientific Discovery: Computational Explorations of the Creative Processes , 1988 .
[50] J. Fodor,et al. Connectionism and cognitive architecture: A critical analysis , 1988, Cognition.
[51] S. Brison. The Intentional Stance , 1989 .
[52] Francis Crick,et al. The recent excitement about neural networks , 1989, Nature.
[53] E. Markman. Categorization and naming in children , 1989 .
[54] Lawrence D. Jackel,et al. Backpropagation Applied to Handwritten Zip Code Recognition , 1989, Neural Computation.
[55] Elizabeth S. Spelke,et al. Principles of Object Perception , 1990, Cogn. Sci..
[56] Michael I. Jordan,et al. Advances in Neural Information Processing Systems 30 , 1995 .
[57] Richard S. Sutton,et al. Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming , 1990, ML.
[58] A. M. Turing,et al. Computing Machinery and Intelligence , 1950, The Philosophy of Artificial Intelligence.
[59] G. Reeke. The society of mind , 1991 .
[60] Biing-Hwang Juang,et al. Hidden Markov Models for Speech Recognition , 1991 .
[61] E. Capaldi,et al. The organization of behavior. , 1992, Journal of applied behavior analysis.
[62] H. Wellman,et al. Cognitive development: foundational theories of core domains. , 1992, Annual review of psychology.
[63] I. Biederman,et al. Dynamic binding in a neural network for shape recognition. , 1992, Psychological review.
[64] Elie Bienenstock,et al. Neural Networks and the Bias/Variance Dilemma , 1992, Neural Computation.
[65] John R. Koza,et al. Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.
[66] Joel L. Davis,et al. A Model of How the Basal Ganglia Generate and Use Neural Signals That Predict Reinforcement , 1994 .
[67] T. B. Ward. Structured Imagination: the Role of Category Structure in Exemplar Generation , 1994, Cognitive Psychology.
[68] G. Kane. Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol 1: Foundations, vol 2: Psychological and Biological Models , 1994 .
[69] B. Ross,et al. Predictions From Uncertain Categorizations , 1994, Cognitive Psychology.
[70] Massimo Buscema,et al. Self-reflexive networks: Theory · topology · applications , 1995 .
[71] James L. McClelland,et al. Why there are complementary learning systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory. , 1995, Psychological review.
[72] Peter Norvig,et al. Artificial Intelligence: A Modern Approach , 1995 .
[73] Geoffrey E. Hinton,et al. The Helmholtz Machine , 1995, Neural Computation.
[74] Geoffrey E. Hinton,et al. The "wake-sleep" algorithm for unsupervised neural networks. , 1995, Science.
[75] M. Goodale,et al. The visual brain in action , 1995 .
[76] Elie Bienenstock,et al. Compositionality, MDL Priors, and Object Recognition , 1996, NIPS.
[77] Peter Dayan,et al. A Neural Substrate of Prediction and Reward , 1997, Science.
[78] J. Disterhoft,et al. Enhanced synaptic transmission in CA1 hippocampus after eyeblink conditioning. , 1997, Journal of neurophysiology.
[79] A. Premack,et al. Infants Attribute Value to the Goal-Directed Actions of Self-propelled Objects , 1997, Journal of Cognitive Neuroscience.
[80] Kenneth D. Forbus,et al. Qualitative Mental Models: Simulations or Memories? , 1997 .
[81] A. Gopnik,et al. Words, thoughts, and theories , 1997 .
[82] R. Lewin,et al. MASTERING THE GAME , 1998 .
[83] G. Marcus. Rethinking Eliminative Connectionism , 1998, Cognitive Psychology.
[84] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[85] D. Wolpert,et al. Internal models in the cerebellum , 1998, Trends in Cognitive Sciences.
[86] C. Harley. Noradrenergic long-term potentiation in the dentate gyrus. , 1998, Advances in pharmacology.
[87] Donald Favareau. The Symbolic Species: The Co-evolution of Language and the Brain , 1998 .
[88] R. Siegler,et al. Developmental Differences in Rule Learning: A Microgenetic Analysis , 1998, Cognitive Psychology.
[89] H. Wellman,et al. Knowledge acquisition in foundational domains. , 1998 .
[90] A. Markman,et al. Referential communication and category acquisition. , 1998, Journal of experimental psychology. General.
[91] S. Carey,et al. Whose gaze will infants follow? The elicitation of gaze-following in 12-month-olds , 1998 .
[92] D. Medin,et al. Concepts do more than categorize , 1999, Trends in Cognitive Sciences.
[93] Kenji Doya,et al. What are the computations of the cerebellum, the basal ganglia and the cerebral cortex? , 1999, Neural Networks.
[94] N. Chater,et al. Computational models and Rethinking innateness , 1999, Journal of Child Language.
[95] T. Shultz,et al. The Developmental Course of Distance, Time, and Velocity Concepts:A Generative Connectionist Model , 2000 .
[96] Jonathan Baxter,et al. A Model of Inductive Bias Learning , 2000, J. Artif. Intell. Res..
[97] Paul A. Viola,et al. Learning from one example through shared densities on transforms , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).
[98] Patrice D. Tremoulet,et al. Perception of Animacy from the Motion of a Single Object , 2000, Perception.
[99] E. Deci,et al. Intrinsic and Extrinsic Motivations: Classic Definitions and New Directions. , 2000, Contemporary educational psychology.
[100] R. Hastie,et al. Causal knowledge and categories: the effects of causal beliefs on categorization, induction, and similarity. , 2001, Journal of experimental psychology. General.
[101] Robert L. Goldstone,et al. Letter spirit (part two): modeling creativity in a visual domain , 2001 .
[102] G. Marcus. The Algebraic Mind: Integrating Connectionism and Cognitive Science , 2001 .
[103] Refractor. Vision , 2000, The Lancet.
[104] G. Bi,et al. Synaptic modification by correlated activity: Hebb's postulate revisited. , 2001, Annual review of neuroscience.
[105] Leonid Rozenblit,et al. The misunderstood limits of folk science: an illusion of explanatory depth , 2002, Cogn. Sci..
[106] Linda B. Smith,et al. Object name Learning Provides On-the-Job Training for Attention , 2002, Psychological science.
[107] Stephen Johnson,et al. Development of object perception , 2002 .
[108] Stephen Jose Hanson,et al. On the Emergence of Rules in Neural Networks , 2002, Neural Computation.
[109] Noam Chomsky,et al. The faculty of language: what is it, who has it, and how did it evolve? , 2002 .
[110] Z. Harris,et al. Foundations of Language , 1940 .
[111] Geoffrey E. Hinton. Training Products of Experts by Minimizing Contrastive Divergence , 2002, Neural Computation.
[112] A. Alao. Anatomy of the mind. , 2002, Psychiatric services.
[113] Stephen Wolfram,et al. A New Kind of Science , 2003, Artificial Life.
[114] Noam Chomsky,et al. The faculty of language: what is it, who has it, and how did it evolve? , 2002, Science.
[115] A. Markman,et al. Category use and category learning. , 2003, Psychological bulletin.
[116] B. Rehder. A causal-model theory of conceptual representation and categorization. , 2003, Journal of experimental psychology. Learning, memory, and cognition.
[117] Michael L. Anderson. Embodied Cognition: A field guide , 2003, Artif. Intell..
[118] T. Shultz. Computational Developmental Psychology , 2003 .
[119] Susan Goldin-Meadow,et al. What makes us smart? Core knowledge and natural language , 2003 .
[120] S. Gelman,et al. The Essential Child : Origins of Essentialism in Everyday Thought , 2003 .
[121] György Gergely,et al. One-year-old infants use teleological representations of actions productively , 2003, Cogn. Sci..
[122] Peter Dayan,et al. Q-learning , 1992, Machine Learning.
[123] Tom M. Mitchell,et al. Explanation-Based Generalization: A Unifying View , 1986, Machine Learning.
[124] S. Grossberg,et al. Adaptive pattern classification and universal recoding: I. Parallel development and coding of neural feature detectors , 1976, Biological Cybernetics.
[125] Stephen Grossberg,et al. Adaptive pattern classification and universal recoding: II. Feedback, expectation, olfaction, illusions , 1976, Biological Cybernetics.
[126] R. Baillargeon. Infants' Physical World , 2004 .
[127] Kunihiko Fukushima,et al. Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position , 1980, Biological Cybernetics.
[128] S. Carey. The Origin of Concepts , 2000 .
[129] Peter Dayan,et al. Technical Note: Q-Learning , 2004, Machine Learning.
[130] David M. Sobel,et al. A theory of causal learning in children: causal maps and Bayes nets. , 2004, Psychological review.
[131] John R Anderson,et al. An integrated theory of the mind. , 2004, Psychological review.
[132] S. Carey. Bootstrapping & the origin of concepts , 2004, Daedalus.
[133] M. Bouton. Context and behavioral processes in extinction. , 2004, Learning & memory.
[134] H. Levesque,et al. Object-Oriented Representation , 2004 .
[135] Stuart G. Shanker,et al. The nature of insight , 1995, Minds and Machines.
[136] Jonathan D. Cohen,et al. Prefrontal cortex and flexible cognitive control: rules without symbols. , 2005, Proceedings of the National Academy of Sciences of the United States of America.
[137] P. Dayan,et al. Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral control , 2005, Nature Neuroscience.
[138] G. Lakoff,et al. The Brain's concepts: the role of the Sensory-motor system in conceptual knowledge , 2005, Cognitive neuropsychology.
[139] P. Glimcher,et al. Midbrain Dopamine Neurons Encode a Quantitative Reward Prediction Error Signal , 2005, Neuron.
[140] Tamar Flash,et al. Motor primitives in vertebrates and invertebrates , 2005, Current Opinion in Neurobiology.
[141] J. Elman. Connectionist models of cognitive development: where next? , 2005, Trends in Cognitive Sciences.
[142] A. Graybiel. The basal ganglia: learning new tricks and loving it , 2005, Current Opinion in Neurobiology.
[143] P. Redgrave,et al. The short-latency dopamine signal: a role in discovering novel actions? , 2006, Nature Reviews Neuroscience.
[144] A. Schlottmann,et al. Perceived physical and social causality in animated motions: spontaneous reports and ratings. , 2006, Acta psychologica.
[145] Richard Granger,et al. Engines of the Brain: The Computational Instruction Set of Human Cognition , 2006, AI Mag..
[146] Yee Whye Teh,et al. A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.
[147] Alain Cardon,et al. Artificial consciousness, artificial emotions, and autonomous robots , 2006, Cognitive Processing.
[148] Adrian Hilton,et al. A survey of advances in vision-based human motion capture and analysis , 2006, Comput. Vis. Image Underst..
[149] Katherine D. Kinzler,et al. Core knowledge. , 2007, Developmental science.
[150] J. Tenenbaum,et al. Word learning as Bayesian inference. , 2007, Psychological review.
[151] C. Glymour,et al. Preschool children learn about causal structure from conditional interventions. , 2007, Developmental science.
[152] D. Wegner,et al. Dimensions of Mind Perception , 2007, Science.
[153] Karl J. Friston,et al. Predictive coding: an account of the mirror neuron system , 2007, Cognitive Processing.
[154] Antonio Torralba,et al. Sharing Visual Features for Multiclass and Multiview Object Detection , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[155] J. Hamlin,et al. Social evaluation by preverbal infants , 2007, Nature.
[156] S. Harkness,et al. Teachers' ethnotheories of the ‘ideal student’ in five western cultures , 2007 .
[157] Thomas L. Griffiths,et al. Modeling the effects of memory on human online sentence processing with particle filters , 2008, NIPS.
[158] Andre Cohen,et al. An object-oriented representation for efficient reinforcement learning , 2008, ICML '08.
[159] A. Antunes. Democracia e Cidadania na Escola: Do Discurso à Prática , 2008 .
[160] G. Csibra. Goal attribution to inanimate agents by 6.5-month-old infants , 2008, Cognition.
[161] R. Baillargeon,et al. An Account of Infants' Physical Reasoning , 2008 .
[162] Emilio Kropff,et al. Place cells, grid cells, and the brain's spatial representation system. , 2008, Annual review of neuroscience.
[163] David Silver,et al. Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence (2008) Achieving Master Level Play in 9 × 9 Computer Go , 2022 .
[164] Larissa K. Samuelson,et al. Fast Mapping but Poor Retention by 24-Month-Old Infants. , 2008, Infancy : the official journal of the International Society on Infant Studies.
[165] Using Qualitative Reasoning for the Attribution of Moral Responsibility , 2008 .
[166] Susan J. Hespos,et al. Young infants’ actions reveal their developing knowledge of support variables: Converging evidence for violation-of-expectation findings , 2008, Cognition.
[167] Francisco Câmara Pereira. Creativity and Artificial Intelligence , 2007 .
[168] Joshua B. Tenenbaum,et al. Church: a language for generative models , 2008, UAI.
[169] Joshua B. Tenenbaum,et al. The acquisition of inductive constraints , 2008 .
[170] M. Tomasello. Origins of human communication , 2008 .
[171] Joshua B. Tenenbaum,et al. Help or Hinder: Bayesian Models of Social Goal Inference , 2009, NIPS.
[172] T. Lombrozo. Explanation and categorization: How “why?” informs “what?” , 2009, Cognition.
[173] Y. Niv. Reinforcement learning in the brain , 2009 .
[174] J. Bach,et al. Principles of Synthetic Intelligence: Psi: An Architecture of Motivated Cognition , 2009 .
[175] Chris L. Baker,et al. Action understanding as inverse planning , 2009, Cognition.
[176] Michael C. Frank,et al. PSYCHOLOGICAL SCIENCE Research Article Using Speakers ’ Referential Intentions to Model Early Cross-Situational Word Learning , 2022 .
[177] Susan J. Hespos,et al. PSYCHOLOGICAL SCIENCE Research Article Five-Month-Old Infants Have Different Expectations for Solids and Liquids , 2022 .
[178] Dima Damen,et al. Computer Vision and Pattern Recognition (CVPR) , 2009 .
[179] Joshua B Tenenbaum,et al. Theory-based causal induction. , 2009, Psychological review.
[180] Masaki Ogino,et al. Cognitive Developmental Robotics: A Survey , 2009, IEEE Transactions on Autonomous Mental Development.
[181] Kenneth D. Forbus,et al. An Integrated Systems Approach to Explanation-Based Conceptual Change , 2010, AAAI.
[182] S. Casper. Book Review: Mind as machine: a history of cognitive science , 2010, Medical History.
[183] Jennie Hill. The Gates of Hell: Sir John Franklin's Tragic Quest for the North West Passage (review) , 2011 .
[184] John K Kruschke,et al. Bayesian data analysis. , 2010, Wiley interdisciplinary reviews. Cognitive science.
[185] Joseph Jay Williams,et al. The role of explanation in discovery and generalization: evidence from category learning , 2010, ICLS.
[186] J. Tenenbaum,et al. Probabilistic models of cognition: exploring representations and inductive biases , 2010, Trends in Cognitive Sciences.
[187] Dedre Gentner,et al. Bootstrapping the Mind: Analogical Processes and Symbol Systems , 2010, Cogn. Sci..
[188] M. Guasti. How Children Learn the Meanings of Words , 2010 .
[189] J. Tenenbaum,et al. Infants consider both the sample and the sampling process in inductive generalization , 2010, Proceedings of the National Academy of Sciences.
[190] J. Hamlin,et al. Three-month-olds show a negativity bias in their social evaluations. , 2010, Developmental science.
[191] James L. McClelland,et al. Letting structure emerge: connectionist and dynamical systems approaches to cognition , 2010, Trends in Cognitive Sciences.
[192] David Poeppel,et al. Analysis by Synthesis: A (Re-)Emerging Program of Research for Language and Vision , 2010, Biolinguistics.
[193] M. Buscema,et al. A new meta-classifier , 2010, 2010 Annual Meeting of the North American Fuzzy Information Processing Society.
[194] Carl H. Berdahl,et al. A neural network model of Borderline Personality Disorder , 2010, Neural Networks.
[195] Mark Steyvers,et al. Using Inverse Planning and Theory of Mind for Social Goal Inference , 2011, CogSci.
[196] Petr Baudis,et al. PACHI: State of the Art Open Source Go Program , 2011, ACG.
[197] Charles Kemp,et al. How to Grow a Mind: Statistics, Structure, and Abstraction , 2011, Science.
[198] Daniel J. Graham,et al. The Packet Switching Brain , 2011, Journal of Cognitive Neuroscience.
[199] Joshua B. Tenenbaum,et al. Learning to share visual appearance for multiclass object detection , 2011, CVPR 2011.
[200] Edward Vul,et al. Pure Reasoning in 12-Month-Old Infants as Probabilistic Inference , 2011, Science.
[201] Wolfgang Maass,et al. Probabilistic Inference in General Graphical Models through Sampling in Stochastic Networks of Spiking Neurons , 2011, PLoS Comput. Biol..
[202] Noah D. Goodman,et al. Where science starts: Spontaneous experiments in preschoolers’ exploratory play , 2011, Cognition.
[203] Noah D. Goodman. Learning and the language of thought , 2011, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops).
[204] Amir Dezfouli,et al. Speed/Accuracy Trade-Off between the Habitual and the Goal-Directed Processes , 2011, PLoS Comput. Biol..
[205] David Silver,et al. Monte-Carlo tree search and rapid action value estimation in computer Go , 2011, Artif. Intell..
[206] D. Knill,et al. Bayesian sampling in visual perception , 2011, Proceedings of the National Academy of Sciences.
[207] Wolfgang Maass,et al. Neural Dynamics as Sampling: A Model for Stochastic Computation in Recurrent Networks of Spiking Neurons , 2011, PLoS Comput. Biol..
[208] Gianluca Baldassarre,et al. What are intrinsic motivations? A biological perspective , 2011, 2011 IEEE International Conference on Development and Learning (ICDL).
[209] Mirko Farina. The Evolved Apprentice , 2012 .
[210] Joshua B. Tenenbaum,et al. Exploiting compositionality to explore a large space of model structures , 2012, UAI.
[211] D. McDermott. LANGUAGE OF THOUGHT , 2012 .
[212] Hosein Hashemi,et al. Fuzzy Clustering of Seismic Sequences: Segmentation of Time-Frequency Representations , 2012, IEEE Signal Processing Magazine.
[213] Joshua B. Tenenbaum,et al. One-Shot Learning with a Hierarchical Nonparametric Bayesian Model , 2011, ICML Unsupervised and Transfer Learning.
[214] Trevor Bekolay,et al. Supplementary Materials for A Large-Scale Model of the Functioning Brain , 2012 .
[215] Jürgen Schmidhuber,et al. Multi-column deep neural networks for image classification , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[216] John P. Cunningham,et al. Neural population dynamics during , 2012 .
[217] Karl J. Friston,et al. Canonical Microcircuits for Predictive Coding , 2012, Neuron.
[218] Joshua B. Tenenbaum,et al. Concept learning as motor program induction: A large-scale empirical study , 2012, CogSci.
[219] Noah D. Goodman,et al. Theory learning as stochastic search in the language of thought , 2012 .
[220] L. Schulz. The origins of inquiry: inductive inference and exploration in early childhood , 2012, Trends in Cognitive Sciences.
[221] Adam N. Sanborn,et al. Bridging Levels of Analysis for Probabilistic Models of Cognition , 2012 .
[222] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[223] Shimon Ullman,et al. From simple innate biases to complex visual concepts , 2012, Proceedings of the National Academy of Sciences.
[224] D. Wegner,et al. Feeling robots and human zombies: Mind perception and the uncanny valley , 2012, Cognition.
[225] Tara N. Sainath,et al. Deep Neural Networks for Acoustic Modeling in Speech Recognition , 2012 .
[226] Joshua B. Tenenbaum,et al. Multistability and Perceptual Inference , 2012, Neural Computation.
[227] S. Mahadevan,et al. Learning Theory , 2001 .
[228] Vincent G. Berthiaume,et al. A constructivist connectionist model of transitions on false-belief tasks , 2013, Cognition.
[229] Alex Graves,et al. Playing Atari with Deep Reinforcement Learning , 2013, ArXiv.
[230] Francesco Mannella,et al. The Hierarchical Organisation of Cortical and Basal-Ganglia Systems: A Computationally-Informed Review and Integrated Hypothesis , 2013, Computational and Robotic Models of the Hierarchical Organization of Behavior.
[231] B. Scholl,et al. Perceiving Animacy and Intentionality , 2013 .
[232] Jessica B. Hamrick,et al. Simulation as an engine of physical scene understanding , 2013, Proceedings of the National Academy of Sciences.
[233] Charles Kemp,et al. A probabilistic account of exemplar and category generation , 2013, Cognitive Psychology.
[234] 中垣 恒太郎. 郊外の未来像 : Do Androids Dream of Electric Sheep?における消費文化・環境正義 , 2013 .
[235] Geoffrey E. Hinton,et al. Speech recognition with deep recurrent neural networks , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.
[236] M. D. Rutherford,et al. Social perception : detection and interpretation of animacy, agency, and intention , 2013 .
[237] Noah D. Goodman,et al. The mentalistic basis of core social cognition: experiments in preverbal infants and a computational model. , 2013, Developmental science.
[238] Owen Macindoe,et al. Sidekick agents for sequential planning problems , 2013 .
[239] Massimo Buscema,et al. Meta Net: A New Meta-Classifier Family , 2013 .
[240] Nitish Srivastava,et al. Discriminative Transfer Learning with Tree-based Priors , 2013, NIPS.
[241] Daniel Tarlow,et al. Learning to Pass Expectation Propagation Messages , 2013, NIPS.
[242] Jeffrey Dean,et al. Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.
[243] Joshua B. Tenenbaum,et al. Learning with Hierarchical-Deep Models , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[244] Alex Graves,et al. Generating Sequences With Recurrent Neural Networks , 2013, ArXiv.
[245] Pierre-Yves Oudeyer,et al. Active learning of inverse models with intrinsically motivated goal exploration in robots , 2013, Robotics Auton. Syst..
[246] J. Hamlin,et al. Moral Judgment and Action in Preverbal Infants and Toddlers , 2013 .
[247] Marco Mirolli,et al. Intrinsically Motivated Learning in Natural and Artificial Systems , 2013 .
[248] Anne G E Collins,et al. Cognitive control over learning: creating, clustering, and generalizing task-set structure. , 2013, Psychological review.
[249] A. Schlottmann,et al. Domain-specific perceptual causality in children depends on the spatio-temporal configuration, not motion onset , 2013, Front. Psychol..
[250] Vikash K. Mansinghka,et al. Reconciling intuitive physics and Newtonian mechanics for colliding objects. , 2013, Psychological review.
[251] Léon Bottou,et al. From machine learning to machine reasoning , 2011, Machine Learning.
[252] Francesco Mannella,et al. Intrinsically motivated action-outcome learning and goal-based action recall: a system-level bio-constrained computational model. , 2013, Neural networks : the official journal of the International Neural Network Society.
[253] P. Dayan,et al. Goals and Habits in the Brain , 2013, Neuron.
[254] Noah D. Goodman,et al. Learning Stochastic Inverses , 2013, NIPS.
[255] C. Gallistel,et al. The neuroscience of learning: beyond the Hebbian synapse. , 2013, Annual review of psychology.
[256] Karol Gregor,et al. Neural Variational Inference and Learning in Belief Networks , 2014, ICML.
[257] Yoshua Bengio,et al. How transferable are features in deep neural networks? , 2014, NIPS.
[258] M. Baily,et al. US Manufacturing: Understanding Its Past and Its Potential Future , 2014 .
[259] Xiang Zhang,et al. OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks , 2013, ICLR.
[260] Joshua B. Tenenbaum,et al. Automatic Construction and Natural-Language Description of Nonparametric Regression Models , 2014, AAAI.
[261] Rob Fergus,et al. Visualizing and Understanding Convolutional Networks , 2013, ECCV.
[262] Ha Hong,et al. Performance-optimized hierarchical models predict neural responses in higher visual cortex , 2014, Proceedings of the National Academy of Sciences.
[263] Thomas L. Griffiths,et al. One and Done? Optimal Decisions From Very Few Samples , 2014, Cogn. Sci..
[264] Daan Wierstra,et al. Stochastic Backpropagation and Approximate Inference in Deep Generative Models , 2014, ICML.
[265] P. Jucevičienė,et al. What does it mean to be smart , 2014 .
[266] C. Eliasmith,et al. The use and abuse of large-scale brain models , 2014, Current Opinion in Neurobiology.
[267] Rajesh P. N. Rao,et al. Neurons as Monte Carlo Samplers: Bayesian Inference and Learning in Spiking Networks , 2014, NIPS.
[268] Max Welling,et al. Semi-supervised Learning with Deep Generative Models , 2014, NIPS.
[269] Joshua B. Tenenbaum,et al. Information Selection in Noisy Environments with Large Action Spaces , 2014, CogSci.
[270] Alex Graves,et al. Recurrent Models of Visual Attention , 2014, NIPS.
[271] Noah D. Goodman,et al. Amortized Inference in Probabilistic Reasoning , 2014, CogSci.
[272] Biing-Hwang Juang,et al. Recurrent deep neural networks for robust speech recognition , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[273] Daniel Cownden,et al. Random feedback weights support learning in deep neural networks , 2014, ArXiv.
[274] S. Denison,et al. Probabilistic models, learning algorithms, and response variability: sampling in cognitive development , 2014, Trends in Cognitive Sciences.
[275] Yura N. Perov,et al. Venture: a higher-order probabilistic programming platform with programmable inference , 2014, ArXiv.
[276] Kevin B. Clark,et al. Basis for a neuronal version of Grover's quantum algorithm , 2014, Front. Mol. Neurosci..
[277] Honglak Lee,et al. Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search Planning , 2014, NIPS.
[278] Trevor Darrell,et al. DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition , 2013, ICML.
[279] A. Markman,et al. Journal of Experimental Psychology : General Retrospective Revaluation in Sequential Decision Making : A Tale of Two Systems , 2012 .
[280] Noah D. Goodman,et al. A rational account of pedagogical reasoning: Teaching by, and learning from, examples , 2014, Cognitive Psychology.
[281] Pushmeet Kohli,et al. Just-In-Time Learning for Fast and Flexible Inference , 2014, NIPS.
[282] Nikolaus Kriegeskorte,et al. Deep Supervised, but Not Unsupervised, Models May Explain IT Cortical Representation , 2014, PLoS Comput. Biol..
[283] B. Lake. Towards more human-like concept learning in machines : compositionality, causality, and learning-to-learn , 2014 .
[284] Daniel J. Graham,et al. Routing in the brain , 2014, Front. Comput. Neurosci..
[285] Andrea Lockerd Thomaz,et al. Robot Learning from Human Teachers , 2014, Robot Learning from Human Teachers.
[286] James R. Glass,et al. One-shot learning of generative speech concepts , 2014, CogSci.
[287] A. Barto,et al. Intrinsic motivations and open-ended development in animals, humans, and robots: an overview , 2014, Front. Psychol..
[288] Susan J. Hespos,et al. Divisions of the physical world: Concepts of objects and substances. , 2015, Psychological bulletin.
[289] Joshua B. Tenenbaum,et al. Humans predict liquid dynamics using probabilistic simulation , 2015, CogSci.
[290] Jürgen Schmidhuber,et al. Deep learning in neural networks: An overview , 2014, Neural Networks.
[291] Sergey Levine,et al. Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models , 2015, ArXiv.
[292] Lourdes Agapito,et al. Part-based modelling of compound scenes from images , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[293] Joshua B. Tenenbaum,et al. Children’s understanding of the costs and rewards underlying rational action , 2015, Cognition.
[294] Rob Fergus,et al. Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks , 2015, NIPS.
[295] Zeb Kurth-Nelson,et al. Model-Based Reasoning in Humans Becomes Automatic with Training , 2015, PLoS Comput. Biol..
[296] Yoshua Bengio,et al. Show, Attend and Tell: Neural Image Caption Generation with Visual Attention , 2015, ICML.
[297] Jason Weston,et al. End-To-End Memory Networks , 2015, NIPS.
[298] Zoubin Ghahramani,et al. Probabilistic machine learning and artificial intelligence , 2015, Nature.
[299] Joshua B. Tenenbaum,et al. Deep Convolutional Inverse Graphics Network , 2015, NIPS.
[300] Nikolaus Kriegeskorte,et al. Deep neural networks: a new framework for modelling biological vision and brain information processing , 2015, bioRxiv.
[301] Jason Weston,et al. Memory Networks , 2014, ICLR.
[302] Aimee E. Stahl,et al. Observing the unexpected enhances infants’ learning and exploration , 2015, Science.
[303] Joshua B. Tenenbaum,et al. Human-level concept learning through probabilistic program induction , 2015, Science.
[304] Wojciech Zaremba,et al. Deep Neural Networks Predict Category Typicality Ratings for Images , 2015, CogSci.
[305] A. Gelman,et al. Stan , 2015 .
[306] A. Clark,et al. Words and the World , 2015 .
[307] Ernest Davis,et al. Commonsense reasoning and commonsense knowledge in artificial intelligence , 2015, Commun. ACM.
[308] Joshua B. Tenenbaum,et al. How, whether, why: Causal judgments as counterfactual contrasts , 2015, CogSci.
[309] Bernhard Schölkopf,et al. Towards a Learning Theory of Cause-Effect Inference , 2015, ICML.
[310] Alex Graves,et al. DRAW: A Recurrent Neural Network For Image Generation , 2015, ICML.
[311] L. Schulz,et al. Constraints on Hypothesis Selection in Causal Learning , 2015 .
[312] L. Schulz,et al. Imagination and the generation of new ideas , 2015 .
[313] Shakir Mohamed,et al. Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning , 2015, NIPS.
[314] Samuel J. Gershman,et al. Computational rationality: A converging paradigm for intelligence in brains, minds, and machines , 2015, Science.
[315] Tai Sing Lee,et al. The Visual System's Internal Model of the World , 2015, Proceedings of the IEEE.
[316] Giacomo Indiveri,et al. Memory and Information Processing in Neuromorphic Systems , 2015, Proceedings of the IEEE.
[317] J. Tenenbaum,et al. Efficient analysis-by-synthesis in vision : A computational framework , behavioral tests , and comparison with neural representations , 2015 .
[318] Charles Kemp,et al. A decision network account of reasoning about other people’s choices , 2015, Cognition.
[319] Samy Bengio,et al. Show and tell: A neural image caption generator , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[320] Shane Legg,et al. Human-level control through deep reinforcement learning , 2015, Nature.
[321] Dumitru Erhan,et al. Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[322] Michael S. Bernstein,et al. ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.
[323] Phil Blunsom,et al. Learning to Transduce with Unbounded Memory , 2015, NIPS.
[324] Gregory R. Koch,et al. Siamese Neural Networks for One-Shot Image Recognition , 2015 .
[325] Timothy O'Donnell,et al. Productivity and Reuse in Language: A Theory of Linguistic Computation and Storage , 2015 .
[326] Marc G. Bellemare,et al. The Arcade Learning Environment: An Evaluation Platform for General Agents (Extended Abstract) , 2012, IJCAI.
[327] Yoshua Bengio,et al. Neural Machine Translation by Jointly Learning to Align and Translate , 2014, ICLR.
[328] Joshua B. Tenenbaum,et al. Picture: A probabilistic programming language for scene perception , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[329] M. Asada. Development of artificial empathy , 2015, Neuroscience Research.
[330] Yuandong Tian,et al. Better Computer Go Player with Neural Network and Long-term Prediction , 2016, ICLR.
[331] Daan Wierstra,et al. One-Shot Generalization in Deep Generative Models , 2016, ICML.
[332] Ruslan Salakhutdinov,et al. Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning , 2015, ICLR.
[333] Geoffrey E. Hinton,et al. Attend, Infer, Repeat: Fast Scene Understanding with Generative Models , 2016, NIPS.
[334] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[335] Shimon Edelman,et al. The minority report: some common assumptions to reconsider in the modelling of the brain and behaviour , 2016, J. Exp. Theor. Artif. Intell..
[336] Tomas Mikolov,et al. A Roadmap Towards Machine Intelligence , 2015, CICLing.
[337] Marcin Andrychowicz,et al. Learning to learn by gradient descent by gradient descent , 2016, NIPS.
[338] J. Davies. Program good ethics into artificial intelligence , 2016, Nature.
[339] Tom Schaul,et al. Dueling Network Architectures for Deep Reinforcement Learning , 2015, ICML.
[340] Matthias Scheutz,et al. Against the moral Turing test: accountable design and the moral reasoning of autonomous systems , 2016, Ethics and Information Technology.
[341] Guigang Zhang,et al. Deep Learning , 2016, Int. J. Semantic Comput..
[342] Thomas L. Griffiths,et al. Adapting Deep Network Features to Capture Psychological Representations: An Abridged Report , 2017, IJCAI.
[343] Yoshua Bengio,et al. Towards a Biologically Plausible Backprop , 2016, ArXiv.
[344] Sergio Gomez Colmenarejo,et al. Hybrid computing using a neural network with dynamic external memory , 2016, Nature.
[345] Demis Hassabis,et al. Mastering the game of Go with deep neural networks and tree search , 2016, Nature.
[346] Ben Calderhead,et al. Advances in Neural Information Processing Systems 29 , 2016 .
[347] Jason Weston,et al. Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks , 2015, ICLR.
[348] Laurence Aitchison,et al. The Hamiltonian Brain: Efficient Probabilistic Inference with Excitatory-Inhibitory Neural Circuit Dynamics , 2014, PLoS Comput. Biol..
[349] Nando de Freitas,et al. Neural Programmer-Interpreters , 2015, ICLR.
[350] Rob Fergus,et al. Learning Physical Intuition of Block Towers by Example , 2016, ICML.
[351] Joel Z. Leibo,et al. Model-Free Episodic Control , 2016, ArXiv.
[352] Daan Wierstra,et al. Towards Conceptual Compression , 2016, NIPS.
[353] Oriol Vinyals,et al. Matching Networks for One Shot Learning , 2016, NIPS.
[354] Tom Schaul,et al. Prioritized Experience Replay , 2015, ICLR.
[355] Joshua B. Tenenbaum,et al. Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation , 2016, NIPS.
[356] Lorenzo Rosasco,et al. Unsupervised learning of invariant representations , 2016, Theor. Comput. Sci..
[357] Quoc V. Le,et al. Multi-task Sequence to Sequence Learning , 2015, ICLR.
[358] Joel Z. Leibo,et al. How Important Is Weight Symmetry in Backpropagation? , 2015, AAAI.
[359] T. Chouard. The Go Files: AI computer wraps up 4-1 victory against human champion , 2016, Nature.
[360] Tapani Raiko,et al. International Conference on Learning Representations (ICLR) , 2016 .
[361] Bernhard Schölkopf,et al. Unifying distillation and privileged information , 2015, ICLR.
[362] Philippe Gaussier,et al. Learning to Synchronously Imitate Gestures Using Entrainment Effect , 2016, SAB.
[363] Andreea C. Bostan,et al. Consensus Paper: Towards a Systems-Level View of Cerebellar Function: the Interplay Between Cerebellum, Basal Ganglia, and Cortex , 2016, The Cerebellum.
[364] Gary Lupyan,et al. How Language Programs the Mind , 2016, Top. Cogn. Sci..
[365] Yoshua Bengio,et al. Equilibrium Propagation: Bridging the Gap between Energy-Based Models and Backpropagation , 2016, Front. Comput. Neurosci..
[366] K. Rohlfing,et al. Intermodal synchrony – as a form of maternal responsiveness – is associated with language development , 2017 .
[367] Kenneth D. Forbus,et al. Modeling Visual Problem Solving as Analogical Reasoning , 2017, Psychological review.
[368] Fei-Fei Li,et al. Deep visual-semantic alignments for generating image descriptions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[369] Finale Doshi-Velez,et al. A Roadmap for a Rigorous Science of Interpretability , 2017, ArXiv.
[370] Jiqiang Guo,et al. Stan: A Probabilistic Programming Language. , 2017, Journal of statistical software.
[371] Dmitry P. Vetrov,et al. Fast Adaptation in Generative Models with Generative Matching Networks , 2016, ICLR.
[372] Donna Lockery. Why Smart People Can Be So Stupid , 2017 .
[373] Chris L. Baker,et al. Rational quantitative attribution of beliefs, desires and percepts in human mentalizing , 2017, Nature Human Behaviour.
[374] Philipp Koehn,et al. Cognitive Psychology , 1992, Ageing and Society.
[375] Susan Craw,et al. Case-Based Reasoning , 2010, Encyclopedia of Machine Learning.
[376] C. Robert. Superintelligence: Paths, Dangers, Strategies , 2017 .
[377] C. Daly. Why Only Us? Language and Evolution , 2018 .
[378] Robert,et al. Computer Simulation of Individual Belief Systems * , .