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Building machines that learn and think like people

  • Brenden M. Lake (a1), Tomer D. Ullman (a2), Joshua B. Tenenbaum (a3) and Samuel J. Gershman (a4)
Abstract
Abstract

Recent progress in artificial intelligence has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats that of humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn and how they learn it. Specifically, we argue that these machines should (1) build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; (2) ground learning in intuitive theories of physics and psychology to support and enrich the knowledge that is learned; and (3) harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes toward these goals that can combine the strengths of recent neural network advances with more structured cognitive models.

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Andrychowicz M., Denil M., Gomez S., Hoffman M. W., Pfau D., Schaul T., Shillingford B. & de Freitas N. (2016). Learning to learn by gradient descent by gradient descent. Presented at the 2016 Neural Information Processing Systems conference, Barcelona, Spain, December 5–10, 2016. In: Advances in neural information processing systems 29 (NIPS 2016), ed. Lee D. D., Sugiyama M., Luxburg U. V., Guyon I. & Garnett R., pp. 3981–89). Neural Information Processing Systems.
Anselmi F., Leibo J. Z., Rosasco L., Mutch J., Tacchetti A. & Poggio T. (2016) Unsupervised learning of invariant representations. Theoretical Computer Science 633:112–21.
Bahdanau D., Cho K. & Bengio Y. (2015) Neural machine translation by jointly learning to align and translate. Presented at the International Conference on Learning Representations (ICLR), San Diego, CA, May 7–9, 2015. arXiv preprint 1409.0473. Available at: http://arxiv.org/abs/1409.0473v3.
Baillargeon R. (2004) Infants' physical world. Current Directions in Psychological Science 13:8994.
Baillargeon R., Li J., Ng W. & Yuan S. (2009) An account of infants physical reasoning. In: Learning and the infant mind, ed. Woodward A. & Neeham A., pp. 66116. Oxford University Press.
Baker C. L., Saxe R. & Tenenbaum J. B. (2009) Action understanding as inverse planning. Cognition 113(3):329–49.
Barsalou L. W. (1983) Ad hoc categories. Memory & Cognition 11(3):211–27.
Bastos A. M., Usrey W. M., Adams R. A., Mangun G. R., Fries P. & Friston K. J. (2012) Canonical microcircuits for predictive coding. Neuron 76:695711. http://doi.org/10.1016/j.neuron.2012.10.038.
Bates C. J., Yildirim I., Tenenbaum J. B. & Battaglia P. W. (2015) Humans predict liquid dynamics using probabilistic simulation. In: Proceedings of the 37th Annual Conference of the Cognitive Science Society, Pasadena, CA, July 22–25, 2015, pp. 172–77. Cognitive Science Society.
Battaglia P. W., Hamrick J. B. & Tenenbaum J. B. (2013) Simulation as an engine of physical scene understanding. Proceedings of the National Academy of Sciences of the United States of America 110(45):18327–32.
Baudiš P. & Gailly J.-l. (2012) PACHI: State of the art open source Go program. In: Advances in computer games: 13th International Conference, ACG 2011, Tillburg, The Netherlands, November 20–22, 2011, Revised Selected Papers, ed. van den Herik H. Jaap & Plast A., pp. 2438. Springer.
Baxter J. (2000) A model of inductive bias learning. Journal of Artificial Intelligence Research 12:149–98.
Bayer H. M. & Glimcher P. W. (2005) Midbrain dopamine neurons encode a quantitative reward prediction error signal. Neuron 47:129–41.
Bellemare M. G., Naddaf Y., Veness J. & Bowling M. (2013) The arcade learning environment: An evaluation platform for general agents. Journal of Artificial Intelligence Research 47:253–79.
Berlyne D. E. (1966) Curiosity and exploration. Science 153(3731):2533. doi:10.1126/science.153.3731.25
Berthiaume V. G., Shultz T. R. & Onishi K. H. (2013) A constructivist connectionist model of transitions on false-belief tasks. Cognition 126(3): 441–58.
Berwick R. C. & Chomsky N. (2016) Why only us: Language and evolution. MIT Press.
Bever T. G. & Poeppel D. (2010) Analysis by synthesis: A (re-) emerging program of research for language and vision. Biolinguistics 4:174200.
Bi G.-Q. & Poo M.-M. (2001) Synaptic modification by correlated activity: Hebb's postulate revisited. Annual Review of Neuroscience 24:139–66.
Biederman I. (1987) Recognition-by-components: A theory of human image understanding. Psychological Review 94(2):115–47.
Bienenstock E., Cooper L. N. & Munro P. W. (1982) Theory for the development of neuron selectivity: Orientation specificity and binocular interaction in visual cortex. The Journal of Neuroscience 2(1):3248.
Bienenstock E., Geman S. & Potter D. (1997) Compositionality, MDL priors, and object recognition. Presented at the 1996 Neural Information Processing Systems conference, Denver, CO, December 2–5, 1996. In: Advances in neural information processing systems 9, ed. Mozer M. C., Jordan M. I. & Petsche T., pp. 838–44. Neural Information Processing Systems Foundation.
Bloom P. (2000) How children learn the meanings of words. MIT Press.
Blundell C., Uria B., Pritzel A., Li Y., Ruderman A., Leibo J. Z., Rae J., Wierstra D. & Hassabis D. (2016) Model-free episodic control. arXiv preprint 1606.04460. Available at: https://arxiv.org/abs/1606.04460.
Bobrow D. G. & Winograd T. (1977) An overview of KRL, a knowledge representation language. Cognitive Science 1:346.
Boden M. A. (1998) Creativity and artificial intelligence. Artificial Intelligence 103:347–56.
Boden M. A. (2006) Mind as machine: A history of cognitive science. Oxford University Press.
Bonawitz E., Denison S., Griffiths T. L. & Gopnik A. (2014) Probabilistic models, learning algorithms, and response variability: Sampling in cognitive development. Trends in Cognitive Sciences 18:497500.
Bottou L. (2014) From machine learning to machine reasoning. Machine Learning 94(2):133–49.
Bouton M. E. (2004) Context and behavioral processes in extinction. Learning & Memory 11:485–94.
Buckingham D. & Shultz T. R. (2000) The developmental course of distance, time, and velocity concepts: A generative connectionist model. Journal of Cognition and Development 1(3):305–45.
Buesing L., Bill J., Nessler B. & Maass W. (2011) Neural dynamics as sampling: A model for stochastic computation in recurrent networks of spiking neurons. PLoS Computational Biology 7:e1002211.
Carey S. (1978) The child as word learner. In: Linguistic theory and psychological reality, ed. Bresnan J., Miller G. & Halle M., pp. 264–93. MIT Press.
Carey S. (2004) Bootstrapping and the origin of concepts. Daedalus 133(1):5968.
Carey S. (2009) The origin of concepts. Oxford University Press.
Carey S. & Bartlett E. (1978) Acquiring a single new word. Papers and Reports on Child Language Development 15:1729.
Chouard T. (2016) The Go files: AI computer wraps up 4–1 victory against human champion. (Online; posted March 15, 2016.)
Ciresan D., Meier U. & Schmidhuber J. (2012) Multi-column deep neural networks for image classification. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Providence, RI, June 16–21, 2012, pp. 3642–49. IEEE.
Collins A. G. E. & Frank M. J. (2013) Cognitive control over learning: Creating, clustering, and generalizing task-set structure. Psychological Review 120(1):190229.
Cook C., Goodman N. D. & Schulz L. E. (2011) Where science starts: Spontaneous experiments in preschoolers' exploratory play. Cognition 120(3):341–49.
Crick F. (1989) The recent excitement about neural networks. Nature 337:129–32.
Csibra G. (2008) Goal attribution to inanimate agents by 6.5-month-old infants. Cognition 107:705–17.
Csibra G., Biro S., Koos O. & Gergely G. (2003) One-year-old infants use teleological representations of actions productively. Cognitive Science 27:111–33.
Dalrymple D. (2016) Differentiable programming. Available at: https://www.edge.org/response-detail/26794.
Davis E. & Marcus G. (2015) Commonsense reasoning and commonsense knowledge in artificial Intelligence. Communications of the ACM 58(9):92103.
Daw N. D., Niv Y. & Dayan P. (2005) Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral control. Nature Neuroscience 8(12):1704–11. doi:10.1038/nn1560.
Dayan P., Hinton G. E., Neal R. M. & Zemel R. S. (1995) The Helmholtz machine. Neural Computation 7(5):889904.
Deacon T. W. (1998) The symbolic species: The co-evolution of language and the brain. WW Norton.
Denton E., Chintala S., Szlam A. & Fergus R. (2015) Deep generative image models using a Laplacian pyramid of adversarial networks. Presented at the 2015 Neural Information Processing Systems conference, Montreal, QC, Canada, In: Advances in neural information processing systems 28 (NIPS 2015), ed. Cortes C., Lawrence N. D., Lee D. D., Sugiyama M. & Garnett R. [poster]. Neural Information Processing Systems Foundation.
Diuk C., Cohen A. & Littman M. L. (2008) An object-oriented representation for efficient reinforcement learning. In: Proceedings of the 25th International Conference on Machine Learning (ICML'08), Helsinki, Finland, pp. 240–47. ACM.
Dolan R. J. & Dayan P. (2013) Goals and habits in the brain. Neuron 80:312–25.
Donahue J., Jia Y., Vinyals O., Hoffman J., Zhang N., Tzeng E. & Darrell T. (2014) DeCAF: A deep convolutional activation feature for generic visual recognition. Presented at the International Conference on Machine Learning, Beijing, China, June 22–24, 2014. Proceedings of Machine Learning Research 32(1):647–55.
Economides M., Kurth-Nelson Z., Lübbert A., Guitart-Masip M. & Dolan R. J. (2015) Model-based reasoning in humans becomes automatic with training. PLoS Computation Biology 11:e1004463.
Edelman S. (2015) The minority report: Some common assumptions to reconsider in the modelling of the brain and behaviour. Journal of Experimental & Theoretical Artificial Intelligence 28(4):751–76.
Eden M. (1962) Handwriting and pattern recognition. IRE Transactions on Information Theory 8:160–66.
Eliasmith C., Stewart T. C., Choo X., Bekolay T., DeWolf T., Tang & Y. Rasmussen D. (2012) A large-scale model of the functioning brain. Science 338(6111):1202–05.
Elman J. L. (2005) Connectionist models of cognitive development: Where next? Trends in Cognitive Sciences 9(3):111–17.
Elman J. L., Bates E. A., Johnson M. H., Karmiloff-Smith A., Parisi D. & Plunkett K. (1996) Rethinking innateness. MIT Press.
Eslami S. M., Heess N., Weber T., Tassa Y., Kavukcuoglu K. & Hinton G. E. (2016) Attend, infer, repeat: Fast scene understanding with generative models. Presented at the 2016 Neural Information Processing Systems conference, Barcelona, Spain, December 5–10, 2016. In: Advances in Neural Information Processing Systems 29 (NIPS 2016), ed. Lee D. D., Sugiyama M., Luxburg U. V., Guyon I. & Garnett R., pp. 3225–33. Neural Information Processing Systems Foundation.
Eslami S. M. A., Tarlow D., Kohli P. & Winn J. (2014) Just-in-time learning for fast and flexible inference. Presented at the 2014 Neural Information Processing Systems conference, Montreal, QC, Canada, December 8–13, 2014. In: Advances in neural information processing systems 27 (NIPS 2014), ed. Ghahramani Z., Welling M., Cortes C., Lawrence N. D. & Weinberger K. Q., pp. 1736–44. Neural Information Processing Systems Foundation.
Fodor J. A. (1975) The language of thought. Harvard University Press.
Fodor J. A. & Pylyshyn Z. W. (1988) Connectionism and cognitive architecture: A critical analysis. Cognition 28(1–2):371.
Frank M. C., Goodman N. D. & Tenenbaum J. B. (2009) Using speakers' referential intentions to model early cross-situational word learning. Psychological Science 20:578–85.
Freyd J. (1983) Representing the dynamics of a static form. Memory and Cognition 11(4):342–46.
Freyd J. (1987) Dynamic mental representations. Psychological Review 94(4):427–38.
Fukushima K. (1980) Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological Cybernetics 36:193202.
Gallistel C. & Matzel L. D. (2013) The neuroscience of learning: beyond the Hebbian synapse. Annual Review of Psychology 64:169200.
Gelly S. & Silver D. (2008) Achieving master level play in 9 × 9 computer Go. In: Proceedings of the Twenty-third AAAI Conference on Artificial Intelligence, Chicago, Illinois, July 13–17, 2008, pp. 1537–40. AAAI Press.
Gelly S. & Silver D. (2011) Monte-Carlo tree search and rapid action value estimation in computer go. Artificial Intelligence 175(11):1856–75.
Gelman A., Carlin J. B., Stern H. S. & Rubin D. B. (2004) Bayesian data analysis. Chapman & Hall/CRC.
Gelman A., Lee D. & Guo J. (2015) Stan: A probabilistic programming language for Bayesian inference and optimization. Journal of Educational and Behavioral Statistics 40:530–43.
Geman S., Bienenstock E. & Doursat R. (1992) Neural networks and the bias/variance dilemma. Neural Computation 4:158.
Gershman S. J. & Goodman N. D. (2014) Amortized inference in probabilistic reasoning. In: Proceedings of the 36th Annual Conference of the Cognitive Science Society, Quebec City, QC, Canada, July 23–26, 2014, pp. 517522. Cognitive Science Society.
Gershman S. J., Horvitz E. J. & Tenenbaum J. B. (2015) Computational rationality: A converging paradigm for intelligence in brains, minds, and machines. Science 34:273–78.
Gershman S. J., Markman A. B. & Otto A. R. (2014) Retrospective revaluation in sequential decision making: A tale of two systems. Journal of Experimental Psychology: General 143:182–94.
Gershman S. J., Vul E. & Tenenbaum J. B. (2012) Multistability and perceptual inference. Neural Computation 24:124.
Gerstenberg T., Goodman N. D., Lagnado D. A. & Tenenbaum J. B. (2015) How, whether, why: Causal judgments as counterfactual contrasts. In: Proceedings of the 37th Annual Conference of the Cognitive Science Society, Pasadena, CA, July 22–25, 2015, ed. Noelle D. C., Dale R., Warlaumont A. S., Yoshimi J., Matlock T., Jennings C. D. & Maglio P. P., pp. 782787. Cognitive Science Society.
Ghahramani Z. (2015) Probabilistic machine learning and artificial intelligence. Nature 521:452–59.
Goodman N. D., Mansinghka V. K., Roy D. M., Bonawitz K. & Tenenbaum J. B. (2008) Church: A language for generative models. In: Proceedings of the Twenty-Fourth Annual Conference on Uncertainty in Artificial Intelligence, Helsinki, Finland, July 9–12, 2008, pp. 220–29. AUAI Press.
Gopnik A., Glymour C., Sobel D. M., Schulz L. E., Kushnir T. & Danks D. (2004) A theory of causal learning in children: Causal maps and Bayes nets. Psychological Review 111(1):332.
Gopnik A. & Meltzoff A. N. (1999) Words, thoughts, and theories. MIT Press.
Graves A. (2014) Generating sequences with recurrent neural networks. arXiv preprint 1308.0850. Available at: http://arxiv.org/abs/1308.0850.
Graves A., Mohamed A.-R. & Hinton G. (2013) Speech recognition with deep recurrent neural networks. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Vancouver, BC, Canada, May 26–31, 2013, pp. 6645–49. IEEE.
Graves A., Wayne G. & Danihelka I. (2014) Neural Turing machines. arXiv preprint 1410.5401v1. Available at: http://arxiv.org/abs/1410.5401v1.
Graves A., Wayne G., Reynolds M., Harley T., Danihelka I., Grabska-Barwińska A., Colmenarejo S. G., Grefenstette E., Ramalho T., Agapiou J., Badia A. P., Hermann K. M., Zwols Y., Ostrovski G., Cain A., King H., Summerfield C., Blunsom P., Kayukcuoglu K. & Hassabis D. (2016) Hybrid computing using a neural network with dynamic external memory. Nature 538(7626):471–76.
Grefenstette E., Hermann K. M., Suleyman M. & Blunsom P. (2015). Learning to transduce with unbounded memory. Presented at the 2015 Neural Information Processing Systems conference. In: Advances in Neural Information Processing Systems 28, ed. Cortes C., Lawrence N. D., Lee D. D., Sugiyama M. & Garnett R.. Neural Information Processing Systems Foundation.
Gregor K., Besse F., Rezende D. J., Danihelka I. & Wierstra D. (2016) Towards conceptual compression. Presented at the 2016 Neural Information Processing Systems conference, Barcelona, Spain, December 5–10, 2016. In: Advances in Neural Information Processing Systems 29 (NIPS 2016), ed. Lee D. D., Sugiyama M., Luxburg U. V., Guyon I. & Garnett R. [poster]. Neural Information Processing Systems Foundation.
Gregor K., Danihelka I., Graves A., Rezende D. J. & Wierstra D. (2015) DRAW: A recurrent neural network for image generation. Presented at the 32nd Annual International Conference on Machine Learning (ICML'15), Lille, France, July 7–9, 2015. Proceedings of Machine Learning Research 37:1462–71.
Griffiths T. L., Chater N., Kemp C., Perfors A. & Tenenbaum J. B. (2010) Probabilistic models of cognition: Exploring representations and inductive biases. Trends in Cognitive Sciences 14(8):357–64.
Griffiths T. L., Vul E. & Sanborn A. N. (2012) Bridging levels of analysis for probabilistic models of cognition. Current Directions in Psychological Science 21:263–68.
Grossberg S. (1976) Adaptive pattern classification and universal recoding: I. Parallel development and coding of neural feature detectors. Biological Cybernetics 23:121–34.
Grosse R., Salakhutdinov R., Freeman W. T. & Tenenbaum J. B. (2012) Exploiting compositionality to explore a large space of model structures. In: Proceedings of the Twenty-Eighth Annual Conference on Uncertainty in Artificial Intelligence , Catalina Island , CA, ed. de Freitas N. & Murphy K. , pp. 306–15. AUAI Press
Guo X., Singh S., Lee H., Lewis R. L. & Wang X. (2014) Deep learning for real-time Atari game play using offline Monte-Carlo tree search planning. In: Advances in neural information processing systems 27 (NIPS 2014), ed. Ghahramani Z., Welling M., Cortes C., Lawrence N. D. & Weinberger K. Q. [poster]. Neural Information Processing Systems Foundation.
Gweon H., Tenenbaum J. B. & Schulz L. E. (2010) Infants consider both the sample and the sampling process in inductive generalization. Proceedings of the National Academy of Sciences of the United States of America 107:9066–71.
Halle M. & Stevens K. (1962) Speech recognition: A model and a program for research. IRE Transactions on Information Theory 8(2):155–59.
Hamlin K. J. (2013) Moral judgment and action in preverbal infants and toddlers: Evidence for an innate moral core. Current Directions in Psychological Science 22:186–93.
Hamlin K. J., Ullman T., Tenenbaum J., Goodman N. D. & Baker C. (2013) The mentalistic basis of core social cognition: Experiments in preverbal infants and a computational model. Developmental Science 16:209–26.
Hamlin K. J., Wynn K. & Bloom P. (2007) Social evaluation by preverbal infants. Nature 450:5760.
Hamlin K. J., Wynn K. & Bloom P. (2010) Three-month-olds show a negativity bias in their social evaluations. Developmental Science 13:923–29.
Hannun A., Case C., Casper J., Catanzaro B., Diamos G., Elsen E., Prenger R., Satheesh S., Shubho S., Coates A. & Ng A. Y. (2014) Deep speech: Scaling up end-to-end speech recognition. arXiv preprint 1412.5567. Available at: https://arxiv.org/abs/1412.5567.
Harlow H. F. (1949) The formation of learning sets. Psychological Review 56(1):5165.
Harlow H. F. (1950) Learning and satiation of response in intrinsically motivated complex puzzle performance by monkeys. Journal of Comparative and Physiological Psychology 43:289–94.
Hauser M. D., Chomsky N. & Fitch W. T. (2002) The faculty of language: what is it, who has it, and how did it evolve? Science 298:1569–79.
Hayes-Roth B. & Hayes-Roth F. (1979) A cognitive model of planning. Cognitive Science 3:275310.
He K., Zhang X., Ren S. & Sun J. (2016) Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, NV, June 27–30, 2016. pp. 770–78. IEEE.
Hebb D. O. (1949) The organization of behavior. Wiley.
Heess N., Tarlow D. & Winn J. (2013) Learning to pass expectation propagation messages. Presented at the 25th International Conference on Neural Information Processing Systems, Lake Tahoe, NV, December 3–6, 2012. In: Advances in Neural Information Processing Systems 25 (NIPS 2012), ed. Pereira F., Burges C. J. C., Bottou L. & Weinberger K. Q., pp. 3219–27. Neural Information Processing Systems Foundation.
Hespos S. J. & Baillargeon R. (2008) Young infants' actions reveal their developing knowledge of support variables: Converging evidence for violation-of-expectation findings. Cognition 107:304–16.
Hespos S. J., Ferry A. L. & Rips L. J. (2009) Five-month-old infants have different expectations for solids and liquids. Psychological Science 20(5):603–11.
Hinton G. E. (2002) Training products of experts by minimizing contrastive divergence. Neural Computation 14(8):1771–800.
Hinton G. E., Dayan P., Frey B. J. & Neal R. M. (1995) The “wake-sleep” algorithm for unsupervised neural networks. Science 268(5214):1158–61.
Hinton G. E., Deng L., Yu D., Dahl G. E., Mohamed A.-r., Jaitly N., Senior A., Vanhoucke V., Nguyen P., Sainath T. & Kingsbury B. (2012) Deep neural networks for acoustic modeling in speech recognition. IEEE Signal Processing Magazine 29:8297.
Hinton G. E., Osindero S. & Teh Y. W. (2006) A fast learning algorithm for deep belief nets. Neural Computation 18:1527–54.
Hoffman D. D. & Richards W. A. (1984) Parts of recognition. Cognition 18:6596.
Hofstadter D. R. (1985) Metamagical themas: Questing for the essence of mind and pattern. Basic Books.
Horst J. S. & Samuelson L. K. (2008) Fast mapping but poor retention by 24-month-old infants. Infancy 13(2):128–57.
Huang Y. & Rao R. P. (2014) Neurons as Monte Carlo samplers: Bayesian? inference and learning in spiking networks Presented at the 2014 Neural Information Processing Systems conference, Montreal, QC, Canada. In: Advances in neural information processing systems 27 (NIPS 2014), ed. Ghahramani Z., Welling M., Cortes C., Lawrence N. D. & Weinberger K. Q., pp. 1943–51. Neural Information Processing Systems Foundation.
Hummel J. E. & Biederman I. (1992) Dynamic binding in a neural network for shape recognition. Psychological Review 99(3):480517.
Jackendoff R. (2003) Foundations of language. Oxford University Press.
Jara-Ettinger J., Gweon H., Tenenbaum J. B. & Schulz L. E. (2015) Children's understanding of the costs and rewards underlying rational action. Cognition 140:1423.
Jern A. & Kemp C. (2013) A probabilistic account of exemplar and category generation. Cognitive Psychology 66(1):85125.
Jern A. & Kemp C. (2015) A decision network account of reasoning about other peoples choices. Cognition 142:1238.
Johnson S. C., Slaughter V. & Carey S. (1998) Whose gaze will infants follow? The elicitation of gaze-following in 12-month-olds. Developmental Science 1:233–38.
Jonge M. de & Racine R. J. (1985) The effects of repeated induction of long-term potentiation in the dentate gyrus. Brain Research 328:181–85.
Juang B. H. & Rabiner L. R. (1990) Hidden Markov models for speech recognition. Technometric 33(3):251–72.
Karpathy A. & Fei-Fei L. (2017) Deep visual-semantic alignments for generating image descriptions. IEEE Transactions on Pattern Analysis and Machine Intelligence 39(4):664–76.
Kemp C. (2007) The acquisition of inductive constraints. Unpublished doctoral dissertation, Massachusetts Institute of Technology.
Keramati M., Dezfouli A. & Piray P. (2011) Speed/accuracy trade-off between the habitual and the goal-directed processes. PLoS Computational Biology 7:e1002055.
Khaligh-Razavi S. M. & Kriegeskorte N. (2014) Deep supervised, but not unsupervised, models may explain IT cortical representation. PLoS Computational Biology 10(11):e1003915.
Kilner J. M., Friston K. J. & Frith C. D. (2007) Predictive coding: An account of the mirror neuron system. Cognitive Processing 8(3):159–66.
Kingma D. P., Rezende D. J., Mohamed S. & Welling M. (2014) Semi-supervised learning with deep generative models. Presented at the 2014 Neural Information Processing Systems conference, Montreal, QC, Canada. In: Advances in neural information processing systems 27 (NIPS 2014), ed. Ghahramani Z., Welling M., Cortes C., Lawrence N. D. & Weinberger K. Q. [spotlight]. Neural Information Processing Systems Foundation.
Koch G., Zemel R. S. & Salakhutdinov R. (2015) Siamese neural networks for one-shot image recognition. Presented at the Deep Learning Workshop at the 2015 International Conference on Machine Learning, Lille, France. Available at: https://www.cs.cmu.edu/~rsalakhu/papers/oneshot1.pdf.
Kodratoff Y. & Michalski R. S. (2014) Machine: earning: An artificial intelligence approach, vol. 3. Morgan Kaufmann.
Koza J. R. (1992) Genetic programming: On the programming of computers by means of natural selection, vol. 1. MIT press.
Kriegeskorte N. (2015) Deep neural networks: A new framework for modeling biological vision and brain information processing. Annual Review of Vision Science 1:417–46.
Krizhevsky A., Sutskever I. & Hinton G. E. (2012). ImageNet classification with deep convolutional neural networks. Presented at the 25th International Conference on Neural Information Processing Systems, Lake Tahoe, NV, December 3–6, 2012. In: Advances in Neural Information Processing Systems 25 (NIPS 2012), ed. Pereira F., Burges C. J. C., Bottou L. & Weinberger K. Q., pp. 1097–105. Neural Information Processing Systems Foundation.
Kulkarni T. D., Kohli P., Tenenbaum J. B. & Mansinghka V. (2015a) Picture: A probabilistic programming language for scene perception. In: Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, June 7–12, 2015, pp. 4390–99. IEEE.
Kulkarni T. D., Narasimhan K. R., Saeedi A. & Tenenbaum J. B. (2016) Hierarchical deep reinforcement learning: Integrating temporal abstraction and intrinsic motivation. arXiv preprint 1604.06057. Available at: https://arxiv.org/abs/1604.06057.
Kulkarni T. D., Whitney W., Kohli P. & Tenenbaum J. B. (2015b) Deep convolutional inverse graphics network. arXiv preprint 1503.03167. Available at: https://arxiv.org/abs/1503.03167.
Lake B. M. (2014) Towards more human-like concept learning in machines: Compositionality, causality, and learning-to-learn. Unpublished doctoral dissertation, Massachusetts Institute of Technology.
Lake B. M., Lee C.-Y., Glass J. R. & Tenenbaum J. B. (2014) One-shot learning of generative speech concepts. In: Proceedings of the 36th Annual Conference of the Cognitive Science Society, Quebec City, QC, Canada, July 23–26, 2014, pp. 803–08. Cognitive Science Society.
Lake B. M., Salakhutdinov R. & Tenenbaum J. B. (2012) Concept learning as motor program induction: A large-scale empirical study. In: Proceedings of the 34th Annual Conference of the Cognitive Science Society, Sapporo, Japan, August 1–4, 2012, pp. 659–64. Cognitive Science Society.
Lake B. M., Salakhutdinov R. & Tenenbaum J. B. (2015a) Human-level concept learning through probabilistic program induction. Science 350(6266):1332–38.
Lake B. M., Zaremba W., Fergus R. & Gureckis T. M. (2015b) Deep neural networks predict category typicality ratings for images. In: Proceedings of the 37th Annual Meeting of the Cognitive Science Society, Pasadena, CA, July 22–25, 2015. Cognitive Science Society. ISBN: 978-0-9911967-2-2.
Landau B., Smith L. B. & Jones S. S. (1988) The importance of shape in early lexical learning. Cognitive Development 3(3):299321.
Langley P., Bradshaw G., Simon H. A. & Zytkow J. M. (1987) Scientific discovery: Computational explorations of the creative processes. MIT Press.
LeCun Y., Bengio Y. & Hinton G. (2015) Deep learning. Nature 521:436–44.
LeCun Y., Boser B., Denker J. S., Henderson D., Howard R. E., Hubbard W. & Jackel L. D. (1989) Backpropagation applied to handwritten zip code recognition. Neural Computation 1:541–51.
LeCun Y., Bottou L., Bengio Y. & Haffner P. (1998) Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11):2278–323.
Lerer A., Gross S. & Fergus R. (2016) Learning physical intuition of block towers by example. Presented at the 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research 48:430–08.
Levy R. P., Reali F. & Griffiths T. L. (2009) Modeling the effects of memory on human online sentence processing with particle filters. Presented at the 2008 Neural Information Processing Systems conference. Vancouver, BC, Canada, December 8–10, 2008. In: Advances in neural information processing systems 21 (NIPS 2008), pp. 937–44. Neural Information Processing Systems.
Liao Q., Leibo J. Z. & Poggio T. (2015) How important is weight symmetry in backpropagation? arXiv preprint arXiv:1510.05067. Available at: https://arxiv.org/abs/1510.05067.
Liberman A. M., Cooper F. S., Shankweiler D. P. & Studdert-Kennedy M. (1967) Perception of the speech code. Psychological Review 74(6):431–61.
Lillicrap T. P., Cownden D., Tweed D. B. & Akerman C. J. (2014) Random feedback weights support learning in deep neural networks. arXiv preprint:1411.0247. Available at: https://arxiv.org/abs/1411.0247.
Lloyd J., Duvenaud D., Grosse R., Tenenbaum J. & Ghahramani Z. (2014) Automatic construction and natural-language description of nonparametric regression models. In: Proceedings of the national conference on artificial intelligence 2:1242–50.
Lombrozo T. (2009) Explanation and categorization: How “why?” informs “what?”. Cognition 110(2):248–53.
Lopez-Paz D., Bottou L., Scholköpf B. & Vapnik V. (2016) Unifying distillation and privileged information. Presented at the International Conference on Learning Representations (ICLR), San Juan, Puerto Rico, May 2–4, 2016. arXiv preprint 1511.03643v3. Available at: https://arxiv.org/abs/1511.03643.
Lopez-Paz D., Muandet K., Scholköpf B. & Tolstikhin I. (2015) Towards a learning theory of cause-effect inference. Presented at the 32nd International Conference on Machine Learning (ICML), Lille, France, July 7–9, 2015. Proceedings of Machine Learning Research 37:1452–61.
Luong M.-T., Le Q. V., Sutskever I., Vinyals O. & Kaiser L. (2015) Multi-task sequence to sequence learning. arXiv preprint 1511.06114. Available at: https://arxiv.org/pdf/1511.06114.pdf.
Lupyan G. & Bergen B. (2016) How language programs the mind. Topics in Cognitive Science 8(2):408–24.
Lupyan G. & Clark A. (2015) Words and the world: Predictive coding and the language perception-cognition interface. Current Directions in Psychological Science 24(4):279–84.
Macindoe O. (2013) Sidekick agents for sequential planning problems. Unpublished doctoral dissertation, Massachusetts Institute of Technology.
Magid R. W., Sheskin M. & Schulz L. E. (2015) Imagination and the generation of new ideas. Cognitive Development 34:99110.
Mansinghka V., Selsam D. & Perov Y. (2014) Venture: A higher-order probabilistic programming platform with programmable inference. arXiv preprint 1404.0099. Available at: https://arxiv.org/abs/1404.0099
Marcus G. (1998) Rethinking eliminative connectionism. Cognitive Psychology 282(37):243–82.
Marcus G. (2001) The algebraic mind: Integrating connectionism and cognitive science. MIT Press.
Markman A. B. & Makin V. S. (1998) Referential communication and category acquisition. Journal of Experimental Psychology: General 127(4):331–54.
Markman A. B. & Ross B. H. (2003) Category use and category learning. Psychological Bulletin 129(4):592613.
Markman E. M. (1989) Categorization and naming in children. MIT Press.
Marr D. C. (1982) Vision. W. H. Freeman.
Marr D. C. & Nishihara H. K. (1978) Representation and recognition of the spatial organization of three-dimensional shapes. Proceedings of the Royal Society of London Series B: Biological Sciences 200(1140):269–94.
McClelland J. L. (1988) Parallel distributed processing: Implications for cognition and development [technical report]. Defense Technical Information Center document. Available at: http://www.dtic.mil/get-tr-doc/pdf?AD=ADA219063.
McClelland J. L., Botvinick M. M., Noelle D. C., Plaut D. C., Rogers T. T., Seidenberg M. S. & Smith L. B. (2010) Letting structure emerge: Connectionist and dynamical systems approaches to cognition. Trends in Cognitive Sciences 14(8):348–56.
McClelland J. L., McNaughton B. L. & O'Reilly R. C. (1995) Why there are complementary learning systems in the hippocampus and neocortex: Insights from the successes and failures of connectionist models of learning and memory. Psychological Review 102(3):419–57.
McClelland J. L. & Rumelhart D. E. (1986) Parallel distributed processing: Explorations in the microstructure of cognition, Vol. 2. MIT Press.
Mikolov T., Joulin A. & Baroni M. (2016) A roadmap towards machine intelligence. arXiv preprint 1511.08130. Available at: http://arxiv.org/abs/1511.08130.
Mikolov T., Sutskever I. & Chen K. (2013) Distributed representations of words and phrases and their compositionality. Presented at the 2013 Neural Information Processing Systems conference, Lake Tahoe, NV, December 5–10, 2013. In: Advances in Neural Information Processing Systems 26 (NIPS), ed Burges C. J. C., Bottou L., Welling M., Ghagramani Z. & Weinberger K. Q. [poster]. Neural Information Processing Systems Foundation.
Miller E. G., Matsakis N. E. & Viola P. A. (2000) Learning from one example through shared densities on transformations. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Hilton Head Island, SC, June 15, 2000. IEEE.
Miller G. A. & Johnson-Laird P. N. (1976) Language and perception. Belknap Press.
Minsky M. L. (1974) A framework for representing knowledge. MIT-AI Laboratory Memo 306.
Minsky M. L. & Papert S. A. (1969) Perceptrons: An introduction to computational geometry. MIT Press.
Mitchell T. M., Keller R. R. & Kedar-Cabelli S. T. (1986) Explanation-based generalization: A unifying view. Machine Learning 1:4780.
Mnih A. & Gregor K. (2014) Neural variational inference and learning in belief networks. Presented at the 31st International Conference on Machine Learning, Beijing, China, June 22–24, 2014. Proceedings of Machine Learning Research 32:1791–99.
Mnih V., Heess N., Graves A. & Kavukcuoglu K. (2014). Recurrent models of visual attention. Presented at the 28th Annual Conference on Neural Information Processing Systems, Montreal, Canada. In: Advances in Neural Information Processing Systems 27(NIPS 2014), ed. Ghahramani Z., Welling M., Cortes C., Lawrence N. D. & Weinberger K. Q.. Neural Information Processing Systems Foundation.
Mnih V., Kavukcuoglu K., Silver D., Rusu A. A., Veness J., Bellemare M. G., Graves A., Riedmiller M., Fidjeland A. K., Ostrovski G., Petersen S., Beattie C., Sadik A., Antonoglous I., King H., Kumaran D., Wierstra D. & Hassabis D. (2015) Human-level control through deep reinforcement learning. Nature 518(7540):529–33.
Mohamed S. & Rezende D. J. (2015) Variational information maximisation for intrinsically motivated reinforcement learning. Presented at the 2015 Neural Information Processing Systems conference, Montreal, QC, Canada, December 7–12, 2015. Advances in Neural Information Processing Systems 28 (NIPS 2015), ed. Cortes C., Lawrence N. D., Lee D. D., Sugiyama M. & Garnett R., pp. 2125–33. Neural Information Processing Systems Foundation.
Moreno-Bote R., Knill D. C. & Pouget A. (2011) Bayesian sampling in visual perception. Proceedings of the National Academy of Sciences of the United States of America 108:12491–96.
Murphy G. L. (1988) Comprehending complex concepts. Cognitive Science 12(4):529–62.
Murphy G. L. & Medin D. L. (1985) The role of theories in conceptual coherence. Psychological Review 92(3):289316.
Murphy G. L. & Ross B. H. (1994) Predictions from uncertain categorizations. Cognitive Psychology 27:148–93.
Neisser U. (1966) Cognitive psychology. Appleton-Century-Crofts.
Newell A. & Simon H. A. (1961) GPS, A program that simulates human thought. Defense Technical Information Center.
Newell A. & Simon H. A. (1972) Human problem solving. Prentice-Hall.
Niv Y. (2009) Reinforcement learning in the brain. Journal of Mathematical Psychology 53:139–54.
O'Donnell T. J. (2015) Productivity and reuse in language: A theory of linguistic computation and storage. MIT Press.
Osherson D. N. & Smith E. E. (1981) On the adequacy of prototype theory as a theory of concepts. Cognition 9(1):3558.
Parisotto E., Ba J. L. & Salakhutdinov R. (2016) Actor-mimic: Deep multitask and transfer reinforcement learning. Presented at the International Conference on Learning Representations (ICLR), San Juan, Puerto Rico. May 2–5, 2016. arXiv preprint 1511.06342v4. Available at: https://www.google.com/search?q=arXiv%3A+preprint+1511.06342v4&ie=utf-8&oe=utf-8.
Pecevski D., Buesing L. & Maass W. (2011) Probabilistic inference in general graphical models through sampling in stochastic networks of spiking neurons. PLoS Computational Biology 7:e1002294.
Peterson J. C., Abbott J. T. & Griffiths T. L. (2016) Adapting deep network features to capture psychological representations. In: Proceedings of the 38th Annual Conference of the Cognitive Science Society, Philadelphia, Pennsylvania, August 10–13, 2016, ed. Papafragou A., Grodner Daniel J., Mirman D. & Trueswell J., pp. 2363–68. Cognitive Science Society.
Piantadosi S. T. (2011) Learning and the language of thought. Unpublished doctoral dissertation, Massachusetts Institute of Technology.
Pinker S. (2007) The stuff of thought: Language as a window into human nature. Penguin.
Pinker S. & Prince A. (1988) On language and connectionism: Analysis of a parallel distributed processing model of language acquisition. Cognition 28:73193.
Power J. M., Thompson L. T., Moyer J. R. & Disterhoft J. F. (1997) Enhanced synaptic transmission in ca1 hippocampus after eyeblink conditioning. Journal of Neurophysiology 78:1184–87.
Premack D. & Premack A. J. (1997) Infants attribute value to the goal-directed actions of self-propelled objects. Cognitive Neuroscience 9(6):848–56. doi: 10.1162/jocn.1997.9.6.848.
Reed S. & de Freitas N. (2016) Neural programmer-interpreters. Presented at the 4th International Conference on Learning Representations (ICLR), San Juan, Puerto Rico, May 2–5, 2016. arXiv preprint 1511.06279. Available at: https://arxiv.org/abs/1511.06279.
Rehder B. (2003) A causal-model theory of conceptual representation and categorization. Journal of Experimental Psychology: Learning, Memory, and Cognition 29(6):1141–59.
Rehder B. & Hastie R. (2001) Causal knowledge and categories: The effects of causal beliefs on categorization, induction, and similarity. Journal of Experimental Psychology: General 130(3):323–60.
Rehling J. A. (2001) Letter spirit (part two): Modeling creativity in a visual domain. Unpublished doctoral dissertation, Indiana University.
Rezende D. J., Mohamed S., Danihelka I., Gregor K. & Wierstra D. (2016) One-shot generalization in deep generative models. Presented at the International Conference on Machine Learning, New York, NY, June 20–22, 2016. Proceedings of Machine Learning Research 48:1521–29.
Rezende D. J., Mohamed S. & Wierstra D. (2014) Stochastic backpropagation and approximate inference in deep generative models. Presented at the International Conference on Machine Learning (ICML), Beijing, China, June 22–24, 2014. Proceedings of Machine Learning Research 32:1278–86.
Rips L. J. (1975) Inductive judgments about natural categories. Journal of Verbal Learning and Verbal Behavior 14(6):665–81.
Rips L. J. & Hespos S. J. (2015) Divisions of the physical world: Concepts of objects and substances. Psychological Bulletin 141:786811.
Rogers T. T. & McClelland J. L. (2004) Semantic cognition. MIT Press.
Rosenblatt F. (1958) The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review 65:386408.
Rougier N. P., Noelle D. C., Braver T. S., Cohen J. D. & O'Reilly R. C. (2005) Prefrontal cortex and flexible cognitive control: Rules without symbols. Proceedings of the National Academy of Sciences of the United States of America 102(20):7338–43.
Rumelhart D. E., Hinton G. & Williams R. (1986a) Learning representations by back-propagating errors. Nature 323(9):533–36.
Rumelhart D. E. & McClelland J. L. (1986) On learning the past tenses of English verbs. In: Parallel distributed processing: Explorations in the microstructure of cognition, Vol. 1, ed. Rumelhart D. F., McClelland J. L. & PDP Research Group, pp. 216–71. MIT Press.
Rumelhart D. E., McClelland J. L. & PDP Research Group. ( 1986b) Parallel distributed processing: Explorations in the microstructure of cognition, Vol. 1. MIT Press.
Russakovsky O., Deng J., Su H., Krause J., Satheesh S., Ma S., Huang Z., Karpathy A., Khosla A., Bernstein M., Berg A.C. & Fei-Fei L. (2015) ImageNet large scale visual recognition. International Journal of Computer Vision 115(3):211–52.
Russell S. & Norvig P. (2003) Artificial intelligence: A modern approach. Prentice–Hall.
Rusu A. A., Rabinowitz N. C., Desjardins G., Soyer H., Kirkpatrick J., Kavukcuoglu K., Pascanu R. & Hadsell R. (2016) Progressive neural networks. arXiv preprint 1606.04671. Available at: http://arxiv.org/abs/1606.04671.
Ryan R. M. & Deci E. L. (2007) Intrinsic and extrinsic motivations: classic definitions and new directions. Contemporary Educational Psychology 25:5467.
Salakhutdinov R., Tenenbaum J. & Torralba A. (2012) One-shot learning with a hierarchical nonparametric Bayesian model. JMLR Workshop on Unsupervised and Transfer Learning 27:195207.
Salakhutdinov R., Tenenbaum J. B. & Torralba A. (2013) Learning with hierarchical-deep models. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(8):1958–71.
Salakhutdinov R., Torralba A. & Tenenbaum J. (2011) Learning to share visual appearance for multiclass object detection. In: Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Colorado Springs, CO, June 20–25, 2011, pp. 1481–88. IEEE.
Sanborn A. N., Mansingkha V. K. & Griffiths T. L. (2013) Reconciling intuitive physics and Newtonian mechanics for colliding objects. Psychological Review 120(2):411–37.
Scellier B. & Bengio Y. (2016) Towards a biologically plausible backprop. arXiv preprint 1602.05179. Available at: https://arxiv.org/abs/1602.05179v2.
Schank R. C. (1972) Conceptual dependency: A theory of natural language understanding. Cognitive Psychology 3:552631.
Schaul T., Quan J., Antonoglou I. & Silver D. (2016) Prioritized experience replay. Presented at International Conference on Learning Representations (ICLR), San Diego, CA, May 7–9, 2015. arXiv preprint 1511.05952. Available at: https://arxiv.org/abs/1511.05952.
Schlottmann A., Cole K., Watts R. & White M. (2013) Domain-specific perceptual causality in children depends on the spatio-temporal configuration, not motion onset. Frontiers in Psychology 4:365.
Schlottmann A., Ray E. D., Mitchell A. & Demetriou N. (2006) Perceived physical and social causality in animated motions: Spontaneous reports and ratings. Acta Psychologica 123:112–43.
Schmidhuber J. (2015) Deep learning in neural networks: An overview. Neural Networks 61:85117.
Scholl B. J. & Gao T. (2013) Perceiving animacy and intentionality: Visual processing or higher-level judgment? In: Social perception: detection and interpretation of animacy, agency, and intention, ed. Rutherford M. D. & Kuhlmeier V. A.. MIT Press Scholarship Online.
Schultz W., Dayan P. & Montague P. R. (1997) A neural substrate of prediction and reward. Science 275:1593–99.
Schulz L. (2012b) The origins of inquiry: Inductive inference and exploration in early childhood. Trends in Cognitive Sciences 16(7):382–89.
Schulz L. E., Gopnik A. & Glymour C. (2007) Preschool children learn about causal structure from conditional interventions. Developmental Science 10:322–32.
Sermanet P., Eigen D., Zhang X., Mathieu M., Fergus R. & LeCun Y. (2014) OverFeat: Integrated recognition, localization and detection using convolutional networks. Presented at the International Conference on Learning Representations (ICLR), Banff, Canada, April 14–16, 2014. arXiv preprint 1312.6229v4. Available at: https://arxiv.org/abs/1312.6229.
Shafto P., Goodman N. D. & Griffiths T. L. (2014) A rational account of pedagogical reasoning: Teaching by, and learning from, examples. Cognitive Psychology 71:5589.
Shultz T. R. (2003) Computational developmental psychology. MIT Press.
Siegler R. S. & Chen Z. (1998) Developmental differences in rule learning: A microgenetic analysis. Cognitive Psychology 36(3):273310.
Silver D., Huang A., Maddison C. J., Guez A., Sifre L., Driessche G. V. D., Schrittwieser J., Antonoglou I., Panneershelvam V., Lanctot M., Dieleman S., Grewe D., Nham J., Kalchbrenner N., Sutskever I., Lillicrap T., Leach M., Kavukcuoglu K, Graepel T. & Hassabis D. (2016) Mastering the game of go with deep neural networks and tree search. Nature 529(7585):484–89.
Smith L. B., Jones S. S., Landau B., Gershkoff-Stowe L. & Samuelson L. (2002) Object name learning provides on-the-job training for attention. Psychological Science 13(1):1319.
Solomon K., Medin D. & Lynch E. (1999) Concepts do more than categorize. Trends in Cognitive Sciences 3(3):99105.
Spelke E. S. (1990) Principles of object perception. Cognitive Science 14(1):2956.
Spelke E. S. (2003) What makes us smart? Core knowledge and natural language. Spelke ES. What makes us smart? Core knowledge and natural language. In: Language in mind: Advances in the Investigation of language and thought, ed. Gentner D. & Goldin-Meadow S., pp. 277311. MIT Press.
Spelke E. S., Gutheil G. & Van de Walle G. (1995) The development of object perception. In: An invitation to cognitive science: vol. 2. Visual cognition, 2nd ed. pp. 297330. Bradford.
Spelke E. S. & Kinzler K. D. (2007) Core knowledge. Developmental Science 10(1):8996.
Srivastava N. & Salakhutdinov R. (2013) Discriminative transfer learning with tree-based priors. Presented at the 2013 Neural Information Processing Systems conference, Lake Tahoe, NV, December 5–10, 2013. In: Advances in Neural Information Processing Systems 26 (NIPS 2013), ed. Burges C J. C., Bottou L., Welling M., Ghagramani Z. & Weinberger K. Q. [poster]. Neural Information Processing Systems Foundation.
Stadie B. C., Levine S. & Abbeel P. (2016) Incentivizing exploration in reinforcement learning with deep predictive models. arXiv preprint 1507.00814. Available at: http://arxiv.org/abs/1507.00814.
Stahl A. E. & Feigenson L. (2015) Observing the unexpected enhances infants' learning and exploration. Science 348(6230):9194.
Sternberg R. J. & Davidson J. E. (1995) The nature of insight. MIT Press.
Stuhlmüller A., Taylor J. & Goodman N. D. (2013) Learning stochastic inverses. Presented at the 2013 Neural Information Processing Systems conference, Lake Tahoe, NV, December 5–10, 2013. In: Advances in Neural Information Processing Systems 26 (NIPS 2013), ed. Burges C J. C., Bottou L., Welling M., Ghagramani Z. & Weinberger K. Q., pp. 3048–56. Neural Information Processing Systems Foundation.
Sukhbaatar S., Szlam A., Weston J. & Fergus R. (2015) End-to-end memory networks. Presented at the 2015 Neural Information Processing Systems conference, Montreal, QC, Canada, December 7–12, 2015. In: Advances in neural information processing systems 28 (NIPS 2015), ed. Cortes C., Lawrence N. D., Lee D. D., Sugiyama M. & Garnett R. [oral presentation]. Neural Information Processing Systems Foundation.
Sutton R. S. (1990) Integrated architectures for learning, planning, and reacting based on approximating dynamic programming. In: Proceedings of the 7th International Workshop on Machine Learning (ICML), Austin, TX, pp. 216–24. International Machine Learning Society.
Szegedy C., Liu W., Jia Y., Sermanet P., Reed S., Anguelov D., Erhan D., Vanhoucke V. & Rabinovich A. (2014) Going deeper with convolutions. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, June 7–12, 2015, pp. 19. IEEE.
Tauber S. & Steyvers M. (2011) Using inverse planning and theory of mind for social goal inference. In: Proceedings of the 33rd Annual Conference of the Cognitive Science Society, Boston, MA, July 20–23, 2011, pp. 2480–85. Cognitive Science Society.
Téglás E., Vul E., Girotto V., Gonzalez M., Tenenbaum J. B. & Bonatti L. L. (2011) Pure reasoning in 12-month-old infants as probabilistic inference. Science 332(6033):1054–59.
Tenenbaum J. B., Kemp C., Griffiths T. L. & Goodman N. D. (2011) How to grow a mind: Statistics, structure, and abstraction. Science 331(6022):1279–85.
Tian Y. & Zhu Y. (2016) Better computer Go player with neural network and long-term prediction. Presented at the International Conference on Learning Representations (ICLR), San Juan, Puerto Rico, May 2–4, 2016. arXiv preprint 1511.06410. Available at: https://arxiv.org/abs/1511.06410.
Tomasello M. (2010) Origins of human communication. MIT Press.
Torralba A., Murphy K. P. & Freeman W. T. (2007) Sharing visual features for multiclass and multiview object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(5):854–69.
Tremoulet P. D. & Feldman J. (2000) Perception of animacy from the motion of a single object. Perception 29:943–51.
Tsividis P., Gershman S. J., Tenenbaum J. B. & Schulz L. (2013) Information selection in noisy environments with large action spaces. In: Proceedings of the 36th Annual Conference of the Cognitive Science Society, Austin, TX, pp. 1622–27. Cognitive Science Society.
Tsividis P., Tenenbaum J. B. & Schulz L. E. (2015) Constraints on hypothesis selection in causal learning. Proceedings of the 37th Annual Conference of the Cognitive Sciences, Pasadena, CA, July 23–25, 2015, pp. 2434–439. Cognitive Science Society.
Turing A. M. (1950) Computing machine and intelligence. Mind 59:433–60. Available at: http://mind.oxfordjournals.org/content/LIX/236/433.
Tversky B. & Hemenway K. (1984) Objects, parts, and categories. Journal of Experimental Psychology: General 113(2):169–91.
Ullman S., Harari D. & Dorfman N. (2012a) From simple innate biases to complex visual concepts. Proceedings of the National Academy of Sciences of the United States of America 109(44):18215–20.
Ullman T. D., Goodman N. D. & Tenenbaum J. B. (2012b) Theory learning as stochastic search in the language of thought. Cognitive Development 27(4):455–80.
van den Hengel A., Russell C., Dick A., Bastian J., Pooley D., Fleming L. & Agapitol L. (2015) Part-based modelling of compound scenes from images. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, June 7–12, 2015, pp. 878–86. IEEE.
van Hasselt H., Guez A. & Silver D. (2016) Deep learning with double Q-learning. In: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence and the Twenty-Eighth Innovative Applications of Artificial Intelligence Conference on Artificial Intelligence, Phoenix, AZ. AAAI Press.
Vinyals O., Blundell C., Lillicrap T. & Wierstra D. (2016) Matching networks for one shot learning. Vinyals, O., Blundell, C., Lillicrap, T. Kavukcuoglu, K. & Wierstra, D. (2016). Matching networks for one shot learning. Presented at the 2016 Neural Information Processing Systems conference, Barcelona, Spain, December 5–10, 2016. In: Advances in Neural Information Processing Systems 29 (NIPS 2016), ed. Lee D. D., Sugiyama M., Luxburg U. V., Guyon I. & Garnett R., pp. 3630–38. Neural Information Processing Systems Foundation.
Vinyals O., Toshev A., Bengio S. & Erhan D. (2014) Show and tell: A neural image caption generator. arXiv preprint 1411.4555. Available at: https://arxiv.org/abs/1411.4555.
Vul E., Goodman N., Griffiths T. L. & Tenenbaum J. B. (2014) One and done? Optimal decisions from very few samples. Cognitive Science 38(4):599637.
Wang Z., Schaul T., Hessel M., Hasselt H. van, Lanctot M. & de Freitas N. (2016) Dueling network architectures for deep reinforcement learning. arXiv preprint 1511.06581. Available at: http://arxiv.org/abs/1511.06581.
Ward T. B. (1994) Structured imagination: The role of category structure in exemplar generation. Cognitive Psychology 27:140.
Watkins C. J. & Dayan P. (1992) Q-learning. Machine Learning 8:279–92.
Wellman H. M. & Gelman S. A. (1992) Cognitive development: Foundational theories of core domains. Annual Review of Psychology 43:337–75.
Wellman H. M. & Gelman S. A. (1998). Knowledge acquisition in foundational domains. In: Handbook of child psychology: Vol. 2. Cognition, perception, and language development, 5th ed., series ed. Damon W., vol. ed. Damon W., pp. 523–73. Wiley.
Weston J., Chopra S. & Bordes A. (2015b) Memory networks. Presented at the International Conference on Learning Representations, San Diego, CA, May 7–9, 2015. arXiv:1410.3916. Available at: https://arxiv.org/abs/1410.3916.
Williams J. J. & Lombrozo T. (2010) The role of explanation in discovery and generalization: Evidence from category learning. Cognitive Science 34(5):776806.
Winograd T. (1972) Understanding natural language. Cognitive Psychology 3:1191.
Winston P. H. (1975) Learning structural descriptions from examples. In: The psychology of computer vision, pp.157210. McGraw-Hill.
Xu F. & Tenenbaum J. B. (2007) Word learning as Bayesian inference. Psychological Review 114(2):245–72.
Xu K., Ba J., Kiros R., Cho K., Courville A., Salakhutdinov R., Zemel R. & Bengio Y. (2015) Show, attend and tell: Neural image caption generation with visual attention. Presented at the 2015 International Conference on Machine Learning. Proceedings of Machine Learning Research 37:2048–57.
Yamins D. L. K., Hong H., Cadieu C. F., Solomon E. A., Seibert D. & DiCarlo J. J. (2014) Performance-optimized hierarchical models predict neural responses in higher visual cortex. Proceedings of the National Academy of Sciences of the United States of America 111(23):8619–24.
Yildirim I., Kulkarni T. D., Freiwald W. A. & Tenenbaum J. (2015) Efficient analysis-by-synthesis in vision: A computational framework, behavioral tests, and comparison with neural representations. In: Proceedings of the 37th Annual Conference of the Cognitive Science Society, Pasadena, CA, July 22–25, 2015. Cognitive Science Society. Available at: https://mindmodeling.org/cogsci2015/papers/0471/index.html.
Yosinski J., Clune J., Bengio Y. & Lipson H. (2014) How transferable are features in deep neural networks? Presented at the 2014 Neural Information Processing Systems conference, Montreal, QC, Canada. In: Advances in neural information processing systems 27 (NIPS 2014), ed. Ghahramani Z., Welling M., Cortes C., Lawrence N. D. & Weinberger K. Q. [oral presentation]. Neural Information Processing Systems Foundation.
Zeiler M. D. & Fergus R. (2014) Visualizing and understanding convolutional networks. In: Computer Vision—ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6–12, 2014, Proceedings, Part I, ed. Fleet D., Pajdla T., Schiele B. & Tuytelaars T., pp. 818–33. Springer.
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