Learning physical parameters from dynamic scenes
Tài liệu tham khảo
Andersson, 2008, Realism of confidence, modes of apprehension, and variable-use in visual discrimination of relative mass, Ecological Psychology, 20, 1, 10.1080/10407410701766601
Baillargeon, 2002, The acquisition of physical knowledge in infancy: A summary in eight lessons, Blackwell Handbook of Childhood Cognitive Development, 47, 10.1002/9780470996652.ch3
Baillargeon, 2008, Innate ideas revisited: For a principle of persistence in infants’ physical reasoning, Perspectives on Psychological Science, 3, 2, 10.1111/j.1745-6916.2008.00056.x
Battaglia, 2013, Simulation as an engine of physical scene understanding, Proceedings of the National Academy of Sciences of the United States of America, 110, 18327, 10.1073/pnas.1306572110
Battaglia, 2016, Interaction networks for learning about objects, relations and physics
Blum, 2013, A comparative review of dimension reduction methods in approximate Bayesian computation, Statistical Science, 28, 189, 10.1214/12-STS406
Bonawitz, 2014, Probabilistic models, learning algorithms, and response variability: Sampling in cognitive development, Trends in Cognitive Sciences, 18, 497, 10.1016/j.tics.2014.06.006
Carey, 2004, Bootstrapping and the origin of concepts, Daedalus, 133, 59, 10.1162/001152604772746701
Carroll, 2015, Evaluating the inverse reasoning account of object discovery, Cognition, 139, 130, 10.1016/j.cognition.2015.03.003
Chang, 2017, A compositional object-based approach to learning physical dynamics
Efron, 1986, Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy, Statistical Science, 54, 10.1214/ss/1177013815
Forbus, 1988, Qualitative physics: Past, present, and future, 239
Gershman, 2015, Computational rationality: A converging paradigm for intelligence in brains, minds, and machines, Science, 349, 273, 10.1126/science.aac6076
Gershman, 2012, Multistability and perceptual inference, Neural Computation, 24, 1, 10.1162/NECO_a_00226
Gerstenberg, 2012, Noisy Newtons: Unifying process and dependency accounts of causal attribution
Gilden, 1989, Understanding collision dynamics, Journal of Experimental Psychology: Human Perception and Performance, 15, 372
Gilden, 1994, Heuristic judgment of mass ratio in two-body collisions, Perception & Psychophysics, 56, 708, 10.3758/BF03208364
Goodman, 2015, Relevant and robust. A response to Marcus and Davis, Psychological Science, 10.1177/0956797614559544
Goodman, 2015, Concepts in a probabilistic language of thought
Goodman, 2008, Church: A language for generative models, Uncertainty in Artificial Intelligence
Goodman, 2013, Knowledge and implicature: Modeling language understanding as social cognition, Topics in Cognitive Science, 10.1111/tops.12007
Goodman, 2011, Learning a theory of causality, Psychological Review, 118, 110, 10.1037/a0021336
Gopnik, A., & Schulz, L. (2004). Mechanisms of theory formation in young children.
Gopnik, 2000, Detecting blickets: How young children use information about novel causal powers in categorization and induction, Child Development, 17, 1205, 10.1111/1467-8624.00224
Gopnik, 2012, Reconstructing constructivism: Causal models, Bayesian learning mechanisms, and the theory theory, Psychological Bulletin, 138, 1085, 10.1037/a0028044
Gourieroux, 1993, Indirect inference, Journal of Applied Econometrics, 8, S85, 10.1002/jae.3950080507
Griffiths, 2004, Using physical theories to infer hidden causal structure, 500
Griffiths, 2012, Bridging levels of analysis for probabilistic models of cognition, Current Directions in Psychological Science, 21, 263, 10.1177/0963721412447619
Hamrick, 2016, Inferring mass in complex physical scenes via probabilistic simulation, Cognition, 1, 2
Hespos, 2009, Five-month-old infants have different expectations for solids and liquids, Psychological Science, 20, 603, 10.1111/j.1467-9280.2009.02331.x
Jordan, 1999, An introduction to variational methods for graphical models, Machine Learning, 37, 183, 10.1023/A:1007665907178
Kemp, 2010, A probabilistic model of theory formation, Cognition, 114, 165, 10.1016/j.cognition.2009.09.003
Kim, 1992, Infants’ sensitivity to effects of gravity on visible object motion, Journal of Experimental Psychology: Human Perception and Performance, 18, 385
Kucukelbir, 2015, Automatic variational inference in Stan, 568
Kulkarni, T., Yildirim, I., Kohli, P., Freiwald, W., & Tenenbaum, J. (2014). Deep generative vision as approximate Bayesian computation. In NIPS 2014 ABC workshop.
Lamme, 2000, The distinct modes of vision offered by feedforward and recurrent processing, Trends in Neurosciences, 23, 571, 10.1016/S0166-2236(00)01657-X
Lee, 2010, Two systems of spatial representation underlying navigation, Experimental Brain Research, 206, 179, 10.1007/s00221-010-2349-5
Lieder, F., Griffiths, T., Huys, Q. J., & Goodman, N. (2016). A rational perspective on anchoring-and-adjustment: The anchoring bias reflects rational use of cognitive resources. Unpublished Manuscript.
Lieder, 2012, Burn-in, bias, and the rationality of anchoring, 2690
Lucas, 2014, When children are better (or at least more open-minded) learners than adults: Developmental differences in learning the forms of causal relationships, Cognition, 131, 284, 10.1016/j.cognition.2013.12.010
Marcus, 2013, How robust are probabilistic models of higher-level cognition?, Psychological Science, 24, 2351, 10.1177/0956797613495418
Marr, 1976, From understanding computation to understanding neural circuitry, AI Memo, 357, 1
McIntyre, 2001, Does the brain model Newton’s laws?, Nature Neuroscience, 4, 693, 10.1038/89477
Needham, 1993, Intuitions about support in 4.5-month-old infants, Cognition, 47, 121, 10.1016/0010-0277(93)90002-D
Oaksford, 1994, A rational analysis of the selection task as optimal data selection, Psychological Review, 101, 608, 10.1037/0033-295X.101.4.608
Rips, L. J., & Hespos, S. J. (2015). Divisions of the physical world: Concepts of objects and substances.
Ritchie, D., Horsfall, P., & Goodman, N. D. (2016). Deep amortized inference for probabilistic programs. Available from 1610.05735.
Runeson, 2000, Visual perception of dynamic properties: Cue heuristics versus direct-perceptual competence, Psychological Review, 107, 525, 10.1037/0033-295X.107.3.525
Sanborn, 2013, Reconciling intuitive physics and newtonian mechanics for colliding objects, Psychological Review, 120, 411, 10.1037/a0031912
Smith, 2013, Sources of uncertainty in intuitive physics, Topics in Cognitive Science, 5, 185, 10.1111/tops.12009
Spelke, E. S., & Kinzler, K. D. (2007). Core knowledge.
Stanovich, 2000, Advancing the rationality debate, Behavioral and Brain Sciences, 23, 701, 10.1017/S0140525X00623439
Stickgold, 2000, Replaying the game: Hypnagogic images in normals and amnesics, Science (New York, NY), 290, 350, 10.1126/science.290.5490.350
Stuhlmüller, 2013, Reasoning about reasoning by nested conditioning: Modeling theory of mind with probabilistic programs, Cognitive Systems Research
Téglás, 2011, Pure reasoning in 12-month-old infants as probabilistic inference, Science (New York, N.Y.), 332, 1054, 10.1126/science.1196404
Tenenbaum, 2011, How to grow a mind: Statistics, structure, and abstraction, Science (New York, N.Y.), 331, 1279, 10.1126/science.1192788
Todd, 1982, Visual perception of relative mass in dynamic events, Perception, 11, 325, 10.1068/p110325
Ullman, 1995, Sequence seeking and counter streams: A computational model for bidirectional information flow in the visual cortex, Cerebral Cortex, 5, 1, 10.1093/cercor/5.1.1
Ullman, 2012, Theory learning as stochastic search in the language of the thought, Cognitive Development, 10.1016/j.cogdev.2012.07.005
Ullman, 2017, Mind games: Game engines as an architecture for intuitive physics, Trends in Cognitive Sciences, 21, 649, 10.1016/j.tics.2017.05.012
Vul, 2014, One and done? Optimal decisions from very few samples, Cognitive Science, 38, 599, 10.1111/cogs.12101
Wellman, 1992, Cognitive development: Foundational theories of core domains, Annual Review of Psychology, 43, 337, 10.1146/annurev.ps.43.020192.002005
Wingate, D., & Weber, T. (2013). Automated variational inference in probabilistic programming. Available from 1301.1299.
Wu, 2015, Galileo: Perceiving physical object properties by integrating a physics engine with deep learning, 127