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When encountering novel objects, humans are able to infer a wide range of physical properties such as mass, friction and deformability by interacting with them in a goal driven way.
Mechanical reasoning by mental simulation
Mary Hegarty · 2004
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The development of embodied cognition: Six lessons from babies
Linda Smith and Michael Gasser · 2005
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Core knowledge
Elizabeth S Spelke and Katherine D Kinzler · 2007
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Best arm identification in multi-armed bandits
Jean-Yves Audibert and Sébastien Bubeck · 2010
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Internal physics models guide probabilistic judgments about object dynamics
Jessica Hamrick, Peter Battaglia, and Joshua B Tenenbaum · 2011
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Horde: A scalable real-time architecture for learning knowledge from unsupervised sensorimotor interaction
Richard S Sutton, Joseph Modayil, Michael Delp, Thomas Degris, Patrick M Pilarski, Adam White, and Doina Precup · 2011
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Scene semantics from long-term observation of people
Vincent Delaitre, David F Fouhey, Ivan Laptev, Josef Sivic, Abhinav Gupta, and Alexei A Efros · 2012
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Scientific thinking in young children: Theoretical advances, empirical research, and policy implications
Alison Gopnik · 2012
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Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups
Geoffrey E. Hinton, Li Deng, Dong Yu, George E. Dahl, Abdel-rahman Mohamed, Navdeep Jaitly, Andrew Senior, Vincent Vanhoucke, Patrick Nguyen, Tara N. Sainath, and Brian Kingsbury · 2012
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Simulation as an engine of physical scene understanding
Peter W Battaglia, Jessica B Hamrick, and Joshua B Tenenbaum · 2013
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People watching: Human actions as a cue for single view geometry
David F Fouhey, Vincent Delaitre, Abhinav Gupta, Alexei A Efros, Ivan Laptev, and Josef Sivic · 2014
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2014
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Video (language) modeling: a baseline for generative models of natural videos
MarcAurelio Ranzato, Arthur Szlam, Joan Bruna, Michael Mathieu, Ronan Collobert, and Sumit Chopra · 2014
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Learning to see by moving
Pulkit Agrawal, João Carreira, and Jitendra Malik · 2015
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Data-efficient learning of feedback policies from image pixels using deep dynamical models
John-Alexander M Assael, Niklas Wahlström, Thomas B Schön, and Marc Peter Deisenroth · 2015
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Unsupervised visual representation learning by context prediction
Carl Doersch, Abhinav Gupta, and Alexei A Efros · 2015
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Learning image representations tied to ego-motion
Dinesh Jayaraman and Kristen Grauman · 2015
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Human-level concept learning through probabilistic program induction
Brenden M Lake, Ruslan Salakhutdinov, and Joshua B Tenenbaum · 2015
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Action-conditional video prediction using deep networks in Atari games
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Look-ahead before you leap: End-to-end active recognition by forecasting the effect of motion
Dinesh Jayaraman and Kristen Grauman · 2016
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Learning physical intuition of block towers by example
Adam Lerer, Sam Gross, and Rob Fergus · 2016
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Sergey Levine, Peter Pastor, Alex Krizhevsky, and Deirdre Quillen · 2016
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Asynchronous methods for deep reinforcement learning
Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy P Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu · 2016
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Junhyuk Oh, Xiaoxiao Guo, Honglak Lee, Richard Lewis, and Satinder Singh · 2015
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Andrew Owens, Phillip Isola, Josh McDermott, Antonio Torralba, Edward H Adelson, and William T Freeman · 2015
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Unsupervised learning of video representations using lstms
Nitish Srivastava, Elman Mansimov, and Ruslan Salakhutdinov · 2015
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Embed to control: A locally linear latent dynamics model for control from raw images
Manuel Watter, Jost Springenberg, Joschka Boedecker, and Martin Riedmiller · 2015
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Galileo: Perceiving physical object properties by integrating a physics engine with deep learning
Jiajun Wu, Ilker Yildirim, Joseph J Lim, Bill Freeman, and Josh Tenenbaum · 2015
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Learning to poke by poking: Experiential learning of intuitive physics
Pulkit Agrawal, Ashvin Nair, Pieter Abbeel, and Jitendra Malik · 2016
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Active viewing in toddlers facilitates visual object learning: An egocentric vision approach
Sven Bambach, David J Crandall, Linda B Smith, and Chen Yu · 2016
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“what happens if…” learning to predict the effect of forces in images
Roozbeh Mottaghi, Mohammad Rastegari, Abhinav Gupta, and Ali Farhadi · 2016
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Ambient sound provides supervision for visual learning
Andrew Owens, Jiajun Wu, Josh H McDermott, William T Freeman, and Antonio Torralba · 2016
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Supersizing self-supervision: Learning to grasp from 50k tries and 700 robot hours
Lerrel Pinto and Abhinav Gupta · 2016
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The curious robot: Learning visual representations via physical interactions
Lerrel Pinto, Dhiraj Gandhi, Yuanfeng Han, Yong-Lae Park, and Abhinav Gupta · 2016
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Label-free supervision of neural networks with physics and domain knowledge
Russell Stewart and Stefano Ermon · 2016
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Pixel recurrent neural networks
Aaron van den Oord, Nal Kalchbrenner, and Koray Kavukcuoglu · 2016
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Physics 101: Learning physical object properties from unlabeled videos
Jiajun Wu, Joseph J. Lim, Hongyi Zhang, Joshua B. Tenenbaum, and William T. Freeman · 2016
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Visual dynamics: Probabilistic future frame synthesis via cross convolutional networks
Tianfan Xue, Jiajun Wu, Katherine L. Bouman, and William T. Freeman · 2016
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Richard Zhang, Phillip Isola, and Alexei A Efros · 2016
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