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Evolution has resulted in highly developed abilities in many natural intelligences to quickly and accurately predict mechanical phenomena.
Long short-term memory
Hochreiter, Sepp and Schmidhuber, Jürgen · 1997
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Imagenet classification with deep convolutional neural networks
Krizhevsky, Alex, Sutskever, Ilya, and Hinton, Geoffrey E · 2012
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Lecture 6.5—RMSProp: Divide the gradient by a running average of its recent magnitude
Tieleman, T. and Hinton, G · 2012
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Simulation as an engine of physical scene understanding
Battaglia, Peter W, Hamrick, Jessica B, and Tenenbaum, Joshua B · 2013
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Learning phrase representations using RNN encoder–decoder for statistical machine translation
Cho, Kyunghyun, van Merriënboer, Bart, Gülçehre, Çağlar, Bahdanau, Dzmitry, Bougares, Fethi, Schwenk, Holger, and Bengio, Yoshua · 2014
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TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
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Learning visual predictive models of physics for playing billiards
Fragkiadaki, Katerina, Agrawal, Pulkit, Levine, Sergey, and Malik, Jitendra · 2015
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Long, Jonathan, Shelhamer, Evan, and Darrell, Trevor · 2015
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Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A · 2015
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Galileo: Perceiving physical object properties by integrating a physics engine with deep learning
Wu, Jiajun, Yildirim, Ilker, Lim, Joseph J, Freeman, Bill, and Tenenbaum, Josh · 2015
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Learning to Poke by Poking: Experiential Learning of Intuitive Physics
Agrawal, Pulkit, Nair, Ashvin V, Abbeel, Pieter, Malik, Jitendra, and Levine, Sergey · 2016
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Interaction networks for learning about objects, relations and physics
Battaglia, Peter, Pascanu, Razvan, Lai, Matthew, Rezende, Danilo Jimenez, et al · 2016
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A compositional object-based approach to learning physical dynamics
Chang, Michael B, Ullman, Tomer, Torralba, Antonio, and Tenenbaum, Joshua B · 2016
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Lerer, Adam, Gross, Sam, and Fergus, Rob · 2016
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Li, Wenbin, Leonardis, Aleš, and Fritz, Mario · 2016
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SMASH: Physics-guided Reconstruction of Collisions from Videos
Monszpart, Aron, Thuerey, Nils, and Mitra, Niloy · 2016
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Newtonian scene understanding: Unfolding the dynamics of objects in static images
Mottaghi, Roozbeh, Bagherinezhad, Hessam, Rastegari, Mohammad, and Farhadi, Ali · 2016
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Deep tracking: Seeing beyond seeing using recurrent neural networks
Ondruska, Peter and Posner, Ingmar · 2016
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Learning to perform physics experiments via deep reinforcement learning
Denil, Misha, Agrawal, Pulkit, Kulkarni, Tejas D, Erez, Tom, Battaglia, Peter, and de Freitas, Nando · 2016
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Inferring mass in complex scenes by mental simulation
Hamrick, J. B., Battaglia, P. W., Griffiths, T. L., and Tenenbaum, J. B · 2016
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Deep residual learning for image recognition
He, Kaiming, Zhang, Xiangyu, Ren, Shaoqing, and Sun, Jian · 2016
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Stewart, Russell and Ermon, Stefano · 2016
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Accelerating Eulerian Fluid Simulation With Convolutional Networks
Tompson, J., Schlachter, K., Sprechmann, P., and Perlin, K · 2016
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Physics 101: Learning physical object properties from unlabeled videos
Wu, Jiajun, Lim, Joseph J, Zhang, Hongyi, Tenenbaum, Joshua B, and Freeman, William T · 2016
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Visual dynamics: Probabilistic future frame synthesis via cross convolutional networks
Xue, Tianfan, Wu, Jiajun, Bouman, Katherine L, and Freeman, William T · 2016
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