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We are interested in learning models of intuitive physics similar to the ones that animals use for navigation, manipulation and planning.
Learning to see physics via visual de-animation
J. Wu et al · 1905
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Understanding the difficulty of training deep feedforward neural networks
X. Glorot and Y. Bengio · 2010
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. Hinton · 2012
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Simulation as an engine of physical scene understanding
P.W. Battaglia, J. Hamrick, and J.B. Tenenbaum · 2013
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Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
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TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
Abadi et al · 2015
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Data-driven fluid simulations using regression forests
Ladický et al · 2015
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Action-conditional video prediction using deep networks in atari games
J. Oh et al · 2015
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U-net: Convolutional networks for biomedical image segmentation
O. Ronneberger and T. Fischer, P.and Brox · 2015
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2015
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Galileo: Perceiving physical object properties by integrating a physics engine with deep learning
J. Wu et al · 2015
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Learning to Poke by Poking: Experiential Learning of Intuitive Physics
P. Agrawal et al · 2016
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Interaction networks for learning about objects, relations and physics
P. Battaglia et al · 2016
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Dynamic image networks for action recognition
H. Bilen et al · 2016
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Learning to perform physics experiments via deep reinforcement learning
M. Denil et al · 2016
Cited alongside, same era.
Deep spatial autoencoders for visuomotor learning
C. Finn et al · 2016
Cited alongside, same era.
Unsupervised learning for physical interaction through video prediction
C. Finn, I. Goodfellow, and S. Levine · 2016
Cited alongside, same era.
Learning visual predictive models of physics for playing billiards
K. Fragkiadaki et al · 2016
Cited alongside, same era.
Perceptual losses for real-time style transfer and super-resolution
J. Johnson, A. Alahi, and L. Fei-Fei · 2016
Cited alongside, same era.
Learning physical intuition of block towers by example
A. Lerer, S. Gross, and R. Fergus · 2016
Cited alongside, same era.
Schema networks: Zero-shot transfer with a generative causal model of intuitive physics
K. Kansky et al · 2017
Later among the works it cites.
Visual stability prediction and its application to manipulation
W. Li, A. Leonardis, and M. Fritz · 2017
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Label-free supervision of neural networks with physics and domain knowledge
R. Stewart and S. Ermon · 2017
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Visual interaction networks: Learning a physics simulator from video
N. Watters et al · 2017
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Long-term image boundary prediction
A. Bhattacharyya et al · 2018
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Unsupervised intuitive physics from visual observations
S. Ehrhardt et al · 2018
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SMASH: Physics-guided Reconstruction of Collisions from Videos
A. Monszpart, N. Thuerey, and N. Mitra · 2016
Cited alongside, same era.
Newtonian scene understanding: Unfolding the dynamics of objects in static images
R. Mottaghi et al · 2016
Cited alongside, same era.
Deep tracking: Seeing beyond seeing using recurrent neural networks
P. Ondruska and I. Posner · 2016
Cited alongside, same era.
Accelerating Eulerian Fluid Simulation With Convolutional Networks
J. Tompson et al · 2016
Cited alongside, same era.
Physics 101: Learning physical object properties from unlabeled videos
J. Wu et al · 2016
Cited alongside, same era.
A compositional object-based approach to learning physical dynamics
M. B. Chang et al · 2017
Cited alongside, same era.
Shapestacks: Learning vision-based physical intuition for generalised object stacking
O. Groth et al · 2018
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Flexible Neural Representation for Physics Prediction
D. Mrowca et al · 2018
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IntPhys: A Framework and Benchmark for Visual Intuitive Physics Reasoning
R. Riochet et al · 2018
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Graph networks as learnable physics engines for inference and control
A. Sanchez-Gonzalez et al · 2018
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Relational neural expectation maximization: Unsupervised discovery of objects and their interactions
S. van Steenkiste et al · 2018
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Neural allocentric intuitive physics prediction from real videos
Z. Wang et al · 2018
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Taking visual motion prediction to new heightfields
S. Ehrhardt et al · 2019
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