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[1] Ole Winther,et al. A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning , 2017, NIPS.
[2] Joshua B. Tenenbaum,et al. End-to-End Differentiable Physics for Learning and Control , 2018, NeurIPS.
[3] Romeo Ortega,et al. Putting energy back in control , 2001 .
[4] Nicola De Cao,et al. Hyperspherical Variational Auto-Encoders , 2018, UAI 2018.
[5] Sergey Levine,et al. Visual Foresight: Model-Based Deep Reinforcement Learning for Vision-Based Robotic Control , 2018, ArXiv.
[6] Raia Hadsell,et al. Graph networks as learnable physics engines for inference and control , 2018, ICML.
[7] Razvan Pascanu,et al. Visual Interaction Networks: Learning a Physics Simulator from Video , 2017, NIPS.
[8] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[9] Demis Hassabis,et al. Mastering the game of Go without human knowledge , 2017, Nature.
[10] Martin A. Riedmiller,et al. Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images , 2015, NIPS.
[11] Amit Chakraborty,et al. Symplectic ODE-Net: Learning Hamiltonian Dynamics with Control , 2020, ICLR.
[12] Chen Sun,et al. Unsupervised Learning of Object Structure and Dynamics from Videos , 2019, NeurIPS.
[13] Ankush Gupta,et al. Unsupervised Learning of Object Keypoints for Perception and Control , 2019, NeurIPS.
[14] Ruben Villegas,et al. Learning Latent Dynamics for Planning from Pixels , 2018, ICML.
[15] Ang Li,et al. Prediction, Consistency, Curvature: Representation Learning for Locally-Linear Control , 2020, ICLR.
[16] Kristian Kersting,et al. Structured Object-Aware Physics Prediction for Video Modeling and Planning , 2020, ICLR.
[17] Max Welling,et al. Auto-Encoding Variational Bayes , 2013, ICLR.
[18] Kyle Cranmer,et al. Hamiltonian Graph Networks with ODE Integrators , 2019, ArXiv.
[19] Natalia Gimelshein,et al. PyTorch: An Imperative Style, High-Performance Deep Learning Library , 2019, NeurIPS.
[20] Andrew Jaegle,et al. Hamiltonian Generative Networks , 2020, ICLR.
[21] Jan Peters,et al. Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning , 2019, ICLR.
[22] Razvan Pascanu,et al. Interaction Networks for Learning about Objects, Relations and Physics , 2016, NIPS.
[23] Quoc V. Le,et al. Sequence to Sequence Learning with Neural Networks , 2014, NIPS.
[24] Amit Chakraborty,et al. Dissipative SymODEN: Encoding Hamiltonian Dynamics with Dissipation and Control into Deep Learning , 2020, ArXiv.
[25] Richard S. Sutton,et al. Generalization in Reinforcement Learning: Successful Examples Using Sparse Coarse Coding , 1995, NIPS.
[26] Eric Kauderer-Abrams,et al. Quantifying Translation-Invariance in Convolutional Neural Networks , 2017, ArXiv.
[27] Mohammad Norouzi,et al. Dream to Control: Learning Behaviors by Latent Imagination , 2019, ICLR.
[28] Marc Peter Deisenroth,et al. Variational Integrator Networks for Physically Structured Embeddings , 2020, AISTATS.
[29] David Duvenaud,et al. Neural Ordinary Differential Equations , 2018, NeurIPS.
[30] Naomi Ehrich Leonard,et al. Controlled Lagrangians and the stabilization of mechanical systems. I. The first matching theorem , 2000, IEEE Trans. Autom. Control..
[31] Jason Yosinski,et al. Hamiltonian Neural Networks , 2019, NeurIPS.
[32] Alexander Lerchner,et al. COBRA: Data-Efficient Model-Based RL through Unsupervised Object Discovery and Curiosity-Driven Exploration , 2019, ArXiv.
[33] Andrew Zisserman,et al. Spatial Transformer Networks , 2015, NIPS.