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Chen Sun | Jiajun Wu | Joshua B. Tenenbaum | Kevin Murphy | Per Karlsson | J. Tenenbaum | K. Murphy | Chen Sun | Jiajun Wu | Per Karlsson
[1] Fabio Viola,et al. Learning and Querying Fast Generative Models for Reinforcement Learning , 2018, ArXiv.
[2] Kris M. Kitani,et al. Activity Forecasting: An Invitation to Predictive Perception , 2017, Group and Crowd Behavior for Computer Vision.
[3] Jürgen Schmidhuber,et al. Relational Neural Expectation Maximization: Unsupervised Discovery of Objects and their Interactions , 2018, ICLR.
[4] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[5] Rob Fergus,et al. Stochastic Video Generation with a Learned Prior , 2018, ICML.
[6] Hamid Abrishami Moghaddam,et al. A survey on player tracking in soccer videos , 2017, Comput. Vis. Image Underst..
[7] Ole Winther,et al. Sequential Neural Models with Stochastic Layers , 2016, NIPS.
[8] Sergey Levine,et al. Backprop KF: Learning Discriminative Deterministic State Estimators , 2016, NIPS.
[9] Samy Bengio,et al. Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks , 2015, NIPS.
[10] Philip H. S. Torr,et al. DESIRE: Distant Future Prediction in Dynamic Scenes with Interacting Agents , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[11] Joshua B. Tenenbaum,et al. A Compositional Object-Based Approach to Learning Physical Dynamics , 2016, ICLR.
[12] R. Zemel,et al. Neural Relational Inference for Interacting Systems , 2018, ICML.
[13] Niloy J. Mitra,et al. Taking Visual Motion Prediction To New Heightfields , 2019, Comput. Vis. Image Underst..
[14] Yoshua Bengio,et al. A Recurrent Latent Variable Model for Sequential Data , 2015, NIPS.
[15] Yaakov Bar-Shalom,et al. A note on "book review tracking and data fusion: A handbook of algorithms" [Authors' reply] , 2013 .
[16] Antonio Torralba,et al. Anticipating Visual Representations from Unlabeled Video , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[17] Razvan Pascanu,et al. Interaction Networks for Learning about Objects, Relations and Physics , 2016, NIPS.
[18] Ira Kemelmacher-Shlizerman,et al. Soccer on Your Tabletop , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[19] Sergey Levine,et al. Stochastic Adversarial Video Prediction , 2018, ArXiv.
[20] Thomas B. Moeslund,et al. Identifying Basketball Plays from Sensor Data; Towards a Low-Cost Automatic Extraction of Advanced Statistics , 2017, 2017 IEEE International Conference on Data Mining Workshops (ICDMW).
[21] Jitendra Malik,et al. Learning Visual Predictive Models of Physics for Playing Billiards , 2015, ICLR.
[22] Jiajun Wu,et al. Visual Dynamics: Probabilistic Future Frame Synthesis via Cross Convolutional Networks , 2016, NIPS.
[23] Jiajun Wu,et al. Learning to See Physics via Visual De-animation , 2017, NIPS.
[24] Rob Fergus,et al. Learning Physical Intuition of Block Towers by Example , 2016, ICML.
[25] Jitendra Malik,et al. What will Happen Next? Forecasting Player Moves in Sports Videos , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[26] Sergey Levine,et al. Stochastic Variational Video Prediction , 2017, ICLR.
[27] Raia Hadsell,et al. Graph networks as learnable physics engines for inference and control , 2018, ICML.
[28] Razvan Pascanu,et al. Relational inductive biases, deep learning, and graph networks , 2018, ArXiv.
[29] Silvio Savarese,et al. Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[30] Jan Kautz,et al. MoCoGAN: Decomposing Motion and Content for Video Generation , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[31] Yisong Yue,et al. Generating Multi-Agent Trajectories using Programmatic Weak Supervision , 2018, ICLR.
[32] Samy Bengio,et al. Generating Sentences from a Continuous Space , 2015, CoNLL.
[33] Max Welling,et al. Auto-Encoding Variational Bayes , 2013, ICLR.
[34] Yann LeCun,et al. Deep multi-scale video prediction beyond mean square error , 2015, ICLR.
[35] Dushyant Rao,et al. Deep tracking in the wild: End-to-end tracking using recurrent neural networks , 2018, Int. J. Robotics Res..
[36] Martial Hebert,et al. An Uncertain Future: Forecasting from Static Images Using Variational Autoencoders , 2016, ECCV.
[37] Razvan Pascanu,et al. A simple neural network module for relational reasoning , 2017, NIPS.
[38] Yee Whye Teh,et al. Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects , 2018, NeurIPS.
[39] Antonio Torralba,et al. Generating Videos with Scene Dynamics , 2016, NIPS.
[40] Maximilian Karl,et al. Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data , 2016, ICLR.
[41] Alexander A. Alemi,et al. Fixing a Broken ELBO , 2017, ICML.
[42] Silvio Savarese,et al. Social LSTM: Human Trajectory Prediction in Crowded Spaces , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[43] Yoshua Bengio,et al. Z-Forcing: Training Stochastic Recurrent Networks , 2017, NIPS.
[44] Yisong Yue,et al. Generative Multi-Agent Behavioral Cloning , 2018, ArXiv.
[45] Ali Farhadi,et al. Newtonian Image Understanding: Unfolding the Dynamics of Objects in Static Images , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[46] Uri Shalit,et al. Structured Inference Networks for Nonlinear State Space Models , 2016, AAAI.
[47] Yedid Hoshen,et al. VAIN: Attentional Multi-agent Predictive Modeling , 2017, NIPS.
[48] Razvan Pascanu,et al. Visual Interaction Networks: Learning a Physics Simulator from Video , 2017, NIPS.
[49] Pietro Liò,et al. Graph Attention Networks , 2017, ICLR.
[50] Chen Sun,et al. Rethinking Spatiotemporal Feature Learning: Speed-Accuracy Trade-offs in Video Classification , 2017, ECCV.