暂无分享,去创建一个
Sergey Levine | Jiajun Wu | Joshua B. Tenenbaum | William T. Freeman | Chelsea Finn | Michael Janner | S. Levine | J. Tenenbaum | W. Freeman | Chelsea Finn | Michael Janner | Jiajun Wu
[1] Lawrence G. Roberts,et al. Machine Perception of Three-Dimensional Solids , 1963, Outstanding Dissertations in the Computer Sciences.
[2] Patrick Henry Winston,et al. Learning structural descriptions from examples , 1970 .
[3] Dirk P. Kroese,et al. The Cross Entropy Method: A Unified Approach To Combinatorial Optimization, Monte-carlo Simulation (Information Science and Statistics) , 2004 .
[4] Andre Cohen,et al. An object-oriented representation for efficient reinforcement learning , 2008, ICML '08.
[5] Alexei A. Efros,et al. Blocks World Revisited: Image Understanding Using Qualitative Geometry and Mechanics , 2010, ECCV.
[6] Jessica B. Hamrick. Internal physics models guide probabilistic judgments about object dynamics , 2011 .
[7] Yuval Tassa,et al. MuJoCo: A physics engine for model-based control , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.
[8] David Wingate,et al. A Physics-Based Model Prior for Object-Oriented MDPs , 2014, ICML.
[9] Kun Zhou,et al. Imagining the unseen , 2014, ACM Trans. Graph..
[10] Katsushi Ikeuchi,et al. Scene Understanding by Reasoning Stability and Safety , 2015, International Journal of Computer Vision.
[11] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[12] Tsuhan Chen,et al. 3D Reasoning from Blocks to Stability , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[13] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[14] Geoffrey E. Hinton,et al. Attend, Infer, Repeat: Fast Scene Understanding with Generative Models , 2016, NIPS.
[15] Jitendra Malik,et al. Learning Visual Predictive Models of Physics for Playing Billiards , 2015, ICLR.
[16] 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).
[17] Li Fei-Fei,et al. Perceptual Losses for Real-Time Style Transfer and Super-Resolution , 2016, ECCV.
[18] Rob Fergus,et al. Learning Physical Intuition of Block Towers by Example , 2016, ICML.
[19] Razvan Pascanu,et al. Visual Interaction Networks: Learning a Physics Simulator from Video , 2017, NIPS.
[20] Jürgen Schmidhuber,et al. Neural Expectation Maximization , 2017, NIPS.
[21] Mario Fritz,et al. Visual stability prediction for robotic manipulation , 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA).
[22] Joshua B. Tenenbaum,et al. A Compositional Object-Based Approach to Learning Physical Dynamics , 2016, ICLR.
[23] Jiajun Wu,et al. Neural Scene De-rendering , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[24] Jiajun Wu,et al. Learning to See Physics via Visual De-animation , 2017, NIPS.
[25] Sergey Levine,et al. Deep Object-Centric Representations for Generalizable Robot Learning , 2017, 2018 IEEE International Conference on Robotics and Automation (ICRA).
[26] Pascal Poupart,et al. Unsupervised Video Object Segmentation for Deep Reinforcement Learning , 2018, NeurIPS.
[27] Sergey Levine,et al. Stochastic Variational Video Prediction , 2017, ICLR.
[28] Jürgen Schmidhuber,et al. Relational Neural Expectation Maximization: Unsupervised Discovery of Objects and their Interactions , 2018, ICLR.
[29] Sergey Levine,et al. Stochastic Adversarial Video Prediction , 2018, ArXiv.
[30] Emma Brunskill,et al. Strategic Object Oriented Reinforcement Learning , 2018, ArXiv.
[31] Niloy J. Mitra,et al. Taking Visual Motion Prediction To New Heightfields , 2019, Comput. Vis. Image Underst..