Galileo: Perceiving Physical Object Properties by Integrating a Physics Engine with Deep Learning
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Jiajun Wu | Joshua B. Tenenbaum | Joseph J. Lim | Bill Freeman | Ilker Yildirim | J. Tenenbaum | Joseph J. Lim | Jiajun Wu | Bill Freeman | Ilker Yildirim
[1] Geoffrey E. Hinton,et al. The Helmholtz Machine , 1995, Neural Computation.
[2] Katsushi Ikeuchi,et al. Detecting potential falling objects by inferring human action and natural disturbance , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).
[3] Tsuhan Chen,et al. 3D Reasoning from Blocks to Stability , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[4] S. Carey. The Origin of Concepts , 2000 .
[5] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[6] R. Baillargeon. Infants' Physical World , 2004 .
[7] Pieter Abbeel,et al. Tracking deformable objects with point clouds , 2013, 2013 IEEE International Conference on Robotics and Automation.
[8] Jessica B. Hamrick,et al. Simulation as an engine of physical scene understanding , 2013, Proceedings of the National Academy of Sciences.
[9] Joshua B. Tenenbaum,et al. Efficient analysis-by-synthesis in vision: A computational framework, behavioral tests, and modeling neuronal representations , 2015, Annual Meeting of the Cognitive Science Society.
[10] Noah D. Goodman,et al. Learning physics from dynamical scenes , 2014 .
[11] Joshua B. Tenenbaum,et al. Learning physical theories from dynamical scenes , 2014, CogSci.
[12] Vikash K. Mansinghka,et al. Reconciling intuitive physics and Newtonian mechanics for colliding objects. , 2013, Psychological review.