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Flexible neural representation for physics prediction
Damian Mrowca · Chengxu Zhuang · Elias Wang · Nick Haber · Li Fei-Fei · Josh Tenenbaum · Daniel Yamins

Tue Dec 04 07:45 AM -- 09:45 AM (PST) @ Room 517 AB #102

Humans have a remarkable capacity to understand the physical dynamics of objects in their environment, flexibly capturing complex structures and interactions at multiple levels of detail.
Inspired by this ability, we propose a hierarchical particle-based object representation that covers a wide variety of types of three-dimensional objects, including both arbitrary rigid geometrical shapes and deformable materials.
We then describe the Hierarchical Relation Network (HRN), an end-to-end differentiable neural network based on hierarchical graph convolution, that learns to predict physical dynamics in this representation. Compared to other neural network baselines, the HRN accurately handles complex collisions and nonrigid deformations, generating plausible dynamics predictions at long time scales in novel settings, and scaling to large scene configurations. These results demonstrate an architecture with the potential to form the basis of next-generation physics predictors for use in computer vision, robotics, and quantitative cognitive science.

Author Information

Damian Mrowca (Stanford University)

Young children are excellent at playing, an ability to explore and (re)structure their environment that allows them to develop a remarkable visual and physical representation of their world that sets them apart from even the most advanced robots. Damian Mrowca is studying (1) representations and architectures that allow machines to efficiently develop an intuitive physical understanding of their world and (2) mechanisms that allow agents to learn such representations in a self-supervised way. Damian is a 3rd year PhD student co-advised by Prof. Fei-Fei Li and Prof. Daniel Yamins. He received his BSc (2012) and MSc (2015) in Electrical Engineering and Information Theory, both from the Technical University of Munich. During 2014-2015 he was a visiting student with Prof. Trevor Darrell at UC Berkeley. After a year in start-up land, looking to apply his research in businesses, he joined the Stanford Vision Lab and NeuroAILab in September 2016.

Chengxu Zhuang (Stanford University)
Elias Wang (Stanford University)
Nick Haber (Stanford University)
Li Fei-Fei (Stanford University & Google)
Josh Tenenbaum (MIT)

Josh Tenenbaum is an Associate Professor of Computational Cognitive Science at MIT in the Department of Brain and Cognitive Sciences and the Computer Science and Artificial Intelligence Laboratory (CSAIL). He received his PhD from MIT in 1999, and was an Assistant Professor at Stanford University from 1999 to 2002. He studies learning and inference in humans and machines, with the twin goals of understanding human intelligence in computational terms and bringing computers closer to human capacities. He focuses on problems of inductive generalization from limited data -- learning concepts and word meanings, inferring causal relations or goals -- and learning abstract knowledge that supports these inductive leaps in the form of probabilistic generative models or 'intuitive theories'. He has also developed several novel machine learning methods inspired by human learning and perception, most notably Isomap, an approach to unsupervised learning of nonlinear manifolds in high-dimensional data. He has been Associate Editor for the journal Cognitive Science, has been active on program committees for the CogSci and NIPS conferences, and has co-organized a number of workshops, tutorials and summer schools in human and machine learning. Several of his papers have received outstanding paper awards or best student paper awards at the IEEE Computer Vision and Pattern Recognition (CVPR), NIPS, and Cognitive Science conferences. He is the recipient of the New Investigator Award from the Society for Mathematical Psychology (2005), the Early Investigator Award from the Society of Experimental Psychologists (2007), and the Distinguished Scientific Award for Early Career Contribution to Psychology (in the area of cognition and human learning) from the American Psychological Association (2008).

Daniel Yamins (Stanford University)

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