P
Peter W. Battaglia
Researcher at Google
Publications - 123
Citations - 13721
Peter W. Battaglia is an academic researcher from Google. The author has contributed to research in topics: Computer science & Graph (abstract data type). The author has an hindex of 44, co-authored 104 publications receiving 9841 citations. Previous affiliations of Peter W. Battaglia include INAF & Massachusetts Institute of Technology.
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Relational inductive biases, deep learning, and graph networks
Peter W. Battaglia,Jessica B. Hamrick,Victor Bapst,Alvaro Sanchez-Gonzalez,Vinicius Zambaldi,Mateusz Malinowski,Andrea Tacchetti,David Raposo,Adam Santoro,Ryan Faulkner,Caglar Gulcehre,H. Francis Song,Andrew J. Ballard,Justin Gilmer,George E. Dahl,Ashish Vaswani,Kelsey R. Allen,Charlie Nash,Victoria Langston,Chris Dyer,Nicolas Heess,Daan Wierstra,Pushmeet Kohli,Matthew Botvinick,Oriol Vinyals,Yujia Li,Razvan Pascanu +26 more
TL;DR: It is argued that combinatorial generalization must be a top priority for AI to achieve human-like abilities, and that structured representations and computations are key to realizing this objective.
Proceedings Article
Interaction networks for learning about objects, relations and physics
TL;DR: The interaction network is introduced, a model which can reason about how objects in complex systems interact, supporting dynamical predictions, as well as inferences about the abstract properties of the system, and is implemented using deep neural networks.
Proceedings Article
A simple neural network module for relational reasoning
Adam Santoro,David Raposo,David G. T. Barrett,Mateusz Malinowski,Razvan Pascanu,Peter W. Battaglia,Timothy P. Lillicrap +6 more
TL;DR: In this paper, the authors describe how to use Relation Networks (RNs) as a simple plug-and-play module to solve problems that fundamentally hinge on relational reasoning.
Posted Content
A simple neural network module for relational reasoning
Adam Santoro,David Raposo,David G. T. Barrett,Mateusz Malinowski,Razvan Pascanu,Peter W. Battaglia,Timothy P. Lillicrap +6 more
TL;DR: This work shows how a deep learning architecture equipped with an RN module can implicitly discover and learn to reason about entities and their relations.
Journal ArticleDOI
Simulation as an engine of physical scene understanding
TL;DR: This work proposes a model based on an “intuitive physics engine,” a cognitive mechanism similar to computer engines that simulate rich physics in video games and graphics, but that uses approximate, probabilistic simulations to make robust and fast inferences in complex natural scenes where crucial information is unobserved.