Search Results for author: Peter W. Battaglia

Found 14 papers, 8 papers with code

ETA Prediction with Graph Neural Networks in Google Maps

no code implementations25 Aug 2021 Austin Derrow-Pinion, Jennifer She, David Wong, Oliver Lange, Todd Hester, Luis Perez, Marc Nunkesser, Seongjae Lee, Xueying Guo, Brett Wiltshire, Peter W. Battaglia, Vishal Gupta, Ang Li, Zhongwen Xu, Alvaro Sanchez-Gonzalez, Yujia Li, Petar Veličković

Travel-time prediction constitutes a task of high importance in transportation networks, with web mapping services like Google Maps regularly serving vast quantities of travel time queries from users and enterprises alike.

Graph Representation Learning

Generating Images with Sparse Representations

1 code implementation5 Mar 2021 Charlie Nash, Jacob Menick, Sander Dieleman, Peter W. Battaglia

The high dimensionality of images presents architecture and sampling-efficiency challenges for likelihood-based generative models.

Colorization Image Compression +1

Learning Mesh-Based Simulation with Graph Networks

6 code implementations ICLR 2021 Tobias Pfaff, Meire Fortunato, Alvaro Sanchez-Gonzalez, Peter W. Battaglia

Our model can be trained to pass messages on a mesh graph and to adapt the mesh discretization during forward simulation.

Numerical Integration

PolyGen: An Autoregressive Generative Model of 3D Meshes

2 code implementations ICML 2020 Charlie Nash, Yaroslav Ganin, S. M. Ali Eslami, Peter W. Battaglia

Polygon meshes are an efficient representation of 3D geometry, and are of central importance in computer graphics, robotics and games development.

3D Shape Generation Surface Reconstruction

Learning to Simulate Complex Physics with Graph Networks

9 code implementations ICML 2020 Alvaro Sanchez-Gonzalez, Jonathan Godwin, Tobias Pfaff, Rex Ying, Jure Leskovec, Peter W. Battaglia

Here we present a machine learning framework and model implementation that can learn to simulate a wide variety of challenging physical domains, involving fluids, rigid solids, and deformable materials interacting with one another.

Object-oriented state editing for HRL

no code implementations31 Oct 2019 Victor Bapst, Alvaro Sanchez-Gonzalez, Omar Shams, Kimberly Stachenfeld, Peter W. Battaglia, Satinder Singh, Jessica B. Hamrick

We introduce agents that use object-oriented reasoning to consider alternate states of the world in order to more quickly find solutions to problems.

Structured agents for physical construction

no code implementations5 Apr 2019 Victor Bapst, Alvaro Sanchez-Gonzalez, Carl Doersch, Kimberly L. Stachenfeld, Pushmeet Kohli, Peter W. Battaglia, Jessica B. Hamrick

Our results show that agents which use structured representations (e. g., objects and scene graphs) and structured policies (e. g., object-centric actions) outperform those which use less structured representations, and generalize better beyond their training when asked to reason about larger scenes.

Scene Understanding

Relational Forward Models for Multi-Agent Learning

no code implementations ICLR 2019 Andrea Tacchetti, H. Francis Song, Pedro A. M. Mediano, Vinicius Zambaldi, Neil C. Rabinowitz, Thore Graepel, Matthew Botvinick, Peter W. Battaglia

The behavioral dynamics of multi-agent systems have a rich and orderly structure, which can be leveraged to understand these systems, and to improve how artificial agents learn to operate in them.

Relational inductive bias for physical construction in humans and machines

no code implementations4 Jun 2018 Jessica B. Hamrick, Kelsey R. Allen, Victor Bapst, Tina Zhu, Kevin R. McKee, Joshua B. Tenenbaum, Peter W. Battaglia

While current deep learning systems excel at tasks such as object classification, language processing, and gameplay, few can construct or modify a complex system such as a tower of blocks.

Inductive Bias reinforcement-learning

Metacontrol for Adaptive Imagination-Based Optimization

1 code implementation7 May 2017 Jessica B. Hamrick, Andrew J. Ballard, Razvan Pascanu, Oriol Vinyals, Nicolas Heess, Peter W. Battaglia

The metacontroller component is a model-free reinforcement learning agent, which decides both how many iterations of the optimization procedure to run, as well as which model to consult on each iteration.

Decision Making

Interaction Networks for Learning about Objects, Relations and Physics

6 code implementations NeurIPS 2016 Peter W. Battaglia, Razvan Pascanu, Matthew Lai, Danilo Rezende, Koray Kavukcuoglu

Here we introduce the interaction network, 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.

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