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Peter Battaglia

26 accepted papers

2023

Learning rigid dynamics with face interaction graph networks

Kelsey R Allen, Yulia Rubanova, Tatiana Lopez-Guevara, William F Whitney, Alvaro Sanchez-Gonzalez, Peter Battaglia +1
ICLR 2023top-25%

Simulating rigid collisions among arbitrary shapes is notoriously difficult due to complex geometry and the strong non-linearity of the interactions. While graph neural network (GNN)-based models are effective at learning to simulate complex physical dynamics, such as fluids, cloth and articulated b…

Cited by 34Source ↗PDF ↗Scholar ↗
2023

Pre-training via Denoising for Molecular Property Prediction

Sheheryar Zaidi, Michael Schaarschmidt, James Martens, Hyunjik Kim, Yee Whye Teh, Alvaro Sanchez-Gonzalez +3
ICLR 2023top-25%

Many important problems involving molecular property prediction from 3D structures have limited data, posing a generalization challenge for neural networks. In this paper, we describe a pre-training technique based on denoising that achieves a new state-of-the-art in molecular property prediction by…

Cited by 145Source ↗PDF ↗code ↗Scholar ↗
2022

Constraint-based graph network simulator

Yulia Rubanova, Alvaro Sanchez-Gonzalez, Tobias Pfaff, Peter Battaglia
ICML 2022spotlight

In the area of physical simulations, nearly all neural-network-based methods directly predict future states from the input states. However, many traditional simulation engines instead model the constraints of the system and select the state which satisfies them. Here we present a framework for const…

Cited by 38Source ↗PDF ↗Scholar ↗
2022

Graph network simulators can learn discontinuous, rigid contact dynamics

Kelsey R Allen, Tatiana Lopez Guevara, Yulia Rubanova, Kim Stachenfeld, Alvaro Sanchez-Gonzalez, Peter Battaglia +1
CoRL 2022poster

Recent years have seen a rise in techniques for modeling discontinuous dynamics, such as rigid contact or switching motion modes, using deep learning. A common claim is that deep networks are incapable of accurately modeling rigid-body dynamics without explicit modules for handling contacts, due to…

Cited by 45Source ↗Scholar ↗
2022

Inverse Design for Fluid-Structure Interactions using Graph Network Simulators

Kelsey R Allen, Tatiana Lopez-Guavara, Kim Stachenfeld, Alvaro Sanchez-Gonzalez, Peter Battaglia, Jessica B Hamrick +1
NeurIPS 2022accept

Designing physical artifacts that serve a purpose---such as tools and other functional structures---is central to engineering as well as everyday human behavior. Though automating design using machine learning has tremendous promise, existing methods are often limited by the task-dependent distribut…

Cited by 20Source ↗PDF ↗Scholar ↗
2022

Learned Simulators for Turbulence

Kim Stachenfeld, Drummond Buschman Fielding, Dmitrii Kochkov, Miles Cranmer, Tobias Pfaff, Jonathan Godwin +4
ICLR 2022poster

Turbulence simulation with classical numerical solvers requires high-resolution grids to accurately resolve dynamics. Here we train learned simulators at low spatial and temporal resolutions to capture turbulent dynamics generated at high resolution. We show that our proposed model can simulate tur…

Cited by 47Source ↗PDF ↗Scholar ↗
2022

Simple GNN Regularisation for 3D Molecular Property Prediction and Beyond

Jonathan Godwin, Michael Schaarschmidt, Alexander L Gaunt, Alvaro Sanchez-Gonzalez, Yulia Rubanova, Petar Veličković +2
ICLR 2022poster

In this paper we show that simple noisy regularisation can be an effective way to address oversmoothing. We first argue that regularisers ad-dressing oversmoothing should both penalise node latent similarity and encourage meaningful node representations. From this observation we derive “Noisy Nodes”…

Cited by 147Source ↗PDF ↗Scholar ↗
2021

Learning Mesh-Based Simulation with Graph Networks

Tobias Pfaff, Meire Fortunato, Alvaro Sanchez-Gonzalez, Peter Battaglia
ICLR 2021spotlight

Mesh-based simulations are central to modeling complex physical systems in many disciplines across science and engineering. Mesh representations support powerful numerical integration methods and their resolution can be adapted to strike favorable trade-offs between accuracy and efficiency. However,…

Cited by 1101Source ↗PDF ↗code ↗Scholar ↗
2020

Discovering Symbolic Models from Deep Learning with Inductive Biases

Miles Cranmer, Alvaro Sanchez Gonzalez, Peter Battaglia, Rui Xu, Kyle Cranmer, David Spergel +1
NeurIPS 2020poster

We develop a general approach to distill symbolic representations of a learned deep model by introducing strong inductive biases. We focus on Graph Neural Networks (GNNs). The technique works as follows: we first encourage sparse latent representations when we train a GNN in a supervised setting, th…

Cited by 669Source ↗PDF ↗code ↗Scholar ↗
2020

Learning to Simulate Complex Physics with Graph Networks

Alvaro Sanchez-Gonzalez, Jonathan Godwin, Tobias Pfaff, Rex Ying, Jure Leskovec, Peter Battaglia
ICML 2020poster

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. Our framework—which we term "Graph Network-based Simulators" (GNS)—r…

Cited by 1440Source ↗PDF ↗code ↗Scholar ↗
2020

PolyGen: An Autoregressive Generative Model of 3D Meshes

Charlie Nash, Yaroslav Ganin, S. M. Ali Eslami, Peter Battaglia
ICML 2020poster

Polygon meshes are an efficient representation of 3D geometry, and are of central importance in computer graphics, robotics and games development. Existing learning-based approaches for object synthesis have avoided the challenges of working with 3D meshes, instead using alternative object represent…

Cited by 307Source ↗PDF ↗code ↗Scholar ↗
2019

CompILE: Compositional Imitation Learning and Execution

Thomas Kipf, Yujia Li, Hanjun Dai, Vinicius Zambaldi, Alvaro Sanchez-Gonzalez, Edward Grefenstette +2
ICML 2019oral

We introduce Compositional Imitation Learning and Execution (CompILE): a framework for learning reusable, variable-length segments of hierarchically-structured behavior from demonstration data. CompILE uses a novel unsupervised, fully-differentiable sequence segmentation module to learn latent encod…

Cited by 163Source ↗PDF ↗code ↗Scholar ↗
2019

Deep reinforcement learning with relational inductive biases

Vinicius Zambaldi, David Raposo, Adam Santoro, Victor Bapst, Yujia Li, Igor Babuschkin +10
ICLR 2019poster

We introduce an approach for augmenting model-free deep reinforcement learning agents with a mechanism for relational reasoning over structured representations, which improves performance, learning efficiency, generalization, and interpretability. Our architecture encodes an image as a set of vector…

Cited by 265Source ↗PDF ↗Scholar ↗
2019

Hyperbolic Attention Networks

Caglar Gulcehre, Misha Denil, Mateusz Malinowski, Ali Razavi, Razvan Pascanu, Karl Moritz Hermann +5
ICLR 2019poster

Recent approaches have successfully demonstrated the benefits of learning the parameters of shallow networks in hyperbolic space. We extend this line of work by imposing hyperbolic geometry on the embeddings used to compute the ubiquitous attention mechanisms for different neural networks architectu…

Cited by 302Source ↗PDF ↗Scholar ↗
2019

Structured agents for physical construction

Victor Bapst, Alvaro Sanchez-Gonzalez, Carl Doersch, Kimberly Stachenfeld, Pushmeet Kohli, Peter Battaglia +1
ICML 2019oral

Physical construction—the ability to compose objects, subject to physical dynamics, to serve some function—is fundamental to human intelligence. We introduce a suite of challenging physical construction tasks inspired by how children play with blocks, such as matching a target configuration, stackin…

Cited by 126Source ↗PDF ↗Scholar ↗
2018

Graph Networks as Learnable Physics Engines for Inference and Control

Alvaro Sanchez-Gonzalez, Nicolas Heess, Jost Tobias Springenberg, Josh Merel, Martin Riedmiller, Raia Hadsell +1
ICML 2018oral

Understanding and interacting with everyday physical scenes requires rich knowledge about the structure of the world, represented either implicitly in a value or policy function, or explicitly in a transition model. Here we introduce a new class of learnable models–based on graph networks–which impl…

Cited by 794Source ↗PDF ↗Scholar ↗
2018

Learning Visual Question Answering by Bootstrapping Hard Attention

Mateusz Malinowski, Carl Doersch, Adam Santoro, Peter Battaglia
ECCV 2018poster

Attention mechanisms in biological perception are thought to select subsets of perceptual information for more sophisticated processing which would be prohibitive to perform on all sensory inputs. In computer vision, however, there has been relatively little exploration of hard attention, where some…

Cited by 132Source ↗PDF ↗Scholar ↗
2017

A simple neural network module for relational reasoning

Adam Santoro, David Raposo, David G Barrett, Mateusz Malinowski, Razvan Pascanu, Peter Battaglia +1
NeurIPS 2017spotlight

Relational reasoning is a central component of generally intelligent behavior, but has proven difficult for neural networks to learn. In this paper we describe how to use Relation Networks (RNs) as a simple plug-and-play module to solve problems that fundamentally hinge on relational reasoning. We t…

Cited by 2023Source ↗PDF ↗Scholar ↗
2017

Discovering objects and their relations from entangled scene representations

David Raposo, Adam Santoro, David Barrett, Razvan Pascanu, Timothy Lillicrap, Peter Battaglia
ICLR 2017workshop

Our world can be succinctly and compactly described as structured scenes of objects and relations. A typical room, for example, contains salient objects such as tables, chairs and books, and these objects typically relate to each other by virtue of their correlated features, such as position, functi…

Cited by 133Source ↗Scholar ↗
2017

Imagination-Augmented Agents for Deep Reinforcement Learning

Sébastien Racanière, Theophane Weber, David Reichert, Lars Buesing, Arthur Guez, Danilo Jimenez Rezende +9
NeurIPS 2017oral

We introduce Imagination-Augmented Agents (I2As), a novel architecture for deep reinforcement learning combining model-free and model-based aspects. In contrast to most existing model-based reinforcement learning and planning methods, which prescribe how a model should be used to arrive at a polic…

Cited by 763Source ↗PDF ↗Scholar ↗
2017

Learning to Perform Physics Experiments via Deep Reinforcement Learning

Misha Denil, Pulkit Agrawal, Tejas D Kulkarni, Tom Erez, Peter Battaglia, Nando de Freitas
ICLR 2017poster

When encountering novel objects, humans are able to infer a wide range of physical properties such as mass, friction and deformability by interacting with them in a goal driven way. This process of active interaction is in the same spirit as a scientist performing experiments to discover hidden fact…

Cited by 91Source ↗Scholar ↗
2017

Visual Interaction Networks: Learning a Physics Simulator from Video

Nicholas Watters, Daniel Zoran, Theophane Weber, Peter Battaglia, Razvan Pascanu, Andrea Tacchetti
NeurIPS 2017poster

From just a glance, humans can make rich predictions about the future of a wide range of physical systems. On the other hand, modern approaches from engineering, robotics, and graphics are often restricted to narrow domains or require information about the underlying state. We introduce the Visual…

Cited by 444Source ↗PDF ↗Scholar ↗
2016

Interaction Networks for Learning about Objects, Relations and Physics

Peter Battaglia, Razvan Pascanu, Matthew Lai, Danilo Jimenez Rezende, koray kavukcuoglu
NeurIPS 2016poster

Reasoning about objects, relations, and physics is central to human intelligence, and a key goal of artificial intelligence. 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 abou…

Cited by 1821Source ↗PDF ↗Scholar ↗
2016

Unsupervised Learning of 3D Structure from Images

Danilo Jimenez Rezende, S. M. Ali Eslami, Shakir Mohamed, Peter Battaglia, Max Jaderberg, Nicolas Heess
NeurIPS 2016poster

A key goal of computer vision is to recover the underlying 3D structure that gives rise to 2D observations of the world. If endowed with 3D understanding, agents can abstract away from the complexity of the rendering process to form stable, disentangled representations of scene elements. In this pap…

Cited by 466Source ↗PDF ↗Scholar ↗