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09/05/2023
Diffusion Generative Inverse Design
Inverse design refers to the problem of optimizing the input of an objec...
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08/29/2023
WeatherBench 2: A benchmark for the next generation of data-driven global weather models
WeatherBench 2 is an update to the global, medium-range (1-14 day) weath...
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12/24/2022
GraphCast: Learning skillful medium-range global weather forecasting
We introduce a machine-learning (ML)-based weather simulator–called "Gra...
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12/07/2022
Learning rigid dynamics with face interaction graph networks
Simulating rigid collisions among arbitrary shapes is notoriously diffic...
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10/02/2022
MultiScale MeshGraphNets
In recent years, there has been a growing interest in using machine lear...
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05/31/2022
Pre-training via Denoising for Molecular Property Prediction
Many important problems involving molecular property prediction from 3D ...
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03/17/2022
Transframer: Arbitrary Frame Prediction with Generative Models
We present a general-purpose framework for image modelling and vision ta...
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02/04/2022
Rediscovering orbital mechanics with machine learning
We present an approach for using machine learning to automatically disco...
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02/01/2022
Physical Design using Differentiable Learned Simulators
Designing physical artifacts that serve a purpose - such as tools and ot...
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12/31/2021
Learned Coarse Models for Efficient Turbulence Simulation
Turbulence simulation with classical numerical solvers requires very hig...
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12/16/2021
Constraint-based graph network simulator
In the rapidly advancing area of learned physical simulators, nearly all...
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10/12/2021
Learning ground states of quantum Hamiltonians with graph networks
Solving for the lowest energy eigenstate of the many-body Schrodinger eq...
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06/15/2021
Very Deep Graph Neural Networks Via Noise Regularisation
Graph Neural Networks (GNNs) perform learned message passing over an inp...
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01/11/2021
A Bayesian neural network predicts the dissolution of compact planetary systems
Despite over three hundred years of effort, no solutions exist for predi...
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12/31/2020
Graph Networks with Spectral Message Passing
Graph Neural Networks (GNNs) are the subject of intense focus by the mac...
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06/19/2020
Discovering Symbolic Models from Deep Learning with Inductive Biases
We develop a general approach to distill symbolic representations of a l...
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03/10/2020
Lagrangian Neural Networks
Accurate models of the world are built upon notions of its underlying sy...
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09/27/2019
Hamiltonian Graph Networks with ODE Integrators
We introduce an approach for imposing physically informed inductive bias...
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09/12/2019
Learning Symbolic Physics with Graph Networks
We introduce an approach for imposing physically motivated inductive bia...
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01/23/2019
Causal Reasoning from Meta-reinforcement Learning
Discovering and exploiting the causal structure in the environment is a ...
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12/04/2018
Compositional Imitation Learning: Explaining and executing one task at a time
We introduce a framework for Compositional Imitation Learning and Execut...
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09/05/2018
Modeling human intuitions about liquid flow with particle-based simulation
Humans can easily describe, imagine, and, crucially, predict a wide vari...
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08/01/2018
Learning Visual Question Answering by Bootstrapping Hard Attention
Attention mechanisms in biological perception are thought to select subs...
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06/05/2018
Relational Deep Reinforcement Learning
We introduce an approach for deep reinforcement learning (RL) that impro...
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06/04/2018
Graph networks as learnable physics engines for inference and control
Understanding and interacting with everyday physical scenes requires ric...
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05/24/2018
Hyperbolic Attention Networks
We introduce hyperbolic attention networks to endow neural networks with...
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03/08/2018
Learning Deep Generative Models of Graphs
Graphs are fundamental data structures which concisely capture the relat...
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07/19/2017
Imagination-Augmented Agents for Deep Reinforcement Learning
We introduce Imagination-Augmented Agents (I2As), a novel architecture f...
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07/19/2017
Learning model-based planning from scratch
Conventional wisdom holds that model-based planning is a powerful approa...
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06/05/2017
Visual Interaction Networks
From just a glance, humans can make rich predictions about the future st...
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06/05/2017
A simple neural network module for relational reasoning
Relational reasoning is a central component of generally intelligent beh...
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11/06/2016
Learning to Perform Physics Experiments via Deep Reinforcement Learning
When encountering novel objects, humans are able to infer a wide range o...
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07/03/2016
