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09/26/2022
Learned Force Fields Are Ready For Ground State Catalyst Discovery
We present evidence that learned density functional theory (“DFT”) force...
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08/25/2021
ETA Prediction with Graph Neural Networks in Google Maps
Travel-time prediction constitutes a task of high importance in transpor...
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07/20/2021
Large-scale graph representation learning with very deep GNNs and self-supervision
Effectively and efficiently deploying graph neural networks (GNNs) at sc...
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03/05/2021
Generating Images with Sparse Representations
The high dimensionality of images presents architecture and sampling-eff...
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10/07/2020
Learning Mesh-Based Simulation with Graph Networks
Mesh-based simulations are central to modeling complex physical systems ...
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02/23/2020
PolyGen: An Autoregressive Generative Model of 3D Meshes
Polygon meshes are an efficient representation of 3D geometry, and are o...
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02/21/2020
Learning to Simulate Complex Physics with Graph Networks
Here we present a general framework for learning simulation, and provide...
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12/05/2019
Combining Q-Learning and Search with Amortized Value Estimates
We introduce "Search with Amortized Value Estimates" (SAVE), an approach...
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10/31/2019
Object-oriented state editing for HRL
We introduce agents that use object-oriented reasoning to consider alter...
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04/05/2019
Structured agents for physical construction
Physical construction -- the ability to compose objects, subject to phys...
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09/28/2018
Relational Forward Models for Multi-Agent Learning
The behavioral dynamics of multi-agent systems have a rich and orderly s...
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06/04/2018
Relational inductive biases, deep learning, and graph networks
Artificial intelligence (AI) has undergone a renaissance recently, makin...
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06/04/2018
Relational inductive bias for physical construction in humans and machines
While current deep learning systems excel at tasks such as object classi...
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05/07/2017
Metacontrol for Adaptive Imagination-Based Optimization
Many machine learning systems are built to solve the hardest examples of...
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12/01/2016