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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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10/31/2018
Learning to Represent Edits
We introduce the problem of learning distributed representations of edit...
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10/09/2018
Fixing Variational Bayes: Deterministic Variational Inference for Bayesian Neural Networks
Bayesian neural networks (BNNs) hold great promise as a flexible and pri...
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05/23/2018
Constrained Graph Variational Autoencoders for Molecule Design
Graphs are ubiquitous data structures for representing interactions betw...
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05/22/2018
Generative Code Modeling with Graphs
Generative models for source code are an interesting structured predicti...
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03/16/2018
Graph Partition Neural Networks for Semi-Supervised Classification
We present graph partition neural networks (GPNN), an extension of graph...
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05/27/2017
AMPNet: Asynchronous Model-Parallel Training for Dynamic Neural Networks
New types of machine learning hardware in development and entering the m...
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12/02/2016
Summary - TerpreT: A Probabilistic Programming Language for Program Induction
We study machine learning formulations of inductive program synthesis; t...
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08/15/2016