774 citations · 2.7k across the 22 of their papers we have counts for
10 papers · 1 filter
Learning to Represent Edits
Pengcheng Yin, Graham Neubig, Miltiadis Allamanis +2
We introduce the problem of learning distributed representations of edits. By combining a "neural editor" with an "edit encoder", our models learn to represent the salient informat…
Deterministic Variational Inference for Robust Bayesian Neural Networks
Anqi Wu, Sebastian Nowozin, Edward Meeds +3
Bayesian neural networks (BNNs) hold great promise as a flexible and principled solution to deal with uncertainty when learning from finite data. Among approaches to realize probab…
Constrained Graph Variational Autoencoders for Molecule Design
Qi Liu, Miltiadis Allamanis, Marc Brockschmidt +1
Graphs are ubiquitous data structures for representing interactions between entities. With an emphasis on the use of graphs to represent chemical molecules, we explore the task of…
Generative Code Modeling with Graphs
Marc Brockschmidt, Miltiadis Allamanis, Alexander L. Gaunt +1
Generative models for source code are an interesting structured prediction problem, requiring to reason about both hard syntactic and semantic constraints as well as about natural,…
Graph Partition Neural Networks for Semi-Supervised Classification
Renjie Liao, Marc Brockschmidt, Daniel Tarlow +3
We present graph partition neural networks (GPNN), an extension of graph neural networks (GNNs) able to handle extremely large graphs. GPNNs alternate between locally propagating i…
AMPNet: Asynchronous Model-Parallel Training for Dynamic Neural Networks
Alexander L. Gaunt, Matthew A. Johnson, Maik Riechert +4
New types of machine learning hardware in development and entering the market hold the promise of revolutionizing deep learning in a manner as profound as GPUs. However, existing s…
Summary - TerpreT: A Probabilistic Programming Language for Program Induction
Alexander L. Gaunt, Marc Brockschmidt, Rishabh Singh +4
We study machine learning formulations of inductive program synthesis; that is, given input-output examples, synthesize source code that maps inputs to corresponding outputs. Our k…
Differentiable Programs with Neural Libraries
Alexander L. Gaunt, Marc Brockschmidt, Nate Kushman +1
We develop a framework for combining differentiable programming languages with neural networks. Using this framework we create end-to-end trainable systems that learn to write inte…
DeepCoder: Learning to Write Programs
Matej Balog, Alexander L. Gaunt, Marc Brockschmidt +2
We develop a first line of attack for solving programming competition-style problems from input-output examples using deep learning. The approach is to train a neural network to pr…
TerpreT: A Probabilistic Programming Language for Program Induction
Alexander L. Gaunt, Marc Brockschmidt, Rishabh Singh +4
We study machine learning formulations of inductive program synthesis; given input-output examples, we try to synthesize source code that maps inputs to corresponding outputs. Our…