34 citations · 92 across the 7 of their papers we have counts for
9 papers · 1 filter
Latent Programmer: Discrete Latent Codes for Program Synthesis
Joey Hong, David Dohan, Rishabh Singh +2
In many sequence learning tasks, such as program synthesis and document summarization, a key problem is searching over a large space of possible output sequences. We propose to lea…
Learning Discrete Energy-based Models via Auxiliary-variable Local Exploration
Hanjun Dai, Rishabh Singh, Bo Dai +2
Discrete structures play an important role in applications like program language modeling and software engineering. Current approaches to predicting complex structures typically co…
Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks
David Bieber, Charles Sutton, Hugo Larochelle +1
Graph neural networks (GNNs) have emerged as a powerful tool for learning software engineering tasks including code completion, bug finding, and program repair. They benefit from l…
Neural Program Synthesis with a Differentiable Fixer
Matej Balog, Rishabh Singh, Petros Maniatis +1
We present a new program synthesis approach that combines an encoder-decoder based synthesis architecture with a differentiable program fixer. Our approach is inspired from the fac…
SCELMo: Source Code Embeddings from Language Models
Rafael - Michael Karampatsis, Charles Sutton
Continuous embeddings of tokens in computer programs have been used to support a variety of software development tools, including readability, code search, and program repair. Cont…
OptTyper: Probabilistic Type Inference by Optimising Logical and Natural Constraints
Irene Vlassi Pandi, Earl T. Barr, Andrew D. Gordon +1
We present a new approach to the type inference problem for dynamic languages. Our goal is to combine \emph{logical} constraints, that is, deterministic information from a type sys…