107 citations · 1k across the 45 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…
Deep Learning & Software Engineering: State of Research and Future Directions
Prem Devanbu, Matthew Dwyer, Sebastian Elbaum +6
Given the current transformative potential of research that sits at the intersection of Deep Learning (DL) and Software Engineering (SE), an NSF-sponsored community workshop was co…
BUSTLE: Bottom-Up Program Synthesis Through Learning-Guided Exploration
Augustus Odena, Kensen Shi, David Bieber +3
Program synthesis is challenging largely because of the difficulty of search in a large space of programs. Human programmers routinely tackle the task of writing complex programs b…
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…
Scaling Symbolic Methods using Gradients for Neural Model Explanation
Subham Sekhar Sahoo, Subhashini Venugopalan, Li Li +2
Symbolic techniques based on Satisfiability Modulo Theory (SMT) solvers have been proposed for analyzing and verifying neural network properties, but their usage has been fairly li…