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03/01/2023
R-U-SURE? Uncertainty-Aware Code Suggestions By Maximizing Utility Across Random User Intents
Large language models show impressive results at predicting structured t...
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08/15/2022
A Library for Representing Python Programs as Graphs for Machine Learning
Graph representations of programs are commonly a central element of mach...
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08/09/2022
Learning to Improve Code Efficiency
Improvements in the performance of computing systems, driven by Moore's ...
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06/26/2022
Repository-Level Prompt Generation for Large Language Models of Code
With the success of large language models (LLMs) of code and their use a...
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03/07/2022
Static Prediction of Runtime Errors by Learning to Execute Programs with External Resource Descriptions
The execution behavior of a program often depends on external resources,...
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11/11/2021
Learning Generalized Gumbel-max Causal Mechanisms
To perform counterfactual reasoning in Structural Causal Models (SCMs), ...
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07/16/2021
Beyond In-Place Corruption: Insertion and Deletion In Denoising Probabilistic Models
Denoising diffusion probabilistic models (DDPMs) have shown impressive r...
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07/07/2021
Structured Denoising Diffusion Models in Discrete State-Spaces
Denoising diffusion probabilistic models (DDPMs) (Ho et al. 2020) have s...
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06/14/2021
Learning to Combine Per-Example Solutions for Neural Program Synthesis
The goal of program synthesis from examples is to find a computer progra...
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05/28/2021
Learning to Extend Program Graphs to Work-in-Progress Code
Source code spends most of its time in a broken or incomplete state duri...
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10/23/2020
Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks
Graph neural networks (GNNs) have emerged as a powerful tool for learnin...
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07/09/2020
Learning Graph Structure With A Finite-State Automaton Layer
Graph-based neural network models are producing strong results in a numb...
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07/02/2020
Software Engineering Event Modeling using Relative Time in Temporal Knowledge Graphs
We present a multi-relational temporal Knowledge Graph based on the dail...
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06/15/2020
Gradient Estimation with Stochastic Softmax Tricks
The Gumbel-Max trick is the basis of many relaxed gradient estimators. T...
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03/26/2020
On-the-Fly Adaptation of Source Code Models using Meta-Learning
The ability to adapt to unseen, local contexts is an important challenge...
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11/04/2019
Learning to Fix Build Errors with Graph2Diff Neural Networks
Professional software developers spend a significant amount of time fixi...
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06/27/2019
Fast Training of Sparse Graph Neural Networks on Dense Hardware
Graph neural networks have become increasingly popular in recent years d...
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06/17/2019
Learning Execution through Neural Code Fusion
As the performance of computer systems stagnates due to the end of Moore...
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06/14/2019
Direct Policy Gradients: Direct Optimization of Policies in Discrete Action Spaces
Direct optimization is an appealing approach to differentiating through ...
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04/04/2019
Neural Networks for Modeling Source Code Edits
Programming languages are emerging as a challenging and interesting doma...
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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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02/10/2017
Batch Policy Gradient Methods for Improving Neural Conversation Models
We study reinforcement learning of chatbots with recurrent neural networ...
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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
TerpreT: A Probabilistic Programming Language for Program Induction
We study machine learning formulations of inductive program synthesis; g...
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11/17/2015
Gated Graph Sequence Neural Networks
Graph-structured data appears frequently in domains including chemistry,...
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12/10/2014
Candidate Constrained CRFs for Loss-Aware Structured Prediction
When evaluating computer vision systems, we are often concerned with per...
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10/31/2014
A* Sampling
The problem of drawing samples from a discrete distribution can be conve...
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10/27/2014
Consensus Message Passing for Layered Graphical Models
Generative models provide a powerful framework for probabilistic reasoni...
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01/02/2014
Structured Generative Models of Natural Source Code
We study the problem of building generative models of natural source cod...
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12/19/2013
Detecting Parameter Symmetries in Probabilistic Models
Probabilistic models often have parameters that can be translated, scale...
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09/26/2013
Tighter Linear Program Relaxations for High Order Graphical Models
Graphical models with High Order Potentials (HOPs) have received conside...
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10/16/2012
Fast Exact Inference for Recursive Cardinality Models
Cardinality potentials are a generally useful class of high order potent...
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05/05/2011
