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02/03/2023
Measuring The Impact Of Programming Language Distribution
Current benchmarks for evaluating neural code models focus on only a sma...
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11/03/2022
Robust Dependence Measure using RKHS based Uncertainty Moments and Optimal Transport
Reliable measurement of dependence between variables is essential in man...
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11/03/2022
Quantifying Model Uncertainty for Semantic Segmentation using Operators in the RKHS
Deep learning models for semantic segmentation are prone to poor perform...
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09/22/2021
Quantifying Model Predictive Uncertainty with Perturbation Theory
We propose a framework for predictive uncertainty quantification of a ne...
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08/15/2021
Deep Geospatial Interpolation Networks
Interpolation in Spatio-temporal data has applications in various domain...
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06/26/2021
SpreadsheetCoder: Formula Prediction from Semi-structured Context
Spreadsheet formula prediction has been an important program synthesis p...
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05/19/2021
Image to Image Translation : Generating maps from satellite images
Generation of maps from satellite images is conventionally done by a ran...
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03/02/2021
A Kernel Framework to Quantify a Model's Local Predictive Uncertainty under Data Distributional Shifts
Traditional Bayesian approaches for model uncertainty quantification rel...
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12/01/2020
Latent Programmer: Discrete Latent Codes for Program Synthesis
In many sequence learning tasks, such as program synthesis and document ...
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11/10/2020
Learning Discrete Energy-based Models via Auxiliary-variable Local Exploration
Discrete structures play an important role in applications like program ...
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09/17/2020
Deep Learning Software Engineering: State of Research and Future Directions
Given the current transformative potential of research that sits at the ...
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07/28/2020
BUSTLE: Bottom-up program-Synthesis Through Learning-guided Exploration
Program synthesis is challenging largely because of the difficulty of se...
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06/29/2020
Scaling Symbolic Methods using Gradients for Neural Model Explanation
Symbolic techniques based on Satisfiability Modulo Theory (SMT) solvers ...
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06/19/2020
Neural Program Synthesis with a Differentiable Fixer
We present a new program synthesis approach that combines an encoder-dec...
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03/19/2020
TF-Coder: Program Synthesis for Tensor Manipulations
The success and popularity of deep learning is on the rise, partially du...
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02/27/2020
Towards Modular Algorithm Induction
We present a modular neural network architecture Main that learns algori...
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01/30/2020
Towards a Kernel based Physical Interpretation of Model Uncertainty
This paper introduces a new information theoretic framework that provide...
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12/27/2019
Synthetic Datasets for Neural Program Synthesis
The goal of program synthesis is to automatically generate programs in a...
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10/28/2019
Learning Transferable Graph Exploration
This paper considers the problem of efficient exploration of unseen envi...
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06/16/2019
MoËT: Interpretable and Verifiable Reinforcement Learning via Mixture of Expert Trees
Deep Reinforcement Learning (DRL) has led to many recent breakthroughs o...
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04/12/2019
SyGuS-Comp 2018: Results and Analysis
Syntax-guided synthesis (SyGuS) is the computational problem of finding ...
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04/03/2019
Neural Program Repair by Jointly Learning to Localize and Repair
Due to its potential to improve programmer productivity and software qua...
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01/23/2019
Neural-Guided Symbolic Regression with Semantic Prior
Symbolic regression has been shown to be quite useful in many domains fr...
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07/09/2018
Robust Text-to-SQL Generation with Execution-Guided Decoding
We consider the problem of neural semantic parsing, which translates nat...
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07/09/2018
Execution-Guided Neural Program Decoding
We present a neural semantic parser that translatesnatural language ques...
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07/01/2018
Towards Mixed Optimization for Reinforcement Learning with Program Synthesis
Deep reinforcement learning has led to several recent breakthroughs, tho...
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05/11/2018
Leveraging Grammar and Reinforcement Learning for Neural Program Synthesis
Program synthesis is the task of automatically generating a program cons...
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04/06/2018
Programmatically Interpretable Reinforcement Learning
We study the problem of generating interpretable and verifiable policies...
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03/20/2018
Program Repair via Direct State Manipulation
The goal of program repair is to automatically fix programs to meet a sp...
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03/10/2018
Learning and analyzing vector encoding of symbolic representations
We present a formal language with expressions denoting general symbol st...
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03/02/2018
Natural Language to Structured Query Generation via Meta-Learning
In conventional supervised training, a model is trained to fit all the t...
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02/21/2018
Interpreting Neural Network Judgments via Minimal, Stable, and Symbolic Corrections
The paper describes a new algorithm to generate minimal, stable, and sym...
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01/14/2018
Deep Reinforcement Fuzzing
Fuzzing is the process of finding security vulnerabilities in input-proc...
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11/29/2017
SyGuS-Comp 2017: Results and Analysis
Syntax-Guided Synthesis (SyGuS) is the computational problem of finding ...
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11/20/2017
Dynamic Neural Program Embedding for Program Repair
Neural program embeddings have shown much promise recently for a variety...
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11/20/2017
Data-Driven Feedback Generation for Introductory Programming Exercises
This paper introduces the "Search, Align, and Repair" data-driven progra...
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11/15/2017
WebRelate: Integrating Web Data with Spreadsheets using Examples
Data integration between web sources and relational data is a key challe...
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11/10/2017
Not all bytes are equal: Neural byte sieve for fuzzing
Fuzzing is a popular dynamic program analysis technique used to find vul...
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10/30/2017
Semantic Code Repair using Neuro-Symbolic Transformation Networks
We study the problem of semantic code repair, which can be broadly defin...
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10/21/2017
Program Synthesis using Abstraction Refinement
We present a new approach to example-guided program synthesis based on c...
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10/11/2017
Neural Program Meta-Induction
Most recently proposed methods for Neural Program Induction work under t...
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07/05/2017
Synthesis of Data Completion Scripts using Finite Tree Automata
In application domains that store data in a tabular format, a common tas...
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04/14/2017
Deep API Programmer: Learning to Program with APIs
We present DAPIP, a Programming-By-Example system that learns to program...
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01/25/2017
Learn&Fuzz: Machine Learning for Input Fuzzing
Fuzzing consists of repeatedly testing an application with modified, or ...
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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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11/06/2016
Neuro-Symbolic Program Synthesis
Recent years have seen the proposal of a number of neural architectures ...
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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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03/19/2016
Automated Correction for Syntax Errors in Programming Assignments using Recurrent Neural Networks
We present a method for automatically generating repair feedback for syn...
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04/08/2012
