b'Rishabh Singh'

research

∙ 02/03/2023

Measuring The Impact Of Programming Language Distribution

Current benchmarks for evaluating neural code models focus on only a sma...

0 Gabriel Orlanski, et al. ∙

research

∙ 11/03/2022

Robust Dependence Measure using RKHS based Uncertainty Moments and Optimal Transport

Reliable measurement of dependence between variables is essential in man...

0 Rishabh Singh, et al. ∙

research

∙ 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...

0 Rishabh Singh, et al. ∙

research

∙ 09/22/2021

Quantifying Model Predictive Uncertainty with Perturbation Theory

We propose a framework for predictive uncertainty quantification of a ne...

5 Rishabh Singh, et al. ∙

research

∙ 08/15/2021

Deep Geospatial Interpolation Networks

Interpolation in Spatio-temporal data has applications in various domain...

6 Sumit Kumar Varshney, et al. ∙

research

∙ 06/26/2021

SpreadsheetCoder: Formula Prediction from Semi-structured Context

Spreadsheet formula prediction has been an important program synthesis p...

1 Xinyun Chen, et al. ∙

research

∙ 05/19/2021

Image to Image Translation : Generating maps from satellite images

Generation of maps from satellite images is conventionally done by a ran...

25 Vaishali Ingale, et al. ∙

research

∙ 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...

16 Rishabh Singh, et al. ∙

research

∙ 12/01/2020

Latent Programmer: Discrete Latent Codes for Program Synthesis

In many sequence learning tasks, such as program synthesis and document ...

1 Joey Hong, et al. ∙

research

∙ 11/10/2020

Learning Discrete Energy-based Models via Auxiliary-variable Local Exploration

Discrete structures play an important role in applications like program ...

14 Hanjun Dai, et al. ∙

research

∙ 09/17/2020

Deep Learning Software Engineering: State of Research and Future Directions

Given the current transformative potential of research that sits at the ...

23 Prem Devanbu, et al. ∙

research

∙ 07/28/2020

BUSTLE: Bottom-up program-Synthesis Through Learning-guided Exploration

Program synthesis is challenging largely because of the difficulty of se...

4 Augustus Odena, et al. ∙

research

∙ 06/29/2020

Scaling Symbolic Methods using Gradients for Neural Model Explanation

Symbolic techniques based on Satisfiability Modulo Theory (SMT) solvers ...

1 Subham Sekhar Sahoo, et al. ∙

research

∙ 06/19/2020

Neural Program Synthesis with a Differentiable Fixer

We present a new program synthesis approach that combines an encoder-dec...

0 Matej Balog, et al. ∙

research

∙ 03/19/2020

TF-Coder: Program Synthesis for Tensor Manipulations

The success and popularity of deep learning is on the rise, partially du...

3 Kensen Shi, et al. ∙

research

∙ 02/27/2020

Towards Modular Algorithm Induction

We present a modular neural network architecture Main that learns algori...

0 Daniel A. Abolafia, et al. ∙

research

∙ 01/30/2020

Towards a Kernel based Physical Interpretation of Model Uncertainty

This paper introduces a new information theoretic framework that provide...

22 Rishabh Singh, et al. ∙

research

∙ 12/27/2019

Synthetic Datasets for Neural Program Synthesis

The goal of program synthesis is to automatically generate programs in a...

21 Richard Shin, et al. ∙

research

∙ 10/28/2019

Learning Transferable Graph Exploration

This paper considers the problem of efficient exploration of unseen envi...

12 Hanjun Dai, et al. ∙

research

∙ 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...

0 Marko Vasic, et al. ∙

research

∙ 04/12/2019

SyGuS-Comp 2018: Results and Analysis

Syntax-guided synthesis (SyGuS) is the computational problem of finding ...

0 Rajeev Alur, et al. ∙

research

∙ 04/03/2019

Neural Program Repair by Jointly Learning to Localize and Repair

Due to its potential to improve programmer productivity and software qua...

0 Marko Vasic, et al. ∙

research

∙ 01/23/2019

Neural-Guided Symbolic Regression with Semantic Prior

Symbolic regression has been shown to be quite useful in many domains fr...

12 Li Li, et al. ∙

research

∙ 07/09/2018

Robust Text-to-SQL Generation with Execution-Guided Decoding

We consider the problem of neural semantic parsing, which translates nat...

0 Chenglong Wang, et al. ∙

research

∙ 07/09/2018

Execution-Guided Neural Program Decoding

We present a neural semantic parser that translatesnatural language ques...

0 Chenglong Wang, et al. ∙

research

∙ 07/01/2018

Towards Mixed Optimization for Reinforcement Learning with Program Synthesis

Deep reinforcement learning has led to several recent breakthroughs, tho...

2 Surya Bhupatiraju, et al. ∙

research

∙ 05/11/2018

Leveraging Grammar and Reinforcement Learning for Neural Program Synthesis

Program synthesis is the task of automatically generating a program cons...

0 Rudy Bunel, et al. ∙

research

∙ 04/06/2018

Programmatically Interpretable Reinforcement Learning

We study the problem of generating interpretable and verifiable policies...

0 Abhinav Verma, et al. ∙

research

∙ 03/20/2018

Program Repair via Direct State Manipulation

The goal of program repair is to automatically fix programs to meet a sp...

0 Qinheping Hu, et al. ∙

research

∙ 03/10/2018

Learning and analyzing vector encoding of symbolic representations

We present a formal language with expressions denoting general symbol st...

0 Roland Fernandez, et al. ∙

research

∙ 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...

0 Po-Sen Huang, et al. ∙

research

∙ 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...

0 Xin Zhang, et al. ∙

research

∙ 01/14/2018

Deep Reinforcement Fuzzing

Fuzzing is the process of finding security vulnerabilities in input-proc...

0 Konstantin Böttinger, et al. ∙

research

∙ 11/29/2017

SyGuS-Comp 2017: Results and Analysis

Syntax-Guided Synthesis (SyGuS) is the computational problem of finding ...

0 Rajeev Alur, et al. ∙

research

∙ 11/20/2017

Dynamic Neural Program Embedding for Program Repair

Neural program embeddings have shown much promise recently for a variety...

0 Ke Wang, et al. ∙

research

∙ 11/20/2017

Data-Driven Feedback Generation for Introductory Programming Exercises

This paper introduces the "Search, Align, and Repair" data-driven progra...

0 Ke Wang, et al. ∙

research

∙ 11/15/2017

WebRelate: Integrating Web Data with Spreadsheets using Examples

Data integration between web sources and relational data is a key challe...

0 Jeevana Priya Inala, et al. ∙

research

∙ 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...

0 Mohit Rajpal, et al. ∙

research

∙ 10/30/2017

Semantic Code Repair using Neuro-Symbolic Transformation Networks

We study the problem of semantic code repair, which can be broadly defin...

0 Jacob Devlin, et al. ∙

research

∙ 10/21/2017

Program Synthesis using Abstraction Refinement

We present a new approach to example-guided program synthesis based on c...

0 Xinyu Wang, et al. ∙

research

∙ 10/11/2017

Neural Program Meta-Induction

Most recently proposed methods for Neural Program Induction work under t...

0 Jacob Devlin, et al. ∙

research

∙ 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...

0 Xinyu Wang, et al. ∙

research

∙ 04/14/2017

Deep API Programmer: Learning to Program with APIs

We present DAPIP, a Programming-By-Example system that learns to program...

0 Surya Bhupatiraju, et al. ∙

research

∙ 01/25/2017

Learn&Fuzz: Machine Learning for Input Fuzzing

Fuzzing consists of repeatedly testing an application with modified, or ...

0 Patrice Godefroid, et al. ∙

research

∙ 12/02/2016

Summary - TerpreT: A Probabilistic Programming Language for Program Induction

We study machine learning formulations of inductive program synthesis; t...

0 Alexander L. Gaunt, et al. ∙

research

∙ 11/06/2016

Neuro-Symbolic Program Synthesis

Recent years have seen the proposal of a number of neural architectures ...

0 Emilio Parisotto, et al. ∙

research

∙ 08/15/2016

TerpreT: A Probabilistic Programming Language for Program Induction

We study machine learning formulations of inductive program synthesis; g...

0 Alexander L. Gaunt, et al. ∙

research

∙ 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...

0 Sahil Bhatia, et al. ∙

research

∙ 04/08/2012

Automated Feedback Generation for Introductory Programming Assignments

We present a new method for automatically providing feedback for introdu...

0 Rishabh Singh, et al. ∙

Rishabh Singh

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