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09/10/2023
Mutation-based Fault Localization of Deep Neural Networks
Deep neural networks (DNNs) are susceptible to bugs, just like other typ...
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07/26/2023
What Kinds of Contracts Do ML APIs Need?
Recent work has shown that Machine Learning (ML) programs are error-pron...
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07/18/2023
An Effective Data-Driven Approach for Localizing Deep Learning Faults
Deep Learning (DL) applications are being used to solve problems in crit...
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06/15/2023
Fix Fairness, Don't Ruin Accuracy: Performance Aware Fairness Repair using AutoML
Machine learning (ML) is increasingly being used in critical decision-ma...
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06/14/2023
Characterizing Bugs in Python and R Data Analytics Programs
R and Python are among the most popular languages used in many critical ...
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12/09/2022
Decomposing a Recurrent Neural Network into Modules for Enabling Reusability and Replacement
Can we take a recurrent neural network (RNN) trained to translate betwee...
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12/08/2022
Fairify: Fairness Verification of Neural Networks
Fairness of machine learning (ML) software has become a major concern in...
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12/08/2022
Towards Understanding Fairness and its Composition in Ensemble Machine Learning
Machine Learning (ML) software has been widely adopted in modern society...
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11/30/2022
An Empirical Study on the Bugs Found while Reusing Pre-trained Natural Language Processing Models
In NLP, reusing pre-trained models instead of training from scratch has ...
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07/16/2022
On Scheduling Ring-All-Reduce Learning Jobs in Multi-Tenant GPU Clusters with Communication Contention
Powered by advances in deep learning (DL) techniques, machine learning a...
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02/02/2022
GADGET: Online Resource Optimization for Scheduling Ring-All-Reduce Learning Jobs
Fueled by advances in distributed deep learning (DDL), recent years have...
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12/07/2021
DeepDiagnosis: Automatically Diagnosing Faults and Recommending Actionable Fixes in Deep Learning Programs
Deep Neural Networks (DNNs) are used in a wide variety of applications. ...
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12/06/2021
Manas: Mining Software Repositories to Assist AutoML
Today deep learning is widely used for building software. A software eng...
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12/02/2021
The Art and Practice of Data Science Pipelines: A Comprehensive Study of Data Science Pipelines In Theory, In-The-Small, and In-The-Large
Increasingly larger number of software systems today are including data ...
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10/11/2021
A global convergence theory for deep ReLU implicit networks via over-parameterization
Implicit deep learning has received increasing attention recently due to...
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10/11/2021
Decomposing Convolutional Neural Networks into Reusable and Replaceable Modules
Training from scratch is the most common way to build a Convolutional Ne...
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03/04/2021
DeepLocalize: Fault Localization for Deep Neural Networks
Deep neural networks (DNNs) are becoming an integral part of most softwa...
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05/21/2020
Do the Machine Learning Models on a Crowd Sourced Platform Exhibit Bias? An Empirical Study on Model Fairness
Machine learning models are increasingly being used in important decisio...
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05/03/2020
BCFA: Bespoke Control Flow Analysis for CFA at Scale
Many data-driven software engineering tasks such as discovering programm...
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05/03/2020
Repairing Deep Neural Networks: Fix Patterns and Challenges
Significant interest in applying Deep Neural Network (DNN) has fueled th...
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06/27/2019
What Do Developers Ask About ML Libraries? A Large-scale Study Using Stack Overflow
Modern software systems are increasingly including machine learning (ML)...
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06/03/2019
A Comprehensive Study on Deep Learning Bug Characteristics
Deep learning has gained substantial popularity in recent years. Develop...
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05/30/2019
Identifying Classes Susceptible to Adversarial Attacks
Despite numerous attempts to defend deep learning based image classifier...
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05/16/2019
Inferring Concise Specifications of APIs
Modern software relies on libraries and uses them via application progra...
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04/30/2018
