97 citations · 379 across the 26 of their papers we have counts for
6 papers · 1 filter
Fairify: Fairness Verification of Neural Networks
Sumon Biswas, Hridesh Rajan
Fairness of machine learning (ML) software has become a major concern in the recent past. Although recent research on testing and improving fairness have demonstrated impact on rea…
Decomposing a Recurrent Neural Network into Modules for Enabling Reusability and Replacement
Sayem Mohammad Imtiaz, Fraol Batole, Astha Singh +3
Can we take a recurrent neural network (RNN) trained to translate between languages and augment it to support a new natural language without retraining the model from scratch? Can…
Towards Understanding Fairness and its Composition in Ensemble Machine Learning
Usman Gohar, Sumon Biswas, Hridesh Rajan
Machine Learning (ML) software has been widely adopted in modern society, with reported fairness implications for minority groups based on race, sex, age, etc. Many recent works ha…
An Empirical Study on the Bugs Found while Reusing Pre-trained Natural Language Processing Models
Rangeet Pan, Sumon Biswas, Mohna Chakraborty +2
In NLP, reusing pre-trained models instead of training from scratch has gained popularity; however, NLP models are mostly black boxes, very large, and often require significant res…
On Scheduling Ring-All-Reduce Learning Jobs in Multi-Tenant GPU Clusters with Communication Contention
Menglu Yu, Bo Ji, Hridesh Rajan +1
Powered by advances in deep learning (DL) techniques, machine learning and artificial intelligence have achieved astonishing successes. However, the rapidly growing needs for DL al…
GADGET: Online Resource Optimization for Scheduling Ring-All-Reduce Learning Jobs
Menglu Yu, Ye Tian, Bo Ji +3
Fueled by advances in distributed deep learning (DDL), recent years have witnessed a rapidly growing demand for resource-intensive distributed/parallel computing to process DDL com…