20 citations · 50 across the 9 of their papers we have counts for
4 papers · 1 filter
WindowSHAP: An Efficient Framework for Explaining Time-series Classifiers based on Shapley Values
Amin Nayebi, Sindhu Tipirneni, Chandan K Reddy +2
Unpacking and comprehending how black-box machine learning algorithms make decisions has been a persistent challenge for researchers and end-users. Explaining time-series predictiv…
An Empirical Comparison of Explainable Artificial Intelligence Methods for Clinical Data: A Case Study on Traumatic Brain Injury
Amin Nayebi, Sindhu Tipirneni, Brandon Foreman +2
A longstanding challenge surrounding deep learning algorithms is unpacking and understanding how they make their decisions. Explainable Artificial Intelligence (XAI) offers methods…
XLCoST: A Benchmark Dataset for Cross-lingual Code Intelligence
Ming Zhu, Aneesh Jain, Karthik Suresh +3
Recent advances in machine learning have significantly improved the understanding of source code data and achieved good performance on a number of downstream tasks. Open source rep…
StructCoder: Structure-Aware Transformer for Code Generation
Sindhu Tipirneni, Ming Zhu, Chandan K. Reddy
There has been a recent surge of interest in automating software engineering tasks using deep learning. This paper addresses the problem of code generation, where the goal is to ge…