20 citations · 50 across the 9 of their papers we have counts for
9 papers
Context-Aware Clustering using Large Language Models
Sindhu Tipirneni, Ravinarayana Adkathimar, Nurendra Choudhary +5
Despite the remarkable success of Large Language Models (LLMs) in text understanding and generation, their potential for text clustering tasks remains underexplored. We observed th…
Identifying TBI Physiological States by Clustering Multivariate Clinical Time-Series Data
Hamid Ghaderi, Brandon Foreman, Amin Nayebi +3
Determining clinically relevant physiological states from multivariate time series data with missing values is essential for providing appropriate treatment for acute conditions su…
A Self-Supervised Learning-based Approach to Clustering Multivariate Time-Series Data with Missing Values (SLAC-Time): An Application to TBI Phenotyping
Hamid Ghaderi, Brandon Foreman, Amin Nayebi +3
Self-supervised learning approaches provide a promising direction for clustering multivariate time-series data. However, real-world time-series data often include missing values, a…
Execution-based Code Generation using Deep Reinforcement Learning
Parshin Shojaee, Aneesh Jain, Sindhu Tipirneni +1
The utilization of programming language (PL) models, pre-trained on large-scale code corpora, as a means of automating software engineering processes has demonstrated considerable…
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…
Self-Supervised Transformer for Sparse and Irregularly Sampled Multivariate Clinical Time-Series
Sindhu Tipirneni, Chandan K. Reddy
Multivariate time-series data are frequently observed in critical care settings and are typically characterized by sparsity (missing information) and irregular time intervals. Exis…