20 citations · 50 across the 9 of their papers we have counts for
6 papers · 1 filter
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…
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…