146 citations · 646 across the 40 of their papers we have counts for
42 papers
Graph-based Multi-ODE Neural Networks for Spatio-Temporal Traffic Forecasting
Zibo Liu, Parshin Shojaee, Chandan K Reddy
There is a recent surge in the development of spatio-temporal forecasting models in the transportation domain. Long-range traffic forecasting, however, remains a challenging task d…
SeeGULL: A Stereotype Benchmark with Broad Geo-Cultural Coverage Leveraging Generative Models
Akshita Jha, Aida Davani, Chandan K. Reddy +3
Stereotype benchmark datasets are crucial to detect and mitigate social stereotypes about groups of people in NLP models. However, existing datasets are limited in size and coverag…
Complex Logical Reasoning over Knowledge Graphs using Large Language Models
Nurendra Choudhary, Chandan K. Reddy
Reasoning over knowledge graphs (KGs) is a challenging task that requires a deep understanding of the complex relationships between entities and the underlying logic of their relat…
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…
Transformer-based Planning for Symbolic Regression
Parshin Shojaee, Kazem Meidani, Amir Barati Farimani +1
Symbolic regression (SR) is a challenging task in machine learning that involves finding a mathematical expression for a function based on its values. Recent advancements in SR hav…
Transformer-based Models for Long-Form Document Matching: Challenges and Empirical Analysis
Akshita Jha, Adithya Samavedhi, Vineeth Rakesh +2
Recent advances in the area of long document matching have primarily focused on using transformer-based models for long document encoding and matching. There are two primary challe…
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…
Text Enriched Sparse Hyperbolic Graph Convolutional Networks
Nurendra Choudhary, Nikhil Rao, Karthik Subbian +1
Heterogeneous networks, which connect informative nodes containing text with different edge types, are routinely used to store and process information in various real-world applica…
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…
A Unification Framework for Euclidean and Hyperbolic Graph Neural Networks
Mehrdad Khatir, Nurendra Choudhary, Sutanay Choudhury +2
Hyperbolic neural networks can effectively capture the inherent hierarchy of graph datasets, and consequently a powerful choice of GNNs. However, they entangle multiple incongruent…
CodeAttack: Code-Based Adversarial Attacks for Pre-trained Programming Language Models
Akshita Jha, Chandan K. Reddy
Pre-trained programming language (PL) models (such as CodeT5, CodeBERT, GraphCodeBERT, etc.,) have the potential to automate software engineering tasks involving code understanding…
Multi-Label Clinical Time-Series Generation via Conditional GAN
Chang Lu, Chandan K. Reddy, Ping Wang +2
In recent years, deep learning has been successfully adopted in a wide range of applications related to electronic health records (EHRs) such as representation learning and clinica…
Probabilistic Entity Representation Model for Reasoning over Knowledge Graphs
Nurendra Choudhary, Nikhil Rao, Sumeet Katariya +2
Logical reasoning over Knowledge Graphs (KGs) is a fundamental technique that can provide efficient querying mechanism over large and incomplete databases. Current approaches emplo…
Supervised Contrastive Learning for Interpretable Long-Form Document Matching
Akshita Jha, Vineeth Rakesh, Jaideep Chandrashekar +2
Recent advancements in deep learning techniques have transformed the area of semantic text matching. However, most state-of-the-art models are designed to operate with short docume…
Attention-based Aspect Reasoning for Knowledge Base Question Answering on Clinical Notes
Ping Wang, Tian Shi, Khushbu Agarwal +2
Question Answering (QA) in clinical notes has gained a lot of attention in the past few years. Existing machine reading comprehension approaches in clinical domain can only handle…
Fair Representation Learning using Interpolation Enabled Disentanglement
Akshita Jha, Bhanukiran Vinzamuri, Chandan K. Reddy
With the growing interest in the machine learning community to solve real-world problems, it has become crucial to uncover the hidden reasoning behind their decisions by focusing o…
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…
Self-Supervised Graph Learning with Hyperbolic Embedding for Temporal Health Event Prediction
Chang Lu, Chandan K. Reddy, Yue Ning
Electronic Health Records (EHR) have been heavily used in modern healthcare systems for recording patients' admission information to hospitals. Many data-driven approaches employ t…
Collaborative Graph Learning with Auxiliary Text for Temporal Event Prediction in Healthcare
Chang Lu, Chandan K. Reddy, Prithwish Chakraborty +2
Accurate and explainable health event predictions are becoming crucial for healthcare providers to develop care plans for patients. The availability of electronic health records (E…
Jekyll: Attacking Medical Image Diagnostics using Deep Generative Models
Neal Mangaokar, Jiameng Pu, Parantapa Bhattacharya +2
Advances in deep neural networks (DNNs) have shown tremendous promise in the medical domain. However, the deep learning tools that are helping the domain, can also be used against…