Chandan K. Reddy
6 papers in the PaperMetrix corpus
Papers by this author
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Self-Supervised Hyperboloid Representations from Logical Queries over Knowledge Graphs
2021
Knowledge Graphs (KGs) are ubiquitous structures for information storage in several real-world applications such as web search, e-commerce, social networks, and biology. Querying KGs remains a foundational and challenging problem due to their size and …
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Attention-based aspect reasoning for knowledge base question answering on clinical notes
2022
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 questions about a single block of clinical …
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Self-Supervised Transformer for Sparse and Irregularly Sampled Multivariate Clinical Time-Series
2021 · arXiv (Cornell University)
Multivariate time-series data are frequently observed in critical care settings and are typically characterized by sparsity (missing information) and irregular time intervals. Existing approaches for learning representations in this domain handle these challenges by either …
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A Self-Supervised Learning-based Approach to Clustering Multivariate Time-Series Data with Missing Values (SLAC-Time): An Application to TBI Phenotyping
2023 · arXiv (Cornell University)
Self-supervised learning approaches provide a promising direction for clustering multivariate time-series data. However, real-world time-series data often include missing values, and the existing approaches require imputing missing values before clustering, which may cause extensive computations …
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H-STAR: LLM-driven Hybrid SQL-Text Adaptive Reasoning on Tables
2025
Nikhil Abhyankar, Vivek Gupta, Dan Roth, Chandan K. Reddy. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.
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Deep Reinforcement Learning for Sequence-to-Sequence Models
2019 · IEEE Transactions on Neural Networks and Learning Systems
In recent times, sequence-to-sequence (seq2seq) models have gained a lot of popularity and provide state-of-the-art performance in a wide variety of tasks, such as machine translation, headline generation, text summarization, speech-to-text conversion, and image caption …