Search Results for author: Chandan K. Reddy

Found 45 papers, 33 papers with code

Discovery of Generalizable TBI Phenotypes Using Multivariate Time-Series Clustering

no code implementations15 Jan 2024 Hamid Ghaderi, Brandon Foreman, Chandan K. Reddy, Vignesh Subbian

Importantly, while certain features varied by age, the core characteristics of TBI manifestations tied to each phenotype remain consistent across diverse populations.

Clustering Clustering Multivariate Time Series +3

ViSAGe: A Global-Scale Analysis of Visual Stereotypes in Text-to-Image Generation

no code implementations12 Jan 2024 Akshita Jha, Vinodkumar Prabhakaran, Remi Denton, Sarah Laszlo, Shachi Dave, Rida Qadri, Chandan K. Reddy, Sunipa Dev

First, we show that stereotypical attributes in ViSAGe are thrice as likely to be present in generated images of corresponding identities as compared to other attributes, and that the offensiveness of these depictions is especially higher for identities from Africa, South America, and South East Asia.

Text-to-Image Generation

SNIP: Bridging Mathematical Symbolic and Numeric Realms with Unified Pre-training

2 code implementations3 Oct 2023 Kazem Meidani, Parshin Shojaee, Chandan K. Reddy, Amir Barati Farimani

To bridge the gap, we introduce SNIP, a Symbolic-Numeric Integrated Pre-training model, which employs contrastive learning between symbolic and numeric domains, enhancing their mutual similarities in the embeddings.

Contrastive Learning Few-Shot Learning +4

SeeGULL: A Stereotype Benchmark with Broad Geo-Cultural Coverage Leveraging Generative Models

1 code implementation19 May 2023 Akshita Jha, Aida Davani, Chandan K. Reddy, Shachi Dave, Vinodkumar Prabhakaran, Sunipa Dev

Stereotype benchmark datasets are crucial to detect and mitigate social stereotypes about groups of people in NLP models.

Complex Logical Reasoning over Knowledge Graphs using Large Language Models

1 code implementation2 May 2023 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 relations.

Knowledge Graphs Logical Reasoning

Identifying TBI Physiological States by Clustering Multivariate Clinical Time-Series Data

1 code implementation23 Mar 2023 Hamid Ghaderi, Brandon Foreman, Amin Nayebi, Sindhu Tipirneni, Chandan K. Reddy, Vignesh Subbian

Determining clinically relevant physiological states from multivariate time series data with missing values is essential for providing appropriate treatment for acute conditions such as Traumatic Brain Injury (TBI), respiratory failure, and heart failure.

Clustering Imputation +2

Transformer-based Planning for Symbolic Regression

1 code implementation NeurIPS 2023 Parshin Shojaee, Kazem Meidani, Amir Barati Farimani, Chandan K. Reddy

Unlike conventional decoding strategies, TPSR enables the integration of non-differentiable feedback, such as fitting accuracy and complexity, as external sources of knowledge into the transformer-based equation generation process.

regression Symbolic Regression +1

Transformer-based Models for Long-Form Document Matching: Challenges and Empirical Analysis

no code implementations7 Feb 2023 Akshita Jha, Adithya Samavedhi, Vineeth Rakesh, Jaideep Chandrashekar, Chandan K. Reddy

Firstly, the performance gain provided by transformer-based models comes at a steep cost - both in terms of the required training time and the resource (memory and energy) consumption.

Execution-based Code Generation using Deep Reinforcement Learning

1 code implementation31 Jan 2023 Parshin Shojaee, Aneesh Jain, Sindhu Tipirneni, Chandan K. Reddy

It's important to note that PPOCoder is a task-agnostic and model-agnostic framework that can be used across different code generation tasks and PLs.

Code Completion Code Translation +5

Text Enriched Sparse Hyperbolic Graph Convolutional Networks

no code implementations6 Jul 2022 Nurendra Choudhary, Nikhil Rao, Karthik Subbian, Chandan K. Reddy

In TESH-GCN, we extract semantic node information, which successively acts as a connection signal to extract relevant nodes' local neighborhood and graph-level metapath features from the sparse adjacency tensor in a reformulated hyperbolic graph convolution layer.

Language Modelling Link Prediction

XLCoST: A Benchmark Dataset for Cross-lingual Code Intelligence

1 code implementation16 Jun 2022 Ming Zhu, Aneesh Jain, Karthik Suresh, Roshan Ravindran, Sindhu Tipirneni, Chandan K. Reddy

To the best of our knowledge, it is the largest parallel dataset for source code both in terms of size and the number of languages.

Code Search

Shopping Queries Dataset: A Large-Scale ESCI Benchmark for Improving Product Search

1 code implementation14 Jun 2022 Chandan K. Reddy, Lluís Màrquez, Fran Valero, Nikhil Rao, Hugo Zaragoza, Sambaran Bandyopadhyay, Arnab Biswas, Anlu Xing, Karthik Subbian

This paper introduces the "Shopping Queries Dataset", a large dataset of difficult Amazon search queries and results, publicly released with the aim of fostering research in improving the quality of search results.

StructCoder: Structure-Aware Transformer for Code Generation

1 code implementation10 Jun 2022 Sindhu Tipirneni, Ming Zhu, Chandan K. Reddy

This paper addresses the problem of code generation, where the goal is to generate target code given source code in a different language or a natural language description.

Code Translation Text-to-Code Generation

A Unification Framework for Euclidean and Hyperbolic Graph Neural Networks

1 code implementation9 Jun 2022 Mehrdad Khatir, Nurendra Choudhary, Sutanay Choudhury, Khushbu Agarwal, Chandan K. Reddy

Such an approach enables us to propose a hyperbolic normalization layer and to further simplify the entire hyperbolic model to a Euclidean model cascaded with our hyperbolic normalization layer.

Link Prediction Node Classification

Towards Scalable Hyperbolic Neural Networks using Taylor Series Approximations

no code implementations7 Jun 2022 Nurendra Choudhary, Chandan K. Reddy

However, their adoption in practice remains restricted due to (i) non-scalability on accelerated deep learning hardware, (ii) vanishing gradients due to the closure of hyperbolic space, and (iii) information loss due to frequent mapping between local tangent space and fully hyperbolic space.

Anatomy

CodeAttack: Code-Based Adversarial Attacks for Pre-trained Programming Language Models

2 code implementations31 May 2022 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 and code generation.

Code Translation Translation

Multi-Label Clinical Time-Series Generation via Conditional GAN

1 code implementation10 Apr 2022 Chang Lu, Chandan K. Reddy, Ping Wang, Dong Nie, Yue Ning

In this work, we propose a Multi-label Time-series GAN (MTGAN) to generate EHR and simultaneously improve the quality of uncommon disease generation.

Representation Learning Time Series +2

Probabilistic Entity Representation Model for Reasoning over Knowledge Graphs

1 code implementation NeurIPS 2021 Nurendra Choudhary, Nikhil Rao, Sumeet Katariya, Karthik Subbian, Chandan K. Reddy

Current approaches employ spatial geometries such as boxes to learn query representations that encompass the answer entities and model the logical operations of projection and intersection.

Knowledge Graph Embedding Knowledge Graphs +1

Supervised Contrastive Learning for Interpretable Long-Form Document Matching

1 code implementation20 Aug 2021 Akshita Jha, Vineeth Rakesh, Jaideep Chandrashekar, Adithya Samavedhi, Chandan K. Reddy

When handling such long documents, there are three primary challenges: (i) the presence of different contexts for the same word throughout the document, (ii) small sections of contextually similar text between two documents, but dissimilar text in the remaining parts (this defies the basic understanding of "similarity"), and (iii) the coarse nature of a single global similarity measure which fails to capture the heterogeneity of the document content.

Contrastive Learning Semantic Text Matching +1

Attention-based Aspect Reasoning for Knowledge Base Question Answering on Clinical Notes

no code implementations1 Aug 2021 Ping Wang, Tian Shi, Khushbu Agarwal, Sutanay Choudhury, Chandan K. Reddy

On the other hand, the aspects, entity and context, limit the answers by node-specific information and lead to higher precision and lower recall.

Knowledge Base Question Answering Machine Reading Comprehension

Fair Representation Learning using Interpolation Enabled Disentanglement

no code implementations31 Jul 2021 Akshita Jha, Bhanukiran Vinzamuri, Chandan K. Reddy

In this paper, we propose a novel method to address two key issues: (a) Can we simultaneously learn fair disentangled representations while ensuring the utility of the learned representation for downstream tasks, and (b)Can we provide theoretical insights into when the proposed approach will be both fair and accurate.

Disentanglement Fairness +1

Self-Supervised Transformer for Sparse and Irregularly Sampled Multivariate Clinical Time-Series

1 code implementation29 Jul 2021 Sindhu Tipirneni, Chandan K. Reddy

In addition, to tackle the problem of limited availability of labeled data (which is typically observed in many healthcare applications), STraTS utilizes self-supervision by leveraging unlabeled data to learn better representations by using time-series forecasting as an auxiliary proxy task.

Imputation Mortality Prediction +2

Self-Supervised Graph Learning with Hyperbolic Embedding for Temporal Health Event Prediction

1 code implementation9 Jun 2021 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.

Graph Learning Self-Supervised Learning

Collaborative Graph Learning with Auxiliary Text for Temporal Event Prediction in Healthcare

1 code implementation16 May 2021 Chang Lu, Chandan K. Reddy, Prithwish Chakraborty, Samantha Kleinberg, Yue Ning

Accurate and explainable health event predictions are becoming crucial for healthcare providers to develop care plans for patients.

Graph Learning

Jekyll: Attacking Medical Image Diagnostics using Deep Generative Models

no code implementations5 Apr 2021 Neal Mangaokar, Jiameng Pu, Parantapa Bhattacharya, Chandan K. Reddy, Bimal Viswanath

The potential for fraudulent claims based on such generated 'fake' medical images is significant, and we demonstrate successful attacks on both X-rays and retinal fundus image modalities.

Style Transfer Translation

T-Miner: A Generative Approach to Defend Against Trojan Attacks on DNN-based Text Classification

1 code implementation7 Mar 2021 Ahmadreza Azizi, Ibrahim Asadullah Tahmid, Asim Waheed, Neal Mangaokar, Jiameng Pu, Mobin Javed, Chandan K. Reddy, Bimal Viswanath

T-Miner employs a sequence-to-sequence (seq-2-seq) generative model that probes the suspicious classifier and learns to produce text sequences that are likely to contain the Trojan trigger.

text-classification Text Classification

Self-Supervised Hyperboloid Representations from Logical Queries over Knowledge Graphs

1 code implementation23 Dec 2020 Nurendra Choudhary, Nikhil Rao, Sumeet Katariya, Karthik Subbian, Chandan K. Reddy

Promising approaches to tackle this problem include embedding the KG units (e. g., entities and relations) in a Euclidean space such that the query embedding contains the information relevant to its results.

Anomaly Detection Knowledge Graphs +2

Differentially Private Synthetic Medical Data Generation using Convolutional GANs

1 code implementation22 Dec 2020 Amirsina Torfi, Edward A. Fox, Chandan K. Reddy

Deep learning models have demonstrated superior performance in several application problems, such as image classification and speech processing.

Image Classification Synthetic Data Generation

Question Answering with Long Multiple-Span Answers

1 code implementation Findings of the Association for Computational Linguistics 2020 Ming Zhu, Aman Ahuja, Da-Cheng Juan, Wei Wei, Chandan K. Reddy

To this end, we present MASH-QA, a Multiple Answer Spans Healthcare Question Answering dataset from the consumer health domain, where answers may need to be excerpted from multiple, non-consecutive parts of text spanned across a long document.

Question Answering Sentence

An Interpretable and Uncertainty Aware Multi-Task Framework for Multi-Aspect Sentiment Analysis

2 code implementations18 Sep 2020 Tian Shi, Ping Wang, Chandan K. Reddy

In addition, we also propose an Attention-driven Keywords Ranking (AKR) method, which can automatically discover aspect keywords and aspect-level opinion keywords from the review corpus based on the attention weights.

Extract Aspect Multi-Task Learning +3

A Simple and Effective Self-Supervised Contrastive Learning Framework for Aspect Detection

1 code implementation18 Sep 2020 Tian Shi, Liuqing Li, Ping Wang, Chandan K. Reddy

However, recent deep learning-based topic models, specifically aspect-based autoencoder, suffer from several problems, such as extracting noisy aspects and poorly mapping aspects discovered by models to the aspects of interest.

Contrastive Learning Topic Models

Self-Supervised Learning of Contextual Embeddings for Link Prediction in Heterogeneous Networks

1 code implementation22 Jul 2020 Ping Wang, Khushbu Agarwal, Colby Ham, Sutanay Choudhury, Chandan K. Reddy

Representation learning methods for heterogeneous networks produce a low-dimensional vector embedding for each node that is typically fixed for all tasks involving the node.

Link Prediction Representation Learning +1

Corpus-level and Concept-based Explanations for Interpretable Document Classification

1 code implementation24 Apr 2020 Tian Shi, Xuchao Zhang, Ping Wang, Chandan K. Reddy

In this paper, we propose a corpus-level explanation approach, which aims to capture causal relationships between keywords and model predictions via learning the importance of keywords for predicted labels across a training corpus based on attention weights.

Classification Decision Making +2

Image Generation Via Minimizing Fréchet Distance in Discriminator Feature Space

1 code implementation26 Mar 2020 Khoa D. Doan, Saurav Manchanda, Fengjiao Wang, Sathiya Keerthi, Avradeep Bhowmik, Chandan K. Reddy

We use the intuition that it is much better to train the GAN generator by minimizing the distributional distance between real and generated images in a small dimensional feature space representing such a manifold than on the original pixel-space.

Image Generation

Image Hashing by Minimizing Discrete Component-wise Wasserstein Distance

1 code implementation29 Feb 2020 Khoa D. Doan, Saurav Manchanda, Sarkhan Badirli, Chandan K. Reddy

In this paper, we show that the high sample-complexity requirement often results in sub-optimal retrieval performance of the adversarial hashing methods.

Image Retrieval Quantization +1

LATTE: Latent Type Modeling for Biomedical Entity Linking

no code implementations21 Nov 2019 Ming Zhu, Busra Celikkaya, Parminder Bhatia, Chandan K. Reddy

This is of significant importance in the biomedical domain, where it could be used to semantically annotate a large volume of clinical records and biomedical literature, to standardized concepts described in an ontology such as Unified Medical Language System (UMLS).

Entity Disambiguation Entity Linking +1

Text-to-SQL Generation for Question Answering on Electronic Medical Records

1 code implementation28 Jul 2019 Ping Wang, Tian Shi, Chandan K. Reddy

In this paper, we tackle these challenges by developing a deep learning based TRanslate-Edit Model for Question-to-SQL (TREQS) generation, which adapts the widely used sequence-to-sequence model to directly generate the SQL query for a given question, and further performs the required edits using an attentive-copying mechanism and task-specific look-up tables.

Information Retrieval Question Answering +2

LeafNATS: An Open-Source Toolkit and Live Demo System for Neural Abstractive Text Summarization

1 code implementation NAACL 2019 Tian Shi, Ping Wang, Chandan K. Reddy

Neural abstractive text summarization (NATS) has received a lot of attention in the past few years from both industry and academia.

Abstractive Text Summarization

Neural Abstractive Text Summarization with Sequence-to-Sequence Models

5 code implementations5 Dec 2018 Tian Shi, Yaser Keneshloo, Naren Ramakrishnan, Chandan K. Reddy

As part of this survey, we also develop an open source library, namely, Neural Abstractive Text Summarizer (NATS) toolkit, for the abstractive text summarization.

Abstractive Text Summarization Language Modelling +1

Deep Transfer Reinforcement Learning for Text Summarization

1 code implementation15 Oct 2018 Yaser Keneshloo, Naren Ramakrishnan, Chandan K. Reddy

Deep neural networks are data hungry models and thus face difficulties when attempting to train on small text datasets.

reinforcement-learning Reinforcement Learning (RL) +2

Deep Reinforcement Learning For Sequence to Sequence Models

3 code implementations24 May 2018 Yaser Keneshloo, Tian Shi, Naren Ramakrishnan, Chandan K. Reddy

In this survey, we consider seq2seq problems from the RL point of view and provide a formulation combining the power of RL methods in decision-making with sequence-to-sequence models that enable remembering long-term memories.

Abstractive Text Summarization Caption Generation +5

Machine Learning for Survival Analysis: A Survey

no code implementations15 Aug 2017 Ping Wang, Yan Li, Chandan K. Reddy

We hope that this paper will provide a more thorough understanding of the recent advances in survival analysis and offer some guidelines on applying these approaches to solve new problems that arise in applications with censored data.

BIG-bench Machine Learning Survival Analysis

Cannot find the paper you are looking for? You can Submit a new open access paper.