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05/30/2023
Graph-based Multi-ODE Neural Networks for Spatio-Temporal Traffic Forecasting
There is a recent surge in the development of spatio-temporal forecastin...
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05/19/2023
SeeGULL: A Stereotype Benchmark with Broad Geo-Cultural Coverage Leveraging Generative Models
Stereotype benchmark datasets are crucial to detect and mitigate social ...
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05/02/2023
Complex Logical Reasoning over Knowledge Graphs using Large Language Models
Reasoning over knowledge graphs (KGs) is a challenging task that require...
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03/23/2023
Self-Supervised Clustering of Multivariate Time-Series Data for Identifying TBI Physiological States
Determining clinically relevant physiological states from multivariate t...
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03/13/2023
Transformer-based Planning for Symbolic Regression
Symbolic regression (SR) is a challenging task in machine learning that ...
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02/27/2023
A Self-Supervised Learning-based Approach to Clustering Multivariate Time-Series Data with Missing Values (SLAC-Time): An Application to Traumatic Brain Injury Phenotyping
Self-supervised learning approaches provide a promising direction for cl...
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02/07/2023
Transformer-based Models for Long-Form Document Matching: Challenges and Empirical Analysis
Recent advances in the area of long document matching have primarily foc...
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01/31/2023
Execution-based Code Generation using Deep Reinforcement Learning
The utilization of programming language (PL) models, pretrained on large...
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11/11/2022
WindowSHAP: An Efficient Framework for Explaining Time-series Classifiers based on Shapley Values
Unpacking and comprehending how deep learning algorithms make decisions ...
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08/13/2022
An Empirical Comparison of Explainable Artificial Intelligence Methods for Clinical Data: A Case Study on Traumatic Brain Injury
A longstanding challenge surrounding deep learning algorithms is unpacki...
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07/06/2022
Text Enriched Sparse Hyperbolic Graph Convolutional Networks
Heterogeneous networks, which connect informative nodes containing text ...
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06/16/2022
XLCoST: A Benchmark Dataset for Cross-lingual Code Intelligence
Recent advances in machine learning have significantly improved the unde...
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06/14/2022
Shopping Queries Dataset: A Large-Scale ESCI Benchmark for Improving Product Search
Improving the quality of search results can significantly enhance users ...
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06/10/2022
StructCoder: Structure-Aware Transformer for Code Generation
There has been a recent surge of interest in automating software enginee...
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06/09/2022
Pseudo-Poincaré: A Unification Framework for Euclidean and Hyperbolic Graph Neural Networks
Hyperbolic neural networks have recently gained significant attention du...
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06/07/2022
Towards Scalable Hyperbolic Neural Networks using Taylor Series Approximations
Hyperbolic networks have shown prominent improvements over their Euclide...
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05/31/2022
CodeAttack: Code-based Adversarial Attacks for Pre-Trained Programming Language Models
Pre-trained programming language (PL) models (such as CodeT5, CodeBERT, ...
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10/26/2021
Probabilistic Entity Representation Model for Reasoning over Knowledge Graphs
Logical reasoning over Knowledge Graphs (KGs) is a fundamental technique...
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08/20/2021
Supervised Contrastive Learning for Interpretable Long Document Comparison
Recent advancements in deep learning techniques have transformed the are...
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07/31/2021
Fair Representation Learning using Interpolation Enabled Disentanglement
With the growing interest in the machine learning community to solve rea...
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07/29/2021
Self-supervised Transformer for Multivariate Clinical Time-Series with Missing Values
Multivariate time-series (MVTS) data are frequently observed in critical...
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06/09/2021
Self-Supervised Graph Learning with Hyperbolic Embedding for Temporal Health Event Prediction
Electronic Health Records (EHR) have been heavily used in modern healthc...
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05/16/2021
Collaborative Graph Learning with Auxiliary Text for Temporal Event Prediction in Healthcare
Accurate and explainable health event predictions are becoming crucial f...
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04/05/2021
Jekyll: Attacking Medical Image Diagnostics using Deep Generative Models
Advances in deep neural networks (DNNs) have shown tremendous promise in...
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03/07/2021
T-Miner: A Generative Approach to Defend Against Trojan Attacks on DNN-based Text Classification
Deep Neural Network (DNN) classifiers are known to be vulnerable to Troj...
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12/23/2020
Self-Supervised Hyperboloid Representations from Logical Queries over Knowledge Graphs
Knowledge Graphs (KGs) are ubiquitous structures for information storage...
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12/22/2020
Differentially Private Synthetic Medical Data Generation using Convolutional GANs
Deep learning models have demonstrated superior performance in several a...
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07/22/2020
Self-Supervised Learning of Contextual Embeddings for Link Prediction in Heterogeneous Networks
Representation learning methods for heterogeneous networks produce a low...
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04/24/2020
A Concept-based Abstraction-Aggregation Deep Neural Network for Interpretable Document Classification
Using attention weights to identify information that is important for mo...
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03/26/2020
Image Generation Via Minimizing Fréchet Distance in Discriminator Feature Space
For a given image generation problem, the intrinsic image manifold is of...
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02/29/2020
Image Hashing by Minimizing Independent Relaxed Wasserstein Distance
Image hashing is a fundamental problem in the computer vision domain wit...
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11/21/2019
LATTE: Latent Type Modeling for Biomedical Entity Linking
Entity linking is the task of linking mentions of named entities in natu...
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07/28/2019
A Translate-Edit Model for Natural Language Question to SQL Query Generation on Multi-relational Healthcare Data
Electronic health record (EHR) data contains most of the important patie...
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05/28/2019
LeafNATS: An Open-Source Toolkit and Live Demo System for Neural Abstractive Text Summarization
Neural abstractive text summarization (NATS) has received a lot of atten...
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12/05/2018
Neural Abstractive Text Summarization with Sequence-to-Sequence Models
In the past few years, neural abstractive text summarization with sequen...
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10/15/2018
Deep Transfer Reinforcement Learning for Text Summarization
Deep neural networks are data hungry models and thus they face difficult...
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05/24/2018
Deep Reinforcement Learning For Sequence to Sequence Models
In recent years, sequence-to-sequence (seq2seq) models are used in a var...
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08/15/2017
