Texas A&M University

research

∙ 07/16/2020

Kronecker Attention Networks

Attention operators have been applied on both 1-D data like texts and hi...

Hongyang Gao, et al. ∙

research

∙ 05/22/2020

Focus Longer to See Better:Recursively Refined Attention for Fine-Grained Image Classification

Deep Neural Network has shown great strides in the coarse-grained image ...

Prateek Shroff, et al. ∙

research

∙ 10/07/2019

PyODDS: An End-to-End Outlier Detection System

PyODDS is an end-to end Python system for outlier detection with databas...

Yuening Li, et al. ∙

research

∙ 06/03/2020

XGNN: Towards Model-Level Explanations of Graph Neural Networks

Graphs neural networks (GNNs) learn node features by aggregating and com...

Hao Yuan, et al. ∙

research

∙ 05/29/2020

Non-Local Graph Neural Networks

Modern graph neural networks (GNNs) learn node embeddings through multil...

Meng Liu, et al. ∙

research

∙ 07/18/2020

Deep Learning of High-Order Interactions for Protein Interface Prediction

Protein interactions are important in a broad range of biological proces...

YI LIU, et al. ∙

research

∙ 10/03/2019

Score-CAM:Improved Visual Explanations Via Score-Weighted Class Activation Mapping

Recently, more and more attention has been drawn into the internal mecha...

Haofan Wang, et al. ∙

research

∙ 12/17/2019

Accelerating PDE-constrained Inverse Solutions with Deep Learning and Reduced Order Models

Inverse problems are pervasive mathematical methods in inferring knowled...

Sheroze Sheriffdeen, et al. ∙

research

∙ 10/24/2021

Neural Embeddings of Urban Big Data Reveal Emergent Structures in Cities

In this study, we propose using a neural embedding model-graph neural ne...

Chao Fan, et al. ∙

research

∙ 02/09/2022

Reinforcement Learning with Sparse Rewards using Guidance from Offline Demonstration

A major challenge in real-world reinforcement learning (RL) is the spars...

Desik Rengarajan, et al. ∙

research

∙ 07/24/2020

Machine Learning Explanations to Prevent Overtrust in Fake News Detection

Combating fake news and misinformation propagation is a challenging task...

Sina Mohseni, et al. ∙

research

∙ 04/06/2021

Spatio-Temporal Graph Convolutional Networks for Road Network Inundation Status Prediction during Urban Flooding

The objective of this study is to predict the near-future flooding statu...

Faxi Yuan, et al. ∙

research

∙ 10/22/2020

Graph Contrastive Learning with Augmentations

Generalizable, transferrable, and robust representation learning on grap...

Yuning You, et al. ∙

research

∙ 10/16/2021

What do Compressed Large Language Models Forget? Robustness Challenges in Model Compression

Recent works have focused on compressing pre-trained language models (PL...

Mengnan Du, et al. ∙

research

∙ 04/16/2020

Network-principled deep generative models for designing drug combinations as graph sets

Combination therapy has shown to improve therapeutic efficacy while redu...

Mostafa Karimi, et al. ∙

research

∙ 01/13/2021

EventPlus: A Temporal Event Understanding Pipeline

We present EventPlus, a temporal event understanding pipeline that integ...

Mingyu Derek Ma, et al. ∙

research

∙ 10/09/2021

Towards Lifelong Learning of Multilingual Text-To-Speech Synthesis

This work presents a lifelong learning approach to train a multilingual ...

Mu Yang, et al. ∙

research

∙ 08/11/2019

AutoGAN: Neural Architecture Search for Generative Adversarial Networks

Neural architecture search (NAS) has witnessed prevailing success in ima...

Xinyu Gong, et al. ∙

research

∙ 02/21/2020

On the Search for Feedback in Reinforcement Learning

This paper addresses the problem of learning the optimal feedback policy...

Ran Wang, et al. ∙

research

∙ 12/23/2019

Learning an Interpretable Traffic Signal Control Policy

Signalized intersections are managed by controllers that assign right of...

James Ault, et al. ∙

research

∙ 08/11/2019

Deep Structured Cross-Modal Anomaly Detection

Anomaly detection is a fundamental problem in data mining field with man...

Yuening Li, et al. ∙

research

∙ 11/22/2021

Operations for Autonomous Spacecraft

Onboard autonomy technologies such as planning and scheduling, identific...

Rebecca Castano, et al. ∙

research

∙ 02/25/2020

I Am Going MAD: Maximum Discrepancy Competition for Comparing Classifiers Adaptively

The learning of hierarchical representations for image classification ha...

Haotao Wang, et al. ∙

research

∙ 08/10/2019

DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Better

We present a new end-to-end generative adversarial network (GAN) for sin...

Orest Kupyn, et al. ∙

research

∙ 10/19/2020

Topology-Aware Graph Pooling Networks

Pooling operations have shown to be effective on computer vision and nat...

Hongyang Gao, et al. ∙

research

∙ 08/29/2018

Theoretical Linear Convergence of Unfolded ISTA and its Practical Weights and Thresholds

In recent years, unfolding iterative algorithms as neural networks has b...

Xiaohan Chen, et al. ∙

research

∙ 06/06/2019

Acceleration of Radiation Transport Solves Using Artificial Neural Networks

Discontinuous Finite Element Methods (DFEM) have been widely used for so...

Mauricio Tano, et al. ∙

research

∙ 06/15/2020

A Hybrid Deep Learning Model for Predictive Flood Warning and Situation Awareness using Channel Network Sensors Data

The objective of this study is to create and test a hybrid deep learning...

Shangjia Dong, et al. ∙

research

∙ 11/11/2020

Generative and Discriminative Learning for Distorted Image Restoration

Liquify is a common technique for image editing, which can be used for i...

Yi Gu, et al. ∙

research

∙ 01/04/2022

Bringing Your Own View: Graph Contrastive Learning without Prefabricated Data Augmentations

Self-supervision is recently surging at its new frontier of graph learni...

Yuning You, et al. ∙

research

∙ 12/07/2019

DAVID: Dual-Attentional Video Deblurring

Blind video deblurring restores sharp frames from a blurry sequence with...

Junru Wu, et al. ∙

research

∙ 10/22/2020

Noise2Same: Optimizing A Self-Supervised Bound for Image Denoising

Self-supervised frameworks that learn denoising models with merely indiv...

Yaochen Xie, et al. ∙

research

∙ 02/18/2022

Enhanced Multi-Objective A* Using Balanced Binary Search Trees

This work addresses the Multi-Objective Shortest Path Problem (MO-SPP): ...

Zhongqiang Ren, et al. ∙

research

∙ 08/02/2019

Network Shrinkage Estimation

Networks are a natural representation of complex systems across the scie...

Nesreen K. Ahmed, et al. ∙

research

∙ 07/28/2021

Neural Network Approximation of Refinable Functions

In the desire to quantify the success of neural networks in deep learnin...

Ingrid Daubechies, et al. ∙

research

∙ 08/02/2021

Multi-Objective Path-Based D* Lite

Incremental graph search algorithms, such as D* Lite, reuse previous sea...

Zhongqiang Ren, et al. ∙

research

∙ 09/27/2021

Learning from Small Samples: Transformation-Invariant SVMs with Composition and Locality at Multiple Scales

Motivated by the problem of learning when the number of training samples...

Tao Liu, et al. ∙

research

∙ 11/20/2021

Inter-Domain Fusion for Enhanced Intrusion Detection in Power Systems: An Evidence Theoretic and Meta-Heuristic Approach

False alerts due to misconfigured/ compromised IDS in ICS networks can l...

Abhijeet Sahu, et al. ∙

research

∙ 10/10/2019

RLCard: A Toolkit for Reinforcement Learning in Card Games

RLCard is an open-source toolkit for reinforcement learning research in ...

Daochen Zha, et al. ∙

research

∙ 11/29/2017

Predicting Depression Severity by Multi-Modal Feature Engineering and Fusion

We present our preliminary work to determine if patient's vocal acoustic...

Aven Samareh, et al. ∙

research

∙ 09/12/2017

End-to-End United Video Dehazing and Detection

The recent development of CNN-based image dehazing has revealed the effe...

Boyi Li, et al. ∙

research

∙ 09/10/2017

Robust Emotion Recognition from Low Quality and Low Bit Rate Video: A Deep Learning Approach

Emotion recognition from facial expressions is tremendously useful, espe...

Bowen Cheng, et al. ∙

research

∙ 07/22/2018

Towards Privacy-Preserving Visual Recognition via Adversarial Training: A Pilot Study

This paper aims to improve privacy-preserving visual recognition, an inc...

Zhenyu Wu, et al. ∙

research

∙ 06/12/2019

Privacy-Preserving Deep Visual Recognition: An Adversarial Learning Framework and A New Dataset

This paper aims to boost privacy-preserving visual recognition, an incre...

Haotao Wang, et al. ∙

research

∙ 05/07/2020

AutoSpeech: Neural Architecture Search for Speaker Recognition

Speaker recognition systems based on Convolutional Neural Networks (CNNs...

Shaojin Ding, et al. ∙

research

∙ 09/23/2021

Optimal Decision Making in High-Throughput Virtual Screening Pipelines

Effective selection of the potential candidates that meet certain condit...

Hyun-Myung Woo, et al. ∙

research

∙ 04/08/2016

Incentivizing Sharing in Realtime D2D Streaming Networks: A Mean Field Game Perspective

We consider the problem of streaming live content to a cluster of co-loc...

Jian Li, et al. ∙

research

∙ 11/28/2017

Tensor Completion Algorithms in Big Data Analytics

Tensor completion is a problem of filling the missing or unobserved entr...

Qingquan Song, et al. ∙

research

∙ 09/06/2017

Throughput Optimal Decentralized Scheduling of Multi-Hop Networks with End-to-End Deadline Constraints: II Wireless Networks with Interference

Consider a multihop wireless network serving multiple flows in which wir...

Rahul Singh, et al. ∙

research

∙ 05/18/2017

Spatial Variational Auto-Encoding via Matrix-Variate Normal Distributions

The key idea of variational auto-encoders (VAEs) resembles that of tradi...

Zhengyang Wang, et al. ∙

Texas A&M University

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