b'Haohan Wang'

research

∙ 05/28/2023

BadLabel: A Robust Perspective on Evaluating and Enhancing Label-noise Learning

Label-noise learning (LNL) aims to increase the model's generalization g...

0 Jingfeng Zhang, et al. ∙

research

∙ 12/09/2022

Expeditious Saliency-guided Mix-up through Random Gradient Thresholding

Mix-up training approaches have proven to be effective in improving the ...

0 Minh-Long Luu, et al. ∙

research

∙ 11/30/2022

Toward Robust Diagnosis: A Contour Attention Preserving Adversarial Defense for COVID-19 Detection

As the COVID-19 pandemic puts pressure on healthcare systems worldwide, ...

0 Kun Xiang, et al. ∙

research

∙ 09/05/2022

A Principled Evaluation Protocol for Comparative Investigation of the Effectiveness of DNN Classification Models on Similar-but-non-identical Datasets

Deep Neural Network (DNN) models are increasingly evaluated using new re...

6 Esla Timothy Anzaku, et al. ∙

research

∙ 07/18/2022

Robustar: Interactive Toolbox Supporting Precise Data Annotation for Robust Vision Learning

We introduce the initial release of our software Robustar, which aims to...

0 Chonghan Chen, et al. ∙

research

∙ 07/18/2022

MRCLens: an MRC Dataset Bias Detection Toolkit

Many recent neural models have shown remarkable empirical results in Mac...

23 Yifan Zhong, et al. ∙

research

∙ 06/26/2022

Efficiently Leveraging Multi-level User Intent for Session-based Recommendation via Atten-Mixer Network

Session-based recommendation (SBR) aims to predict the user next action ...

0 Peiyan Zhang, et al. ∙

research

∙ 06/04/2022

Toward Learning Robust and Invariant Representations with Alignment Regularization and Data Augmentation

Data augmentation has been proven to be an effective technique for devel...

25 Haohan Wang, et al. ∙

research

∙ 04/09/2022

The Two Dimensions of Worst-case Training and the Integrated Effect for Out-of-domain Generalization

Training with an emphasis on "hard-to-learn" components of the data has ...

4 Zeyi Huang, et al. ∙

research

∙ 12/15/2021

Measure and Improve Robustness in NLP Models: A Survey

As NLP models achieved state-of-the-art performances over benchmarks and...

0 Xuezhi Wang, et al. ∙

research

∙ 11/05/2021

Toward Learning Human-aligned Cross-domain Robust Models by Countering Misaligned Features

Machine learning has demonstrated remarkable prediction accuracy over i....

4 Haohan Wang, et al. ∙

research

∙ 11/05/2021

Tradeoffs of Linear Mixed Models in Genome-wide Association Studies

Motivated by empirical arguments that are well-known from the genome-wid...

0 Haohan Wang, et al. ∙

research

∙ 10/27/2021

Robust Contrastive Learning Using Negative Samples with Diminished Semantics

Unsupervised learning has recently made exceptional progress because of ...

6 Songwei Ge, et al. ∙

research

∙ 02/24/2021

Enabling the Network to Surf the Internet

Few-shot learning is challenging due to the limited data and labels. Exi...

0 Zhuoling Li, et al. ∙

research

∙ 02/24/2021

Active Learning to Classify Macromolecular Structures in situ for Less Supervision in Cryo-Electron Tomography

Motivation: Cryo-Electron Tomography (cryo-ET) is a 3D bioimaging tool t...

6 Xuefeng Du, et al. ∙

research

∙ 11/25/2020

Squared ℓ_2 Norm as Consistency Loss for Leveraging Augmented Data to Learn Robust and Invariant Representations

Data augmentation is one of the most popular techniques for improving th...

0 Haohan Wang, et al. ∙

research

∙ 10/20/2020

Word Shape Matters: Robust Machine Translation with Visual Embedding

Neural machine translation has achieved remarkable empirical performance...

6 Haohan Wang, et al. ∙

research

∙ 07/05/2020

Self-Challenging Improves Cross-Domain Generalization

Convolutional Neural Networks (CNN) conduct image classification by acti...

0 Zeyi Huang, et al. ∙

research

∙ 08/28/2019

Unlearn Dataset Bias in Natural Language Inference by Fitting the Residual

Statistical natural language inference (NLI) models are susceptible to l...

0 He He, et al. ∙

research

∙ 05/29/2019

Learning Robust Global Representations by Penalizing Local Predictive Power

Despite their renowned predictive power on i.i.d. data, convolutional ne...

4 Haohan Wang, et al. ∙

research

∙ 05/28/2019

High Frequency Component Helps Explain the Generalization of Convolutional Neural Networks

We investigate the relationship between the frequency spectrum of image ...

0 Haohan Wang, et al. ∙

research

∙ 09/07/2018

What If We Simply Swap the Two Text Fragments? A Straightforward yet Effective Way to Test the Robustness of Methods to Confounding Signals in Nature Language Inference Tasks

Nature language inference (NLI) task is a predictive task of determining...

0 Haohan Wang, et al. ∙

research

∙ 03/20/2018

Fair Deep Learning Prediction for Healthcare Applications with Confounder Filtering

The rapid development of deep learning methods has permitted the fast an...

0 Zhenglin Wu, et al. ∙

research

∙ 02/02/2018

Deep Learning for Genomics: A Concise Overview

Advancements in genomic research such as high-throughput sequencing tech...

0 Tianwei Yue, et al. ∙

research

∙ 11/22/2017

Sparse Variable Selection on High Dimensional Heterogeneous Data with Tree Structured Responses

We consider the problem of sparse variable selection on high dimension h...

0 Xiang Liu, et al. ∙

research

∙ 11/11/2017

A Sparse Graph-Structured Lasso Mixed Model for Genetic Association with Confounding Correction

While linear mixed model (LMM) has shown a competitive performance in co...

0 Wenting Ye, et al. ∙

research

∙ 02/24/2017

On the Origin of Deep Learning

This paper is a review of the evolutionary history of deep learning mode...

0 Haohan Wang, et al. ∙

research

∙ 11/29/2016

SeDMiD for Confusion Detection: Uncovering Mind State from Time Series Brain Wave Data

Understanding how brain functions has been an intriguing topic for years...

0 Jingkang Yang, et al. ∙

research

∙ 10/16/2015

A Survey: Time Travel in Deep Learning Space: An Introduction to Deep Learning Models and How Deep Learning Models Evolved from the Initial Ideas

This report will show the history of deep learning evolves. It will trac...

0 Haohan Wang, et al. ∙

research

∙ 09/18/2015

Evaluation of Protein-protein Interaction Predictors with Noisy Partially Labeled Data Sets

Protein-protein interaction (PPI) prediction is an important problem in ...

0 Haohan Wang, et al. ∙

research

∙ 12/09/2014

Multimodal Transfer Deep Learning with Applications in Audio-Visual Recognition

We propose a transfer deep learning (TDL) framework that can transfer th...

0 Seungwhan Moon, et al. ∙

research

∙ 07/09/2014

Discovery of Important Crossroads in Road Network using Massive Taxi Trajectories

A major problem in road network analysis is discovery of important cross...

0 Ming Xu, et al. ∙

Haohan Wang

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