Search Results for author: Haohan Wang

Found 36 papers, 13 papers with code

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

1 code implementation18 Jul 2022 Chonghan Chen, Haohan Wang, Leyang Hu, Yuhao Zhang, Shuguang Lyu, Jingcheng Wu, Xinnuo Li, Linjing Sun, Eric P. Xing

We introduce the initial release of our software Robustar, which aims to improve the robustness of vision classification machine learning models through a data-driven perspective.

BIG-bench Machine Learning Image Classification

MRCLens: an MRC Dataset Bias Detection Toolkit

no code implementations18 Jul 2022 Yifan Zhong, Haohan Wang, Eric P. Xing

Many recent neural models have shown remarkable empirical results in Machine Reading Comprehension, but evidence suggests sometimes the models take advantage of dataset biases to predict and fail to generalize on out-of-sample data.

Bias Detection Machine Reading Comprehension

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

no code implementations26 Jun 2022 Peiyan Zhang, Jiayan Guo, Chaozhuo Li, Yueqi Xie, Jaeboum Kim, Yan Zhang, Xing Xie, Haohan Wang, Sunghun Kim

Based on this observation, we intuitively propose to remove the GNN propagation part, while the readout module will take on more responsibility in the model reasoning process.

Session-Based Recommendations

Bear the Query in Mind: Visual Grounding with Query-conditioned Convolution

no code implementations18 Jun 2022 Chonghan Chen, Qi Jiang, Chih-Hao Wang, Noel Chen, Haohan Wang, Xiang Li, Bhiksha Raj

With our proposed QCM, the downstream fusion module receives visual features that are more discriminative and focused on the desired object described in the expression, leading to more accurate predictions.

Visual Grounding

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

1 code implementation4 Jun 2022 Haohan Wang, Zeyi Huang, Xindi Wu, Eric P. Xing

Finally, we test this simple technique we identify (worst-case data augmentation with squared l2 norm alignment regularization) and show that the benefits of this method outrun those of the specially designed methods.

Data Augmentation

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

1 code implementation9 Apr 2022 Zeyi Huang, Haohan Wang, Dong Huang, Yong Jae Lee, Eric P. Xing

Training with an emphasis on "hard-to-learn" components of the data has been proven as an effective method to improve the generalization of machine learning models, especially in the settings where robustness (e. g., generalization across distributions) is valued.

BIG-bench Machine Learning Domain Generalization

The Two Dimensions of Worst-Case Training and Their Integrated Effect for Out-of-Domain Generalization

no code implementations CVPR 2022 Zeyi Huang, Haohan Wang, Dong Huang, Yong Jae Lee, Eric P. Xing

Training with an emphasis on "hard-to-learn" components of the data has been proven as an effective method to improve the generalization of machine learning models, especially in the settings where robustness (e. g., generalization across distributions) is valued.

BIG-bench Machine Learning Domain Generalization

Measure and Improve Robustness in NLP Models: A Survey

no code implementations NAACL 2022 Xuezhi Wang, Haohan Wang, Diyi Yang

Despite robustness being an increasingly studied topic, it has been separately explored in applications like vision and NLP, with various definitions, evaluation and mitigation strategies in multiple lines of research.

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

1 code implementation5 Nov 2021 Haohan Wang, Zeyi Huang, HANLIN ZHANG, Yong Jae Lee, Eric Xing

Machine learning has demonstrated remarkable prediction accuracy over i. i. d data, but the accuracy often drops when tested with data from another distribution.

BIG-bench Machine Learning

Tradeoffs of Linear Mixed Models in Genome-wide Association Studies

no code implementations5 Nov 2021 Haohan Wang, Bryon Aragam, Eric Xing

Motivated by empirical arguments that are well-known from the genome-wide association studies (GWAS) literature, we study the statistical properties of linear mixed models (LMMs) applied to GWAS.

On the Consistency Loss for Leveraging Augmented Data to Learn Robust and Invariant Representations

no code implementations1 Jan 2021 Haohan Wang, Zeyi Huang, Xindi Wu, Eric Xing

Data augmentation is one of the most popular techniques for improving the robustness of neural networks.

Data Augmentation

Learning Robust Models by Countering Spurious Correlations

no code implementations1 Jan 2021 Haohan Wang, Zeyi Huang, Eric Xing

In this paper, we formally study the generalization error bound for this setup with the knowledge of how the spurious features are associated with the label.

Domain Adaptation

Word Shape Matters: Robust Machine Translation with Visual Embedding

no code implementations20 Oct 2020 Haohan Wang, Peiyan Zhang, Eric P. Xing

Neural machine translation has achieved remarkable empirical performance over standard benchmark datasets, yet recent evidence suggests that the models can still fail easily dealing with substandard inputs such as misspelled words, To overcome this issue, we introduce a new encoding heuristic of the input symbols for character-level NLP models: it encodes the shape of each character through the images depicting the letters when printed.

Machine Translation Translation

Self-Challenging Improves Cross-Domain Generalization

7 code implementations ECCV 2020 Zeyi Huang, Haohan Wang, Eric P. Xing, Dong Huang

We introduce a simple training heuristic, Representation Self-Challenging (RSC), that significantly improves the generalization of CNN to the out-of-domain data.

Domain Generalization Image Classification

High-Frequency Component Helps Explain the Generalization of Convolutional Neural Networks

1 code implementation CVPR 2020 Haohan Wang, Xindi Wu, Zeyi Huang, Eric P. Xing

We investigate the relationship between the frequency spectrum of image data and the generalization behavior of convolutional neural networks (CNN).

High Frequency Component Helps Explain the Generalization of Convolutional Neural Networks

1 code implementation28 May 2019 Haohan Wang, Xindi Wu, Zeyi Huang, Eric P. Xing

We investigate the relationship between the frequency spectrum of image data and the generalization behavior of convolutional neural networks (CNN).

Adversarial Attack

Learning Robust Representations by Projecting Superficial Statistics Out

no code implementations ICLR 2019 Haohan Wang, Zexue He, Zachary C. Lipton, Eric P. Xing

We test our method on the battery of standard domain generalization data sets and, interestingly, achieve comparable or better performance as compared to other domain generalization methods that explicitly require samples from the target distribution for training.

Domain Generalization

Removing Confounding Factors Associated Weights in Deep Neural Networks Improves the Prediction Accuracy for Healthcare Applications

1 code implementation20 Mar 2018 Haohan Wang, Zhenglin Wu, Eric P. Xing

The proliferation of healthcare data has brought the opportunities of applying data-driven approaches, such as machine learning methods, to assist diagnosis.

EEG

Deep Learning for Genomics: A Concise Overview

no code implementations2 Feb 2018 Tianwei Yue, Haohan Wang

Advancements in genomic research such as high-throughput sequencing techniques have driven modern genomic studies into "big data" disciplines.

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

no code implementations11 Nov 2017 Wenting Ye, Xiang Liu, Haohan Wang, Eric P. Xing

We proposed the sparse graph-structured linear mixed model (sGLMM) that can incorporate the relatedness information from traits in a dataset with confounding correction.

On the Origin of Deep Learning

no code implementations24 Feb 2017 Haohan Wang, Bhiksha Raj

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

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

no code implementations29 Nov 2016 Jingkang Yang, Haohan Wang, Jun Zhu, Eric P. Xing

In this paper, we propose an extension of State Space Model to work with different sources of information together with its learning and inference algorithms.

Time Series

Select-Additive Learning: Improving Generalization in Multimodal Sentiment Analysis

1 code implementation16 Sep 2016 Haohan Wang, Aaksha Meghawat, Louis-Philippe Morency, Eric P. Xing

In this paper, we propose a Select-Additive Learning (SAL) procedure that improves the generalizability of trained neural networks for multimodal sentiment analysis.

Multimodal Sentiment Analysis

Evaluating Protein-protein Interaction Predictors with a Novel 3-Dimensional Metric

no code implementations6 Nov 2015 Haohan Wang, Madhavi K. Ganapathiraju

In order for the predicted interactions to be directly adopted by biologists, the ma- chine learning predictions have to be of high precision, regardless of recall.

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

no code implementations18 Sep 2015 Haohan Wang, Madhavi K. Ganapathiraju

In this paper, we focused on the problem that non-availability of accurately labeled testing data sets in the domain of protein-protein interaction (PPI) prediction may lead to biased evaluation results.

Multimodal Transfer Deep Learning with Applications in Audio-Visual Recognition

no code implementations9 Dec 2014 Seungwhan Moon, Suyoun Kim, Haohan Wang

We propose a transfer deep learning (TDL) framework that can transfer the knowledge obtained from a single-modal neural network to a network with a different modality.

Video Recognition

Discovery of Important Crossroads in Road Network using Massive Taxi Trajectories

no code implementations9 Jul 2014 Ming Xu, Jianping Wu, Yiman Du, Haohan Wang, Geqi Qi, Kezhen Hu, Yun-Peng Xiao

However, none of existing approaches addresses the problem of identifying network-wide important crossroads in real road network.

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