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08/21/2023
Foundation Model-oriented Robustness: Robust Image Model Evaluation with Pretrained Models
Machine learning has demonstrated remarkable performance over finite dat...
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07/31/2023
Towards Trustworthy and Aligned Machine Learning: A Data-centric Survey with Causality Perspectives
The trustworthiness of machine learning has emerged as a critical topic ...
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06/10/2023
Optimizing the Collaboration Structure in Cross-Silo Federated Learning
In federated learning (FL), multiple clients collaborate to train machin...
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06/09/2023
Leveraging Large Language Models for Scalable Vector Graphics-Driven Image Understanding
Recently, large language models (LLMs) have made significant advancement...
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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...
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12/09/2022
Expeditious Saliency-guided Mix-up through Random Gradient Thresholding
Mix-up training approaches have proven to be effective in improving the ...
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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, ...
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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...
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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...
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07/18/2022
MRCLens: an MRC Dataset Bias Detection Toolkit
Many recent neural models have shown remarkable empirical results in Mac...
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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 ...
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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...
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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 ...
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12/15/2021
Measure and Improve Robustness in NLP Models: A Survey
As NLP models achieved state-of-the-art performances over benchmarks and...
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11/05/2021
Toward Learning Human-aligned Cross-domain Robust Models by Countering Misaligned Features
Machine learning has demonstrated remarkable prediction accuracy over i....
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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...
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10/27/2021
Robust Contrastive Learning Using Negative Samples with Diminished Semantics
Unsupervised learning has recently made exceptional progress because of ...
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02/24/2021
Enabling the Network to Surf the Internet
Few-shot learning is challenging due to the limited data and labels. Exi...
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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...
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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...
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10/20/2020
Word Shape Matters: Robust Machine Translation with Visual Embedding
Neural machine translation has achieved remarkable empirical performance...
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07/05/2020
Self-Challenging Improves Cross-Domain Generalization
Convolutional Neural Networks (CNN) conduct image classification by acti...
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08/28/2019
Unlearn Dataset Bias in Natural Language Inference by Fitting the Residual
Statistical natural language inference (NLI) models are susceptible to l...
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05/29/2019
Learning Robust Global Representations by Penalizing Local Predictive Power
Despite their renowned predictive power on i.i.d. data, convolutional ne...
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05/28/2019
High Frequency Component Helps Explain the Generalization of Convolutional Neural Networks
We investigate the relationship between the frequency spectrum of image ...
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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...
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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...
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02/02/2018
Deep Learning for Genomics: A Concise Overview
Advancements in genomic research such as high-throughput sequencing tech...
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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...
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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...
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02/24/2017
On the Origin of Deep Learning
This paper is a review of the evolutionary history of deep learning mode...
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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...
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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...
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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 ...
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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...
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07/09/2014