b'Sijia Liu'

research

∙ 08/19/2023

Robust Mixture-of-Expert Training for Convolutional Neural Networks

Sparsely-gated Mixture of Expert (MoE), an emerging deep model architect...

0 Yihua Zhang, et al. ∙

research

∙ 08/18/2023

Tensor-Compressed Back-Propagation-Free Training for (Physics-Informed) Neural Networks

Backward propagation (BP) is widely used to compute the gradients in neu...

0 Yequan Zhao, et al. ∙

research

∙ 08/14/2023

AutoSeqRec: Autoencoder for Efficient Sequential Recommendation

Sequential recommendation demonstrates the capability to recommend items...

0 Sijia Liu, et al. ∙

research

∙ 08/01/2023

An Introduction to Bi-level Optimization: Foundations and Applications in Signal Processing and Machine Learning

Recently, bi-level optimization (BLO) has taken center stage in some ver...

0 Yihua Zhang, et al. ∙

research

∙ 06/07/2023

Patch-level Routing in Mixture-of-Experts is Provably Sample-efficient for Convolutional Neural Networks

In deep learning, mixture-of-experts (MoE) activates one or few experts ...

8 Mohammed Nowaz Rabbani Chowdhury, et al. ∙

research

∙ 04/07/2023

Detecting Chinese Fake News on Twitter during the COVID-19 Pandemic

The outbreak of COVID-19 has led to a global surge of Sinophobia partly ...

0 Yongjun Zhang, et al. ∙

research

∙ 03/29/2023

A Pilot Study of Query-Free Adversarial Attack against Stable Diffusion

Despite the record-breaking performance in Text-to-Image (T2I) generatio...

17 Haomin Zhuang, et al. ∙

research

∙ 03/17/2023

Robust Mode Connectivity-Oriented Adversarial Defense: Enhancing Neural Network Robustness Against Diversified ℓ_p Attacks

Adversarial robustness is a key concept in measuring the ability of neur...

1 Ren Wang, et al. ∙

research

∙ 03/13/2023

Can Adversarial Examples Be Parsed to Reveal Victim Model Information?

Numerous adversarial attack methods have been developed to generate impe...

8 Yuguang Yao, et al. ∙

research

∙ 03/09/2023

Text-Visual Prompting for Efficient 2D Temporal Video Grounding

In this paper, we study the problem of temporal video grounding (TVG), w...

1 Yimeng Zhang, et al. ∙

research

∙ 03/07/2023

Robustness-preserving Lifelong Learning via Dataset Condensation

Lifelong learning (LL) aims to improve a predictive model as the data so...

0 Jinghan Jia, et al. ∙

research

∙ 03/04/2023

What Is Missing in IRM Training and Evaluation? Challenges and Solutions

Invariant risk minimization (IRM) has received increasing attention as a...

11 Yihua Zhang, et al. ∙

research

∙ 02/12/2023

A Theoretical Understanding of shallow Vision Transformers: Learning, Generalization, and Sample Complexity

Vision Transformers (ViTs) with self-attention modules have recently ach...

4 Hongkang Li, et al. ∙

research

∙ 02/06/2023

Joint Edge-Model Sparse Learning is Provably Efficient for Graph Neural Networks

Due to the significant computational challenge of training large-scale g...

0 Shuai Zhang, et al. ∙

research

∙ 01/26/2023

Certified Interpretability Robustness for Class Activation Mapping

Interpreting machine learning models is challenging but crucial for ensu...

4 Alex Gu, et al. ∙

research

∙ 01/20/2023

Towards Understanding How Self-training Tolerates Data Backdoor Poisoning

Recent studies on backdoor attacks in model training have shown that pol...

7 Soumyadeep Pal, et al. ∙

research

∙ 01/18/2023

Adaptively Integrated Knowledge Distillation and Prediction Uncertainty for Continual Learning

Current deep learning models often suffer from catastrophic forgetting o...

8 Kanghao Chen, et al. ∙

research

∙ 12/19/2022

Stochastic Inexact Augmented Lagrangian Method for Nonconvex Expectation Constrained Optimization

Many real-world problems not only have complicated nonconvex functional ...

0 Zichong Li, et al. ∙

research

∙ 11/21/2022

CLAWSAT: Towards Both Robust and Accurate Code Models

We integrate contrastive learning (CL) with adversarial learning to co-o...

5 Jinghan Jia, et al. ∙

research

∙ 11/21/2022

Understanding and Improving Visual Prompting: A Label-Mapping Perspective

We revisit and advance visual prompting (VP), an input prompting techniq...

9 Aochuan Chen, et al. ∙

research

∙ 10/16/2022

Data-Model-Circuit Tri-Design for Ultra-Light Video Intelligence on Edge Devices

In this paper, we propose a data-model-hardware tri-design framework for...

2 Yimeng Zhang, et al. ∙

research

∙ 10/12/2022

Visual Prompting for Adversarial Robustness

In this work, we leverage visual prompting (VP) to improve adversarial r...

21 Aochuan Chen, et al. ∙

research

∙ 10/08/2022

Advancing Model Pruning via Bi-level Optimization

The deployment constraints in practical applications necessitate the pru...

9 Yihua Zhang, et al. ∙

research

∙ 09/09/2022

Saliency Guided Adversarial Training for Learning Generalizable Features with Applications to Medical Imaging Classification System

This work tackles a central machine learning problem of performance degr...

15 Xin Li, et al. ∙

research

∙ 07/07/2022

Generalization Guarantee of Training Graph Convolutional Networks with Graph Topology Sampling

Graph convolutional networks (GCNs) have recently achieved great empiric...

6 Hongkang Li, et al. ∙

research

∙ 06/28/2022

The NLP Sandbox: an efficient model-to-data system to enable federated and unbiased evaluation of clinical NLP models

Objective The evaluation of natural language processing (NLP) models for...

13 Yao Yan, et al. ∙

research

∙ 06/15/2022

Queried Unlabeled Data Improves and Robustifies Class-Incremental Learning

Class-incremental learning (CIL) suffers from the notorious dilemma betw...

9 Tianlong Chen, et al. ∙

research

∙ 06/15/2022

Linearity Grafting: Relaxed Neuron Pruning Helps Certifiable Robustness

Certifiable robustness is a highly desirable property for adopting deep ...

4 Tianlong Chen, et al. ∙

research

∙ 06/13/2022

Distributed Adversarial Training to Robustify Deep Neural Networks at Scale

Current deep neural networks (DNNs) are vulnerable to adversarial attack...

15 Gaoyuan Zhang, et al. ∙

research

∙ 05/24/2022

Quarantine: Sparsity Can Uncover the Trojan Attack Trigger for Free

Trojan attacks threaten deep neural networks (DNNs) by poisoning them to...

4 Tianlong Chen, et al. ∙

research

∙ 05/01/2022

A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock Prediction

More and more investors and machine learning models rely on social media...

3 Yong Xie, et al. ∙

research

∙ 04/15/2022

CryoRL: Reinforcement Learning Enables Efficient Cryo-EM Data Collection

Single-particle cryo-electron microscopy (cryo-EM) has become one of the...

12 Quanfu Fan, et al. ∙

research

∙ 03/29/2022

Proactive Image Manipulation Detection

Image manipulation detection algorithms are often trained to discriminat...

1 Vishal Asnani, et al. ∙

research

∙ 03/27/2022

How to Robustify Black-Box ML Models? A Zeroth-Order Optimization Perspective

The lack of adversarial robustness has been recognized as an important i...

2 Yimeng Zhang, et al. ∙

research

∙ 03/26/2022

Reverse Engineering of Imperceptible Adversarial Image Perturbations

It has been well recognized that neural network based image classifiers ...

4 Yifan Gong, et al. ∙

research

∙ 03/12/2022

Optimizer Amalgamation

Selecting an appropriate optimizer for a given problem is of major inter...

11 Tianshu Huang, et al. ∙

research

∙ 02/15/2022

Holistic Adversarial Robustness of Deep Learning Models

Adversarial robustness studies the worst-case performance of a machine l...

1 Pin-Yu Chen, et al. ∙

research

∙ 01/21/2022

How does unlabeled data improve generalization in self-training? A one-hidden-layer theoretical analysis

Self-training, a semi-supervised learning algorithm, leverages a large a...

4 Shuai Zhang, et al. ∙

research

∙ 12/28/2021

To Supervise or Not: How to Effectively Learn Wireless Interference Management Models?

Machine learning has become successful in solving wireless interference ...

3 Bingqing Song, et al. ∙

research

∙ 12/23/2021

Revisiting and Advancing Fast Adversarial Training Through The Lens of Bi-Level Optimization

Adversarial training (AT) has become a widely recognized defense mechani...

11 Yihua Zhang, et al. ∙

research

∙ 12/08/2021

Revisiting Contrastive Learning through the Lens of Neighborhood Component Analysis: an Integrated Framework

As a seminal tool in self-supervised representation learning, contrastiv...

7 Ching-Yun Ko, et al. ∙

research

∙ 11/01/2021

When Does Contrastive Learning Preserve Adversarial Robustness from Pretraining to Finetuning?

Contrastive learning (CL) can learn generalizable feature representation...

4 Lijie Fan, et al. ∙

research

∙ 10/30/2021

RMSMP: A Novel Deep Neural Network Quantization Framework with Row-wise Mixed Schemes and Multiple Precisions

This work proposes a novel Deep Neural Network (DNN) quantization framew...

1 Sung-En Chang, et al. ∙

research

∙ 10/12/2021

Why Lottery Ticket Wins? A Theoretical Perspective of Sample Complexity on Pruned Neural Networks

The lottery ticket hypothesis (LTH) states that learning on a properly p...

28 Shuai Zhang, et al. ∙

research

∙ 09/15/2021

Sign-MAML: Efficient Model-Agnostic Meta-Learning by SignSGD

We propose a new computationally-efficient first-order algorithm for Mod...

18 Chen Fan, et al. ∙

research

∙ 07/04/2021

Certifiably Robust Interpretation via Renyi Differential Privacy

Motivated by the recent discovery that the interpretation maps of CNNs c...

2 Ao Liu, et al. ∙

research

∙ 07/01/2021

Sanity Checks for Lottery Tickets: Does Your Winning Ticket Really Win the Jackpot?

There have been long-standing controversies and inconsistencies over the...

2 Xiaolong Ma, et al. ∙

research

∙ 06/27/2021

ASK: Adversarial Soft k-Nearest Neighbor Attack and Defense

K-Nearest Neighbor (kNN)-based deep learning methods have been applied t...

19 Ren Wang, et al. ∙

research

∙ 04/28/2021

Preserving Earlier Knowledge in Continual Learning with the Help of All Previous Feature Extractors

Continual learning of new knowledge over time is one desirable capabilit...

7 Zhuoyun Li, et al. ∙

research

∙ 03/25/2021

Preserve, Promote, or Attack? GNN Explanation via Topology Perturbation

Prior works on formalizing explanations of a graph neural network (GNN) ...

10 Yi Sun, et al. ∙

Sijia Liu

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