Minhao Cheng | DeepAI

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∙ 07/19/2023

Backdoor Attack against Object Detection with Clean Annotation

Deep neural networks (DNNs) have shown unprecedented success in object d...

0 Yize Cheng, et al. ∙

∙ 05/03/2023

Backdoor Learning on Sequence to Sequence Models

Backdoor learning has become an emerging research area towards building ...

0 Lichang Chen, et al. ∙

∙ 05/03/2023

PTP: Boosting Stability and Performance of Prompt Tuning with Perturbation-Based Regularizer

Recent studies show that prompt tuning can better leverage the power of ...

0 Lichang Chen, et al. ∙

∙ 02/03/2023

Revisiting Personalized Federated Learning: Robustness Against Backdoor Attacks

In this work, besides improving prediction accuracy, we study whether pe...

0 Zeyu Qin, et al. ∙

∙ 11/10/2022

MSDT: Masked Language Model Scoring Defense in Text Domain

Pre-trained language models allowed us to process downstream tasks with ...

0 Jaechul Roh, et al. ∙

∙ 09/27/2022

Efficient Non-Parametric Optimizer Search for Diverse Tasks

Efficient and automated design of optimizers plays a crucial role in ful...

0 Ruochen Wang, et al. ∙

∙ 07/20/2022

FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learning

Federated learning (FL) has recently attracted increasing attention from...

0 Yuanhao Xiong, et al. ∙

∙ 11/18/2021

A Review of Adversarial Attack and Defense for Classification Methods

Despite the efficiency and scalability of machine learning systems, rece...

0 Yao Li, et al. ∙

∙ 08/18/2021

RANK-NOSH: Efficient Predictor-Based Architecture Search via Non-Uniform Successive Halving

Predictor-based algorithms have achieved remarkable performance in the N...

0 Ruochen Wang, et al. ∙

∙ 08/10/2021

Rethinking Architecture Selection in Differentiable NAS

Differentiable Neural Architecture Search is one of the most popular Neu...

0 Ruochen Wang, et al. ∙

∙ 06/01/2021

Concurrent Adversarial Learning for Large-Batch Training

Large-batch training has become a commonly used technique when training ...

0 Yong Liu, et al. ∙

∙ 12/22/2020

Self-Progressing Robust Training

Enhancing model robustness under new and even adversarial environments i...

9 Minhao Cheng, et al. ∙

∙ 11/28/2020

Voting based ensemble improves robustness of defensive models

Developing robust models against adversarial perturbations has been an a...

2 Devvrit, et al. ∙

∙ 06/18/2020

DrNAS: Dirichlet Neural Architecture Search

This paper proposes a novel differentiable architecture search method by...

0 Xiangning Chen, et al. ∙

∙ 02/17/2020

CAT: Customized Adversarial Training for Improved Robustness

Adversarial training has become one of the most effective methods for im...

4 Minhao Cheng, et al. ∙

∙ 10/31/2019

Enhancing Certifiable Robustness via a Deep Model Ensemble

We propose an algorithm to enhance certified robustness of a deep model ...

0 Huan Zhang, et al. ∙

∙ 09/24/2019

Sign-OPT: A Query-Efficient Hard-label Adversarial Attack

We study the most practical problem setup for evaluating adversarial rob...

17 Minhao Cheng, et al. ∙

∙ 09/06/2019

Natural Adversarial Sentence Generation with Gradient-based Perturbation

This work proposes a novel algorithm to generate natural language advers...

0 Yu-Lun Hsieh, et al. ∙

∙ 07/12/2018

Query-Efficient Hard-label Black-box Attack:An Optimization-based Approach

We study the problem of attacking a machine learning model in the hard-l...

8 Minhao Cheng, et al. ∙

∙ 05/30/2018

Stochastic Zeroth-order Optimization via Variance Reduction method

Derivative-free optimization has become an important technique used in m...

0 Liu Liu, et al. ∙

∙ 03/03/2018

Seq2Sick: Evaluating the Robustness of Sequence-to-Sequence Models with Adversarial Examples

Crafting adversarial examples has become an important technique to evalu...

0 Minhao Cheng, et al. ∙

∙ 12/02/2017

Towards Robust Neural Networks via Random Self-ensemble

Recent studies have revealed the vulnerability of deep neural networks -...

0 Xuanqing Liu, et al. ∙

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