Search Results for author: Xiaolu Zhang

Found 17 papers, 4 papers with code

One Model for All: Large Language Models are Domain-Agnostic Recommendation Systems

no code implementations22 Oct 2023 Zuoli Tang, ZhaoXin Huan, Zihao Li, Xiaolu Zhang, Jun Hu, Chilin Fu, Jun Zhou, Chenliang Li

We expect that by mixing the user's behaviors across different domains, we can exploit the common knowledge encoded in the pre-trained language model to alleviate the problems of data sparsity and cold start problems.

Language Modelling Question Answering +2

Rethinking Memory and Communication Cost for Efficient Large Language Model Training

no code implementations9 Oct 2023 Chan Wu, Hanxiao Zhang, Lin Ju, Jinjing Huang, Youshao Xiao, ZhaoXin Huan, Siyuan Li, Fanzhuang Meng, Lei Liang, Xiaolu Zhang, Jun Zhou

In this paper, we rethink the impact of memory consumption and communication costs on the training speed of large language models, and propose a memory-communication balanced strategy set Partial Redundancy Optimizer (PaRO).

Language Modelling Large Language Model

Towards Open Temporal Graph Neural Networks

1 code implementation27 Mar 2023 Kaituo Feng, Changsheng Li, Xiaolu Zhang, Jun Zhou

This will bring two big challenges to the existing dynamic GNN methods: (i) How to dynamically propagate appropriate information in an open temporal graph, where new class nodes are often linked to old class nodes.

Class Incremental Learning Incremental Learning

BadDet: Backdoor Attacks on Object Detection

no code implementations28 May 2022 Shih-Han Chan, Yinpeng Dong, Jun Zhu, Xiaolu Zhang, Jun Zhou

We propose four kinds of backdoor attacks for object detection task: 1) Object Generation Attack: a trigger can falsely generate an object of the target class; 2) Regional Misclassification Attack: a trigger can change the prediction of a surrounding object to the target class; 3) Global Misclassification Attack: a single trigger can change the predictions of all objects in an image to the target class; and 4) Object Disappearance Attack: a trigger can make the detector fail to detect the object of the target class.

Autonomous Driving Backdoor Attack +3

Improving Generative Adversarial Networks via Adversarial Learning in Latent Space

no code implementations29 Sep 2021 Yang Li, Yichuan Mo, Liangliang Shi, Junchi Yan, Xiaolu Zhang, Jun Zhou

Although many efforts have been made in terms of backbone architecture design, loss function, and training techniques, few results have been obtained on how the sampling in latent space can affect the final performance, and existing works on latent space mainly focus on controllability.

Improving Transferability of Adversarial Patches on Face Recognition with Generative Models

no code implementations CVPR 2021 Zihao Xiao, Xianfeng Gao, Chilin Fu, Yinpeng Dong, Wei Gao, Xiaolu Zhang, Jun Zhou, Jun Zhu

However, deep CNNs are vulnerable to adversarial patches, which are physically realizable and stealthy, raising new security concerns on the real-world applications of these models.

Face Recognition

Progressive-Scale Boundary Blackbox Attack via Projective Gradient Estimation

1 code implementation10 Jun 2021 Jiawei Zhang, Linyi Li, Huichen Li, Xiaolu Zhang, Shuang Yang, Bo Li

In this paper, we show that such efficiency highly depends on the scale at which the attack is applied, and attacking at the optimal scale significantly improves the efficiency.

Face Recognition

Nonlinear Projection Based Gradient Estimation for Query Efficient Blackbox Attacks

1 code implementation25 Feb 2021 Huichen Li, Linyi Li, Xiaojun Xu, Xiaolu Zhang, Shuang Yang, Bo Li

We aim to bridge the gap between the two by investigating how to efficiently estimate gradient based on a projected low-dimensional space.

QEBA: Query-Efficient Boundary-Based Blackbox Attack

no code implementations CVPR 2020 Huichen Li, Xiaojun Xu, Xiaolu Zhang, Shuang Yang, Bo Li

Such adversarial attacks can be achieved by adding a small magnitude of perturbation to the input to mislead model prediction.

Autonomous Driving BIG-bench Machine Learning +1

Data-Free Adversarial Perturbations for Practical Black-Box Attack

no code implementations3 Mar 2020 ZhaoXin Huan, Yulong Wang, Xiaolu Zhang, Lin Shang, Chilin Fu, Jun Zhou

Adversarial examples often exhibit black-box attacking transferability, which allows that adversarial examples crafted for one model can fool another model.

Characterizing Membership Privacy in Stochastic Gradient Langevin Dynamics

no code implementations5 Oct 2019 Bingzhe Wu, Chaochao Chen, Shiwan Zhao, Cen Chen, Yuan YAO, Guangyu Sun, Li Wang, Xiaolu Zhang, Jun Zhou

Based on this framework, we demonstrate that SGLD can prevent the information leakage of the training dataset to a certain extent.

Generalization Bounds

Pruning from Scratch

1 code implementation27 Sep 2019 Yulong Wang, Xiaolu Zhang, Lingxi Xie, Jun Zhou, Hang Su, Bo Zhang, Xiaolin Hu

Network pruning is an important research field aiming at reducing computational costs of neural networks.

Network Pruning

Generalization in Generative Adversarial Networks: A Novel Perspective from Privacy Protection

no code implementations NeurIPS 2019 Bingzhe Wu, Shiwan Zhao, Chaochao Chen, Haoyang Xu, Li Wang, Xiaolu Zhang, Guangyu Sun, Jun Zhou

In this paper, we aim to understand the generalization properties of generative adversarial networks (GANs) from a new perspective of privacy protection.

Infinite Curriculum Learning for Efficiently Detecting Gastric Ulcers in WCE Images

no code implementations7 Sep 2018 Xiaolu Zhang, Shiwan Zhao, Lingxi Xie

This paper considers WCE-based gastric ulcer detection, in which the major challenge is to detect the lesions in a local region.

Binary Classification

G2C: A Generator-to-Classifier Framework Integrating Multi-Stained Visual Cues for Pathological Glomerulus Classification

no code implementations30 Jun 2018 Bingzhe Wu, Xiaolu Zhang, Shiwan Zhao, Lingxi Xie, Caihong Zeng, Zhihong Liu, Guangyu Sun

Given an input image from a specified stain, several generators are first applied to estimate its appearances in other staining methods, and a classifier follows to combine visual cues from different stains for prediction (whether it is pathological, or which type of pathology it has).

Classification Decision Making +2

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