Search Results for author: Zaixi Zhang

Found 18 papers, 11 papers with code

Sparse Attention-Based Neural Networks for Code Classification

no code implementations11 Nov 2023 Ziyang Xiang, Zaixi Zhang, Qi Liu

We introduce an approach named the Sparse Attention-based neural network for Code Classification (SACC) in this paper.

Classification Code Classification

AdaptSSR: Pre-training User Model with Augmentation-Adaptive Self-Supervised Ranking

1 code implementation NeurIPS 2023 Yang Yu, Qi Liu, Kai Zhang, Yuren Zhang, Chao Song, Min Hou, Yuqing Yuan, Zhihao Ye, Zaixi Zhang, Sanshi Lei Yu

Specifically, we adopt a multiple pairwise ranking loss which trains the user model to capture the similarity orders between the implicitly augmented view, the explicitly augmented view, and views from other users.

Contrastive Learning Data Augmentation

Full-Atom Protein Pocket Design via Iterative Refinement

1 code implementation NeurIPS 2023 Zaixi Zhang, Zepu Lu, Zhongkai Hao, Marinka Zitnik, Qi Liu

In the initial stage, the residue types and backbone coordinates are refined using a hierarchical context encoder, complemented by two structure refinement modules that capture both inter-residue and pocket-ligand interactions.

A Systematic Survey in Geometric Deep Learning for Structure-based Drug Design

no code implementations20 Jun 2023 Zaixi Zhang, Jiaxian Yan, Qi Liu, Enhong Chen, Marinka Zitnik

Recent developments in geometric deep learning, focusing on the integration and processing of 3D geometric data, coupled with the availability of accurate protein 3D structure predictions from tools like AlphaFold, have greatly advanced the field of structure-based drug design.

Benchmarking Drug Discovery +1

An Equivariant Generative Framework for Molecular Graph-Structure Co-Design

no code implementations12 Apr 2023 Zaixi Zhang, Qi Liu, Chee-Kong Lee, Chang-Yu Hsieh, Enhong Chen

Our extensive investigation reveals that the 2D topology and 3D geometry contain intrinsically complementary information in molecule design, and provide new insights into machine learning-based molecule representation and generation.

Drug Discovery Graph Generation +1

Backdoor Defense via Deconfounded Representation Learning

1 code implementation CVPR 2023 Zaixi Zhang, Qi Liu, Zhicai Wang, Zepu Lu, Qingyong Hu

The other clean model dedicates to capturing the desired causal effects by minimizing the mutual information with the confounding representations from the backdoored model and employing a sample-wise re-weighting scheme.

Backdoor Attack backdoor defense +1

Untargeted Attack against Federated Recommendation Systems via Poisonous Item Embeddings and the Defense

1 code implementation11 Dec 2022 Yang Yu, Qi Liu, Likang Wu, Runlong Yu, Sanshi Lei Yu, Zaixi Zhang

Experiments on two public datasets show that ClusterAttack can effectively degrade the performance of FedRec systems while circumventing many defense methods, and UNION can improve the resistance of the system against various untargeted attacks, including our ClusterAttack.

Contrastive Learning Recommendation Systems

FedRecover: Recovering from Poisoning Attacks in Federated Learning using Historical Information

no code implementations20 Oct 2022 Xiaoyu Cao, Jinyuan Jia, Zaixi Zhang, Neil Zhenqiang Gong

Existing defenses focus on preventing a small number of malicious clients from poisoning the global model via robust federated learning methods and detecting malicious clients when there are a large number of them.

Federated Learning

Hierarchical Graph Transformer with Adaptive Node Sampling

1 code implementation8 Oct 2022 Zaixi Zhang, Qi Liu, Qingyong Hu, Chee-Kong Lee

The Transformer architecture has achieved remarkable success in a number of domains including natural language processing and computer vision.

FLCert: Provably Secure Federated Learning against Poisoning Attacks

no code implementations2 Oct 2022 Xiaoyu Cao, Zaixi Zhang, Jinyuan Jia, Neil Zhenqiang Gong

Our key idea is to divide the clients into groups, learn a global model for each group of clients using any existing federated learning method, and take a majority vote among the global models to classify a test input.

Federated Learning Test

Model Inversion Attacks against Graph Neural Networks

no code implementations16 Sep 2022 Zaixi Zhang, Qi Liu, Zhenya Huang, Hao Wang, Chee-Kong Lee, Enhong Chen

One famous privacy attack against data analysis models is the model inversion attack, which aims to infer sensitive data in the training dataset and leads to great privacy concerns.

Reinforcement Learning (RL)

FLDetector: Defending Federated Learning Against Model Poisoning Attacks via Detecting Malicious Clients

1 code implementation19 Jul 2022 Zaixi Zhang, Xiaoyu Cao, Jinyuan Jia, Neil Zhenqiang Gong

FLDetector aims to detect and remove the majority of the malicious clients such that a Byzantine-robust FL method can learn an accurate global model using the remaining clients.

Federated Learning Model Poisoning

ProtGNN: Towards Self-Explaining Graph Neural Networks

1 code implementation2 Dec 2021 Zaixi Zhang, Qi Liu, Hao Wang, Chengqiang Lu, Cheekong Lee

In this work, we propose Prototype Graph Neural Network (ProtGNN), which combines prototype learning with GNNs and provides a new perspective on the explanations of GNNs.

Motif-based Graph Self-Supervised Learning for Molecular Property Prediction

1 code implementation NeurIPS 2021 Zaixi Zhang, Qi Liu, Hao Wang, Chengqiang Lu, Chee-Kong Lee

To bridge this gap, we propose Motif-based Graph Self-supervised Learning (MGSSL) by introducing a novel self-supervised motif generation framework for GNNs.

Molecular Property Prediction Property Prediction +2

GraphMI: Extracting Private Graph Data from Graph Neural Networks

1 code implementation5 Jun 2021 Zaixi Zhang, Qi Liu, Zhenya Huang, Hao Wang, Chengqiang Lu, Chuanren Liu, Enhong Chen

Then we design a graph auto-encoder module to efficiently exploit graph topology, node attributes, and target model parameters for edge inference.

Backdoor Attacks to Graph Neural Networks

2 code implementations19 Jun 2020 Zaixi Zhang, Jinyuan Jia, Binghui Wang, Neil Zhenqiang Gong

Specifically, we propose a \emph{subgraph based backdoor attack} to GNN for graph classification.

Backdoor Attack General Classification +2

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