Zaixi Zhang | DeepAI

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Featured Co-authors

Hao Wang
366 publications
Qi Liu
156 publications
Yang Yu
155 publications
Enhong Chen
96 publications
Neil Zhenqiang Gong
63 publications
Jinyuan Jia
39 publications
Binghui Wang
34 publications
Xiaoyu Cao
23 publications
Zhenya Huang
22 publications
Qingyong Hu
19 publications
Likang Wu
12 publications

∙ 06/20/2023

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

Structure-based drug design (SBDD), which utilizes the three-dimensional...

0 Zaixi Zhang, et al. ∙

∙ 05/22/2023

Learning Subpocket Prototypes for Generalizable Structure-based Drug Design

Generating molecules with high binding affinities to target proteins (a....

0 Zaixi Zhang, et al. ∙

∙ 03/13/2023

Backdoor Defense via Deconfounded Representation Learning

Deep neural networks (DNNs) are recently shown to be vulnerable to backd...

0 Zaixi Zhang, et al. ∙

∙ 12/11/2022

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

Federated recommendation (FedRec) can train personalized recommenders wi...

0 Yang Yu, et al. ∙

∙ 10/20/2022

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

Federated learning is vulnerable to poisoning attacks in which malicious...

4 Xiaoyu Cao, et al. ∙

∙ 10/08/2022

Hierarchical Graph Transformer with Adaptive Node Sampling

The Transformer architecture has achieved remarkable success in a number...

0 Zaixi Zhang, et al. ∙

∙ 10/02/2022

FLCert: Provably Secure Federated Learning against Poisoning Attacks

Due to its distributed nature, federated learning is vulnerable to poiso...

7 Xiaoyu Cao, et al. ∙

∙ 09/16/2022

Model Inversion Attacks against Graph Neural Networks

Many data mining tasks rely on graphs to model relational structures amo...

8 Zaixi Zhang, et al. ∙

∙ 07/19/2022

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

Federated learning (FL) is vulnerable to model poisoning attacks, in whi...

16 Zaixi Zhang, et al. ∙

∙ 12/02/2021

ProtGNN: Towards Self-Explaining Graph Neural Networks

Despite the recent progress in Graph Neural Networks (GNNs), it remains ...

122 Zaixi Zhang, et al. ∙

∙ 10/03/2021

Motif-based Graph Self-Supervised Learning forMolecular Property Prediction

Predicting molecular properties with data-driven methods has drawn much ...

2 Zaixi Zhang, et al. ∙

∙ 06/05/2021

GraphMI: Extracting Private Graph Data from Graph Neural Networks

As machine learning becomes more widely used for critical applications, ...

102 Zaixi Zhang, et al. ∙

∙ 06/19/2020

Backdoor Attacks to Graph Neural Networks

Node classification and graph classification are two basic graph analyti...

0 Zaixi Zhang, et al. ∙

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