10 citations · 30 across the 8 of their papers we have counts for
8 papers
Approximate message passing from random initialization with applications to synchronization
Gen Li, Wei Fan, Yuting Wei
This paper is concerned with the problem of reconstructing an unknown rank-one matrix with prior structural information from noisy observations. While computing the Bayes-optimal e…
Graph Soft-Contrastive Learning via Neighborhood Ranking
Zhiyuan Ning, Pengfei Wang, Pengyang Wang +5
Graph Contrastive Learning (GCL) has emerged as a promising approach in the realm of graph self-supervised learning. Prevailing GCL methods mainly derive from the principles of con…
A Knowledge Distillation-Based Backdoor Attack in Federated Learning
Yifan Wang, Wei Fan, Keke Yang +2
Federated Learning (FL) is a novel framework of decentralized machine learning. Due to the decentralized feature of FL, it is vulnerable to adversarial attacks in the training proc…
TranSHER: Translating Knowledge Graph Embedding with Hyper-Ellipsoidal Restriction
Yizhi Li, Wei Fan, Chao Liu +2
Knowledge graph embedding methods are important for the knowledge graph completion (or link prediction) task. One existing efficient method, PairRE, leverages two separate vectors…
Anomaly-Injected Deep Support Vector Data Description for Text Outlier Detection
Zeyu You, Yichu Zhou, Tao Yang +1
Anomaly detection or outlier detection is a common task in various domains, which has attracted significant research efforts in recent years. Existing works mainly focus on structu…
An Extension of LIME with Improvement of Interpretability and Fidelity
Sheng Shi, Yangzhou Du, Wei Fan
While deep learning makes significant achievements in Artificial Intelligence (AI), the lack of transparency has limited its broad application in various vertical domains. Explaina…
A Modified Perturbed Sampling Method for Local Interpretable Model-agnostic Explanation
Sheng Shi, Xinfeng Zhang, Wei Fan
Explainability is a gateway between Artificial Intelligence and society as the current popular deep learning models are generally weak in explaining the reasoning process and predi…
Explaining the Predictions of Any Image Classifier via Decision Trees
Sheng Shi, Xinfeng Zhang, Wei Fan
Despite outstanding contribution to the significant progress of Artificial Intelligence (AI), deep learning models remain mostly black boxes, which are extremely weak in explainabi…