92 citations · 199 across the 26 of their papers we have counts for
9 papers · 1 filter
Asynchronous Federated Clustering with Unknown Number of Clusters
Yunfan Zhang, Yiqun Zhang, Yang Lu +3
Federated Clustering (FC) is crucial to mining knowledge from unlabeled non-Independent Identically Distributed (non-IID) data provided by multiple clients while preserving their p…
Categorical Data Clustering via Value Order Estimated Distance Metric Learning
Yiqun Zhang, Mingjie Zhao, Hong Jia +3
Clustering is a popular machine learning technique for data mining that can process and analyze datasets to automatically reveal sample distribution patterns. Since the ubiquitous…
HyReaL: Clustering Attributed Graph via Hyper-Complex Space Representation Learning
Junyang Chen, Yang Lu, Mengke Li +3
Clustering complex data in the form of attributed graphs has attracted increasing attention, where powerful graph representation is a critical prerequisite. However, the well-known…
Improving Visual Prompt Tuning by Gaussian Neighborhood Minimization for Long-Tailed Visual Recognition
Mengke Li, Ye Liu, Yang Lu +3
Long-tail learning has garnered widespread attention and achieved significant progress in recent times. However, even with pre-trained prior knowledge, models still exhibit weaker…
Personalized Federated Learning on Heterogeneous and Long-Tailed Data via Expert Collaborative Learning
Fengling Lv, Xinyi Shang, Yang Zhou +3
Personalized Federated Learning (PFL) aims to acquire customized models for each client without disclosing raw data by leveraging the collective knowledge of distributed clients. H…
Adapt PointFormer: 3D Point Cloud Analysis via Adapting 2D Visual Transformers
Mengke Li, Da Li, Guoqing Yang +2
Pre-trained large-scale models have exhibited remarkable efficacy in computer vision, particularly for 2D image analysis. However, when it comes to 3D point clouds, the constrained…
Transitive Vision-Language Prompt Learning for Domain Generalization
Liyuan Wang, Yan Jin, Zhen Chen +4
The vision-language pre-training has enabled deep models to make a huge step forward in generalizing across unseen domains. The recent learning method based on the vision-language…
Improve Knowledge Distillation via Label Revision and Data Selection
Weichao Lan, Yiu-ming Cheung, Qing Xu +4
Knowledge distillation (KD) has become a widely used technique in the field of model compression, which aims to transfer knowledge from a large teacher model to a lightweight stude…
Dynamically Anchored Prompting for Task-Imbalanced Continual Learning
Chenxing Hong, Yan Jin, Zhiqi Kang +4
Existing continual learning literature relies heavily on a strong assumption that tasks arrive with a balanced data stream, which is often unrealistic in real-world applications. I…