16 citations · 16 across the 1 of their papers we have counts for
5 papers
Masked Random Noise for Communication Efficient Federated Learning
Shiwei Li, Yingyi Cheng, Haozhao Wang +7
Federated learning is a promising distributed training paradigm that effectively safeguards data privacy. However, it may involve significant communication costs, which hinders tra…
MICM: Rethinking Unsupervised Pretraining for Enhanced Few-shot Learning
Zhenyu Zhang, Guangyao Chen, Yixiong Zou +3
Humans exhibit a remarkable ability to learn quickly from a limited number of labeled samples, a capability that starkly contrasts with that of current machine learning systems. Un…
Learning Unknowns from Unknowns: Diversified Negative Prototypes Generator for Few-Shot Open-Set Recognition
Zhenyu Zhang, Guangyao Chen, Yixiong Zou +2
Few-shot open-set recognition (FSOR) is a challenging task that requires a model to recognize known classes and identify unknown classes with limited labeled data. Existing approac…
Masked Graph Autoencoder with Non-discrete Bandwidths
Ziwen Zhao, Yuhua Li, Yixiong Zou +2
Masked graph autoencoders have emerged as a powerful graph self-supervised learning method that has yet to be fully explored. In this paper, we unveil that the existing discrete ed…
CSGCL: Community-Strength-Enhanced Graph Contrastive Learning
Han Chen, Ziwen Zhao, Yuhua Li +3
Graph Contrastive Learning (GCL) is an effective way to learn generalized graph representations in a self-supervised manner, and has grown rapidly in recent years. However, the und…