5 papers
Learnware of Language Models: Specialized Small Language Models Can Do Big
Zhi-Hao Tan, Zi-Chen Zhao, Hao-Yu Shi +4
The learnware paradigm offers a novel approach to machine learning by enabling users to reuse a set of well-trained models for tasks beyond the models' original purposes. It elimin…
Beimingwu: A Learnware Dock System
Zhi-Hao Tan, Jian-Dong Liu, Xiao-Dong Bi +7
The learnware paradigm proposed by Zhou [2016] aims to enable users to reuse numerous existing well-trained models instead of building machine learning models from scratch, with th…
Matrix Information Theory for Self-Supervised Learning
Yifan Zhang, Zhiquan Tan, Jingqin Yang +2
The maximum entropy encoding framework provides a unified perspective for many non-contrastive learning methods like SimSiam, Barlow Twins, and MEC. Inspired by this framework, we…
RelationMatch: Matching In-batch Relationships for Semi-supervised Learning
Yifan Zhang, Jingqin Yang, Zhiquan Tan +1
Semi-supervised learning has emerged as a pivotal approach for leveraging scarce labeled data alongside abundant unlabeled data. Despite significant progress, prevailing SSL method…
SEAL: Simultaneous Label Hierarchy Exploration And Learning
Zhiquan Tan, Zihao Wang, Yifan Zhang
Label hierarchy is an important source of external knowledge that can enhance classification performance. However, most existing methods rely on predefined label hierarchies that m…