11 citations · 15 across the 7 of their papers we have counts for
7 papers
Theoretical Analysis of Sparse Optimization with Reparameterization, Weight Decay, and Adaptive Learning Rate
Huangyu Xu, Jingqin Yang, Qianqian Xu +1
Sparse optimization is a fundamental challenge in various practical applications. A popular approach to sparse optimization is regularization. However, it may encounter op…
Generalization Bounds of Stochastic Gradient Descent in Homogeneous Neural Networks
Wenquan Ma, Yang Sui, Jiaye Teng +3
Algorithmic stability is among the most potent techniques in generalization analysis. However, its derivation usually requires a stepsize under non-convex…
Information Flow in Self-Supervised Learning
Zhiquan Tan, Jingqin Yang, Weiran Huang +2
In this paper, we conduct a comprehensive analysis of two dual-branch (Siamese architecture) self-supervised learning approaches, namely Barlow Twins and spectral contrastive learn…
Cumulative Reasoning with Large Language Models
Yifan Zhang, Jingqin Yang, Yang Yuan +1
Recent advancements in large language models (LLMs) have shown remarkable progress, yet their ability to solve complex problems remains limited. In this work, we introduce Cumulati…
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…
Contrastive Learning Is Spectral Clustering On Similarity Graph
Zhiquan Tan, Yifan Zhang, Jingqin Yang +1
Contrastive learning is a powerful self-supervised learning method, but we have a limited theoretical understanding of how it works and why it works. In this paper, we prove that c…