most citedInterventional Contrastive Learning with Meta Semantic Regularizer

10 citations · 14 across the 3 of their papers we have counts for

collaborators

10 papers

cs.LG2024

Introducing Diminutive Causal Structure into Graph Representation Learning

Hang Gao, Peng Qiao, Yifan Jin +3

When engaging in end-to-end graph representation learning with Graph Neural Networks (GNNs), the intricate causal relationships and rules inherent in graph data pose a formidable c…

cs.CV20245 cited

Meta-Auxiliary Learning for Micro-Expression Recognition

Jingyao Wang, Yunhan Tian, Yuxuan Yang +3

Micro-expressions (MEs) are involuntary movements revealing people's hidden feelings, which has attracted numerous interests for its objectivity in emotion detection. However, desp…

cs.MA20242 cited

T2MAC: Targeted and Trusted Multi-Agent Communication through Selective Engagement and Evidence-Driven Integration

Chuxiong Sun, Zehua Zang, Jiabao Li +4

Communication stands as a potent mechanism to harmonize the behaviors of multiple agents. However, existing works primarily concentrate on broadcast communication, which not only l…

cs.LG2023

Unleash Model Potential: Bootstrapped Meta Self-supervised Learning

Jingyao Wang, Zeen Song, Wenwen Qiang +1

The long-term goal of machine learning is to learn general visual representations from a small amount of data without supervision, mimicking three advantages of human cognition: i)…

cs.CV2023

Information Theory-Guided Heuristic Progressive Multi-View Coding

Jiangmeng Li, Hang Gao, Wenwen Qiang +1

Multi-view representation learning aims to capture comprehensive information from multiple views of a shared context. Recent works intuitively apply contrastive learning to differe…

cs.LG20231 cited

Towards the Sparseness of Projection Head in Self-Supervised Learning

Zeen Song, Xingzhe Su, Jingyao Wang +3

In recent years, self-supervised learning (SSL) has emerged as a promising approach for extracting valuable representations from unlabeled data. One successful SSL method is contra…