17 citations · 19 across the 6 of their papers we have counts for
6 papers
Representation Learning in Diffusion and Flow-based Model: An Application Aspect
Yanchen Xu, Sida Huang, Zhenyu Gu +3
Diffusion models and flow-based models have recently become the dominant paradigms in generative modeling, largely due to their ability to learn rich, multi-level visual representa…
GRPO-RM: Fine-Tuning Representation Models via GRPO-Driven Reinforcement Learning
Yanchen Xu, Ziheng Jiao, Hongyuan Zhang +1
The Group Relative Policy Optimization (GRPO), a reinforcement learning method used to fine-tune large language models (LLMs), has proved its effectiveness in practical application…
Rectified Noise: A Generative Model Using Positive-incentive Noise
Zhenyu Gu, Yanchen Xu, Sida Huang +2
Rectified Flow (RF) has been widely used as an effective generative model. Although RF is primarily based on probability flow Ordinary Differential Equations (ODE), recent studies…
Learn Beneficial Noise as Graph Augmentation
Siqi Huang, Yanchen Xu, Hongyuan Zhang +1
Although graph contrastive learning (GCL) has been widely investigated, it is still a challenge to generate effective and stable graph augmentations. Existing methods often apply h…
Why Does Dropping Edges Usually Outperform Adding Edges in Graph Contrastive Learning?
Yanchen Xu, Siqi Huang, Hongyuan Zhang +1
Graph contrastive learning (GCL) has been widely used as an effective self-supervised learning method for graph representation learning. However, how to apply adequate and stable g…
Data Augmentation of Contrastive Learning is Estimating Positive-incentive Noise
Hongyuan Zhang, Yanchen Xu, Sida Huang +1
Inspired by the idea of Positive-incentive Noise (Pi-Noise or -Noise) that aims at learning the reliable noise beneficial to tasks, we scientifically investigate the connection…