most citedAI Flow: Perspectives, Scenarios, and Approaches

3 citations · 5 across the 3 of their papers we have counts for

collaborators

5 papers

cs.CV2025

Laytrol: Preserving Pretrained Knowledge in Layout Control for Multimodal Diffusion Transformers

Sida Huang, Siqi Huang, Ping Luo +1

With the development of diffusion models, enhancing spatial controllability in text-to-image generation has become a vital challenge. As a representative task for addressing this c…

cs.LG2025

CoLM: Collaborative Large Models via A Client-Server Paradigm

Siqi Huang, Sida Huang, Hongyuan Zhang

Large models have achieved remarkable performance across a range of reasoning and understanding tasks. Prior work often utilizes model ensembles or multi-agent systems to collabora…

cs.AI20253 cited

AI Flow: Perspectives, Scenarios, and Approaches

Hongjun An, Wenhan Hu, Sida Huang +11

Pioneered by the foundational information theory by Claude Shannon and the visionary framework of machine intelligence by Alan Turing, the convergent evolution of information and c…

cs.LG20251 cited

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…

cs.LG20251 cited

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…