7 citations · 11 across the 20 of their papers we have counts for
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Diffusion As Self-Distillation: End-to-End Latent Diffusion In One Model
Xiyuan Wang, Muhan Zhang
Standard Latent Diffusion Models rely on a complex, three-part architecture consisting of a separate encoder, decoder, and diffusion network, which are trained in multiple stages.…
GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning
Weishuo Ma, Yanbo Wang, Xiyuan Wang +2
Graph Neural Networks (GNNs) are powerful tools for processing relational data but often struggle to generalize to unseen graphs, giving rise to the development of Graph Foundation…
Round-trip Reinforcement Learning: Self-Consistent Training for Better Chemical LLMs
Lecheng Kong, Xiyuan Wang, Yixin Chen +1
Large Language Models (LLMs) are emerging as versatile foundation models for computational chemistry, handling bidirectional tasks like reaction prediction and retrosynthesis. Howe…
Meta Pruning via Graph Metanetworks : A Universal Meta Learning Framework for Network Pruning
Yewei Liu, Xiyuan Wang, Muhan Zhang
We propose an entirely new meta-learning framework for network pruning. It is a general framework that can be theoretically applied to almost all types of networks with all kinds o…
OCN: Effectively Utilizing Higher-Order Common Neighbors for Better Link Prediction
Juntong Wang, Xiyuan Wang, Muhan Zhang
Common Neighbors (CNs) and their higher-order variants are important pairwise features widely used in state-of-the-art link prediction methods. However, existing methods often stru…
Do Graph Diffusion Models Accurately Capture and Generate Substructure Distributions?
Xiyuan Wang, Yewei Liu, Lexi Pang +2
Diffusion models have gained popularity in graph generation tasks; however, the extent of their expressivity concerning the graph distributions they can learn is not fully understo…
Using Random Noise Equivariantly to Boost Graph Neural Networks Universally
Xiyuan Wang, Muhan Zhang
Recent advances in Graph Neural Networks (GNNs) have explored the potential of random noise as an input feature to enhance expressivity across diverse tasks. However, naively incor…