6 citations · 6 across the 6 of their papers we have counts for
6 papers
Navigating the Exploration-Exploitation Tradeoff in Inference-Time Scaling of Diffusion Models
Xun Su, Jianming Huang, Yang Yusen +2
Inference-time scaling has achieved remarkable success in language models, yet its adaptation to diffusion models remains underexplored. We observe that the efficacy of recent Sequ…
Self-supervised Subgraph Neural Network With Deep Reinforcement Walk Exploration
Jianming Huang, Hiroyuki Kasai
Graph data, with its structurally variable nature, represents complex real-world phenomena like chemical compounds, protein structures, and social networks. Traditional Graph Neura…
Anchor Space Optimal Transport as a Fast Solution to Multiple Optimal Transport Problems
Jianming Huang, Xun Su, Zhongxi Fang +1
In machine learning, Optimal Transport (OT) theory is extensively utilized to compare probability distributions across various applications, such as graph data represented by node…
Wasserstein Graph Distance Based on -Approximated Tree Edit Distance between Weisfeiler-Lehman Subtrees
Zhongxi Fang, Jianming Huang, Xun Su +1
The Weisfeiler-Lehman (WL) test is a widely used algorithm in graph machine learning, including graph kernels, graph metrics, and graph neural networks. However, it focuses only on…
LCS Graph Kernel Based on Wasserstein Distance in Longest Common Subsequence Metric Space
Jianming Huang, Zhongxi Fang, Hiroyuki Kasai
For graph learning tasks, many existing methods utilize a message-passing mechanism where vertex features are updated iteratively by aggregation of neighbor information. This strat…
Graph embedding using multi-layer adjacent point merging model
Jianming Huang, Hiroyuki Kasai
For graph classification tasks, many traditional kernel methods focus on measuring the similarity between graphs. These methods have achieved great success on resolving graph isomo…