4 papers
Noise is All You Need: Solving Linear Inverse Problems by Noise Combination Sampling with Diffusion Models
Xun Su, Hiroyuki Kasai
Pretrained diffusion models have demonstrated strong capabilities in zero-shot inverse problem solving by incorporating observation information into the generation process of the d…
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…