4 papers · 1 filter
Two Calm Ends and the Wild Middle: A Geometric Picture of Memorization in Diffusion Models
Nick Dodson, Xinyu Gao, Qingsong Wang +2
Diffusion models generate high-quality samples but can also memorize training data, raising serious privacy concerns. Understanding the mechanisms governing when memorization versu…
Robust Graph-Based Semi-Supervised Learning via -Conductances
Sawyer Jack Robertson, Chester Holtz, Zhengchao Wan +2
We study the problem of semi-supervised learning on graphs in the regime where data labels are scarce or possibly corrupted. We propose an approach called -conductance learning…
Elucidating Flow Matching ODE Dynamics with Respect to Data Geometries and Denoisers
Zhengchao Wan, Qingsong Wang, Gal Mishne +1
Flow matching (FM) models extend ODE sampler based diffusion models into a general framework, significantly reducing sampling steps through learned vector fields. However, the theo…
Comparing Graph Transformers via Positional Encodings
Mitchell Black, Zhengchao Wan, Gal Mishne +2
The distinguishing power of graph transformers is closely tied to the choice of positional encoding: features used to augment the base transformer with information about the graph.…