4 papers · 1 filter
On the Wasserstein Gradient Flow Interpretation of Drifting Models
Arthur Gretton, Li Kevin Wenliang, Alexandre Galashov +3
Recently, Deng et al. (2026) proposed Generative Modeling via Drifting (GMD), a novel framework for generative tasks. This note presents an analysis of GMD through the lens of Wass…
Careful with that Scalpel: Improving Gradient Surgery with an EMA
Yu-Guan Hsieh, James Thornton, Eugene Ndiaye +3
Beyond minimizing a single training loss, many deep learning estimation pipelines rely on an auxiliary objective to quantify and encourage desirable properties of the model (e.g. p…
Simple ReFlow: Improved Techniques for Fast Flow Models
Beomsu Kim, Yu-Guan Hsieh, Michal Klein +4
Diffusion and flow-matching models achieve remarkable generative performance but at the cost of many sampling steps, this slows inference and limits applicability to time-critical…
Contrasting Multiple Representations with the Multi-Marginal Matching Gap
Zoe Piran, Michal Klein, James Thornton +1
Learning meaningful representations of complex objects that can be seen through multiple () views or modalities is a core task in machine learning. Existing methods use lo…