5 papers · 1 filter
Controlling Language and Diffusion Models by Transporting Activations
Pau Rodriguez, Arno Blaas, Michal Klein +4
The increasing capabilities of large generative models and their ever more widespread deployment have raised concerns about their reliability, safety, and potential misuse. To addr…
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…
Progressive Entropic Optimal Transport Solvers
Parnian Kassraie, Aram-Alexandre Pooladian, Michal Klein +3
Optimal transport (OT) has profoundly impacted machine learning by providing theoretical and computational tools to realign datasets. In this context, given two large point clouds…
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…
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…