3 citations · 5 across the 33 of their papers we have counts for
7 papers · 2 filters
Leveraging Cardiovascular Simulations for In-Vivo Prediction of Cardiac Biomarkers
Laura Manduchi, Antoine Wehenkel, Jens Behrmann +6
Whole-body hemodynamics simulators, which model blood flow and pressure waveforms as functions of physiological parameters, are now essential tools for studying cardiovascular syst…
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…
Disentangled Representation Learning with the Gromov-Monge Gap
Théo Uscidda, Luca Eyring, Karsten Roth +3
Learning disentangled representations from unlabelled data is a fundamental challenge in machine learning. Solving it may unlock other problems, such as generalization, interpretab…
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…
A Galois theorem for machine learning: Functions on symmetric matrices and point clouds via lightweight invariant features
Ben Blum-Smith, Ningyuan Huang, Marco Cuturi +1
In this work, we present a mathematical formulation for machine learning of (1) functions on symmetric matrices that are invariant with respect to the action of permutations by con…
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…