110 citations · 387 across the 46 of their papers we have counts for
49 papers
Learning -Powered Conditional Distributions with Importance-Weighted Flow Matching
Maciej Falkiewicz, Frantzeska Lavda, Jason Ramapuram +1
Classifier-Free Guidance (CFG) with guidance scale is commonly motivated by the -powered conditional distribution obtained by setting , but CFG does not, in general, sa…
Climate Physics Dynamic Matching
Gurjeet Sangra Singh, Frantzeska Lavda, Alexandros Kalousis
Deep generative models such as flow matching and diffusion models have shown potential for learning complex dynamical systems, but typically act as black boxes that neglect underly…
Symmetry-Breaking De Novo Crystal Generation via Markovian Jump Diffusion
Van Khoa Nguyen, Alexandros Kalousis
Generating crystals has recently attracted significant interest due to their broad applications in materials science. However, existing generative models struggle to produce comple…
Variational Grey-Box Dynamics Matching
Gurjeet Sangra Singh, Frantzeska Lavda, Giangiacomo Mercatali +1
Deep generative models such as flow matching and diffusion models have shown great potential in learning complex distributions and dynamical systems, but often act as black-boxes,…
Noise-Guided Transport for Imitation Learning
Lionel Blondé, Joao A. Candido Ramos, Alexandros Kalousis
We consider imitation learning in the low-data regime, where only a limited number of expert demonstrations are available. In this setting, methods that rely on large-scale pretrai…
Stein Diffusion Guidance: Training-Free Posterior Correction for Sampling Beyond High-Density Regions
Van Khoa Nguyen, Lionel Blondé, Alexandros Kalousis
Training-free diffusion guidance offers a flexible framework for leveraging off-the-shelf classifiers without additional training. Yet, current approaches hinge on posterior approx…