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Danyal REHMAN

Postdoctorat - UdeM
Superviseur⋅e principal⋅e
Yoshua Bengio
Sujets de recherche
Apprentissage de représentations
Apprentissage par renforcement
Apprentissage profond
Modèles génératifs
Modélisation moléculaire
GitHub

Publications

FORT: Forward-Only Regression Training of Normalizing Flows
Danyal REHMAN
Oscar Davis
Jiarui Lu
Jian Tang
Michael M. Bronstein
Yoshua Bengio
Alexander Tong
Joey Bose
Simulation-free training frameworks have been at the forefront of the generative modelling revolution in continuous spaces, leading to neura… (voir plus)l dynamical systems that encompass modern large-scale diffusion and flow matching models. Despite the scalability of training, the generation of high-quality samples and their corresponding likelihood under the model requires expensive numerical simulation -- inhibiting adoption in numerous scientific applications such as equilibrium sampling of molecular systems. In this paper, we revisit classical normalizing flows as one-step generative models with exact likelihoods and propose a novel, scalable training objective that does not require computing the expensive change of variable formula used in conventional maximum likelihood training. We propose Forward-Only Regression Training (FORT), a simple
2025-06-11
ICML.cc/2025/Workshop/GenBio (spotlight)
doi.org
openreview.net