1 citations · 1 across the 3 of their papers we have counts for
3 papers
MIMIC: A Generative Multimodal Foundation Model for Biomolecules
Siavash Golkar, Jake Kovalic, Irina Espejo Morales +28
Biological function emerges from coupled constraints across sequence, structure, regulation, evolution, and cellular context, yet most foundation models in biology are trained with…
On the Value of Tokeniser Pretraining in Physics Foundation Models
Hadi Sotoudeh, Payel Mukhopadhyay, Ruben Ohana +4
We investigate the impact of tokeniser pretraining on the accuracy and efficiency of physics emulation. Modern high-resolution simulations produce vast volumes of data spanning div…
A Fast Generative Framework for High-dimensional Posterior Sampling: Application to CMB Delensing
Hadi Sotoudeh, Pablo Lemos, Laurence Perreault-Levasseur
We introduce a deep generative framework for high-dimensional Bayesian inference that enables efficient posterior sampling. As telescopes and simulations rapidly expand the volume…