Sacl-AI 4 Science Workshop

Session

Contributed Talks

10 juil. 2024, 16:00

BÂTIMENT D’ENSEIGNEMENT MUTUALISÉ (BEM)

10. Designing Molecular RNA Switches with Restricted Boltzmann Machines

Jorge FERNANDEZ-DE-COSSIO-DIAZ (ENS Paris)

10/07/2024 16:00

Contributed talks

Talk (Short)

Riboswitches are structured allosteric RNA molecules that change conformation in response to a metabolite binding event, eventually triggering a regulatory response. Computational modelling of the structure of these molecules is complicated by a complex network of tertiary contacts, stabilized by the presence of their cognate metabolite. In this work, we focus on the aptamer domain of SAM-I...

31. Optimising geometric deep learning methods for particle detection challenges in high energy physics experiments.

Matthieu Melennec

10/07/2024 16:20

Particle physics experiments like CMS (Compact Muon Solenoid) at the LHC and Super-Kamiokande let us probe the fundamental laws of physics by observing the interaction of high energy particles with various detectors. These particles leave their signatures in different sensors composing these detectors and a host of sophisticated algorithms are employed to reconstruct these particles by...

6. Scattering Spectra models for Physics

Dr Rudy Morel (Flatiron Institute)

10/07/2024 16:40

Contributed talks

Talk (Short)

Physicists routinely need probabilistic models for a number of tasks such as parameter inference or the generation of new realizations of a field. Establishing such models for highly non-Gaussian fields is a challenge, especially when the number of samples is limited. In this paper, we introduce scattering spectra models for stationary fields and we show that they provide accurate and robust...

5. Incremental Neural Data Assimilation

Matthieu Blanke (Inria Paris, DI ENS)

11/07/2024 12:15

Talk (Short)

Data assimilation is a central problem in many geophysical applications, such as weather forecasting. It aims to estimate the state of a potentially large system, such as the atmosphere, from sparse observations, supplemented by prior physical knowledge. The size of the systems involved and the complexity of the underlying physical equations make it a challenging task from a computational...

7. Large-scale auto-formalization of mathematical theories: why, how, and why now?

Fabian Gloeckle (Ecole des Ponts ParisTech)

12/07/2024 12:15

Contributed talks

Poster

A machine readable and verifiable account of a large portion of human mathematics would change the way mathematicians can work, learn and collaborate. While impressive progress has been made in the mathematical standard libraries of proof assistants like Lean, Isabelle and Coq, the proportion of mathematical results formalized in such systems remains tiny overall. In the talk, I will argue...