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Convexity in Whitney Problems

Salle W (ENS)

Suppose E is a compact subset of R^n, and we are given a function f, mapping E to the real numbers. How can we tell if the function lies on a smooth convex function? Can we construct an almost optimal, smooth, convex interpolant of the function? These are examples of Whitney-type extension and trace problems; while theoretical, they are driven by practical questions of interpolation of data, where convexity is a natural constraint. I will begin with an answer to these questions by presenting work of mine proving there is a […]

Information-estimation geometry: a scale space view of prior probability models

Salle W

Solving most image processing tasks, such as denoising, deblurring, inpainting, etc, require explicitly or implicitly a prior probability model of natural images. Classically, both learning (by maximizing likelihood) and using (via Bayes' rule) such prior models are intractable due to the curse of dimensionality. Diffusion models take a different approach, where the prior density is replaced by a family of score vector fields across noise levels. They have led to impressive success in generative modeling, but the learned density is not explicit nor is it readily usable as a prior […]

Deep Learning as Neural Low-Degree Filtering: A Spectral Theory of Hierarchical Feature Learning

Salle W

Understanding how deep neural networks learn useful internal representations from data remains a central open problem in the theory of deep learning. We introduce Neural Low-Degree Filtering (Neural LoFi), a stylized limit of gradient-based training in which hierarchical feature learning becomes an explicit iterative spectral procedure. In this limit, the dynamics at each layer decouple: given the current representation, the next layer selects directions with maximal accessible low-degree correlation to the label. This yields a tractable surrogate mechanism for deep learning, together with a natural kernel-space interpretation. Neural LoFi provides […]

Automath! Mathematical Developments in Geophysical Fluid Dynamics, Idealised Models

Institut Henri Poincaré amphithéâtre Hermite

  Le prochain séminaire Automath prendra un format particulier : il consistera en une journée complète consacrée aux usages de l’IA pour le développement mathématique de la dynamique des fluides géophysiques. Cette journée, organisée par Emmanuel Dormy, s’inscrira dans le cadre du workshop dédié à ce thème à l’IHP. Le programme est disponible ici : AI Day program

Yueyun Hu – Deux applications des marches aléatoires branchantes avec sélection

Salle W (ENS)

Les marches aléatoires branchantes avec sélection modélisent des populations de particules qui se déplacent et se reproduisent, mais dont la croissance est limitée par un mécanisme de sélection. Dans cet exposé, je présenterai deux applications de ce modèle : la première concerne la percolation par la moyenne sur le graphe complet, la seconde le problème du couplage planté dans les graphes. Travaux en commun avec Elie Aïdékon et Dana Yang.

Helmut Abels – Diffuse Interface Models and their Sharp Interface Limits

Salle W - ENS PSL 45 rue d'Ulm, Paris, France

Interfaces separating two or more species or components of a material are omnipresent in applications in the sciences. Nowadays there are two main classes of models to describe interfaces, both from a theoretical and practical point of view: In classical so-called "sharp interface models" the species or components under consideration fill disjoint domains that are separated by lower dimensional surfaces of a certain regularity. On the other hand in "diffuse interface models" a partial mixing of the species or components on a small length scale is taken into account, which […]

Guy Gilboa – How to Encode World Knowledge?

Salle W

Foundation models are a key platform which implicitly encodes world knowledge. In this talk we first focus on vision-language models, such as CLIP, and investigate their geometric behavior and logic behind the high-dimensional feature encoding. For instance, we find that as image or text become more rare and distinct they are encoded further from the center of the embedding. We explain why InfoNCE loss leads to that behavior. We also find out empirically that each modality can be well modeled statistically as admitting a multivariate Gaussian distribution. This finding is […]

Hélène Eynard-Bontemps

ENS — amphi Galois 45 rue d'Ulm, Paris, France

Séminaires des Mathématiques

Antoine Joux

ENS — amphi Galois 45 rue d'Ulm, Paris, France

Séminaires des Mathématiques