Jul 06
New preprint “Abide by the Law and Follow the Flow: Conservation Laws for Gradient Flows” by @SibylleMarcotte, @RemiGribonval and @GabrielPeyre
We define and study “conservation laws” for the optimization of over-parameterized models. https://arxiv.org/abs/2307.00144
Jun 16
The following papers will be presented by the team at the next GRETSI:
In addition, Titouan Vayer will organize a special session on “Graph Learning & Learning with Graphs” with Arnaud Breloy.
See you in Grenoble!
Mar 15
We are glad to announce that the paper “Self-supervised learning with rotation-invariant kernels” (Léon Zheng, Gilles Puy, Elisa Riccietti, Patrick Pérez, Rémi Gribonval) has been accepted at the 11th Interanational Conference on Representation Learning (Kigali, May 2023). This project is a collaboration betwen OCKHAM team and valeo.ai.
Oct 27
We are glad to announce that the following paper with contributions from the Dante team have been accepted for publication in the Transaction of Machine Learning Research (TMLR) journal:
Oct 26
We are glad to announce that the following papers with contributions from the Dante team have been accepted for publication at the NeurIPS 2022 conference:
– Template based Graph Neural Network with Optimal Transport Distances, C. Vincent-Cuaz, R. Flamary, M. Corneli, T. Vayer & N. Courty.
– Benchopt: Reproducible, efficient and collaborative optimization benchmarks, T. Moreau, M. Massias, A. Gramfort and others.
– Beyond L1: Fast and better sparse models with skglm, Q. Bertrand, Q. Klopfenstein, P.-A. Bannier, G. Gidel & M. Massias
Oct 10
We are pleased to announce that our work “Efficient Identification of Butterfly Sparse Matrix Factorizations” (Léon Zheng, Elisa Riccietti, Rémi Gribonval) has been accepted for publication in SIAM Journal on Mathematics of Data Science.
This work studies identifiability aspects of sparse matrix factorizations with butterfly constraints, a structure associated with fast transforms and used in recent neural network compression methods for its expressiveness and complexity reduction properties. In particular, we show that the butterfly factorization algorithm from the article “Fast learning of fast transforms, with guarantees” (ICASSP 2022) is endowed with exact recovery guarantees.
Apr 12
Notre équipe bénéficie cette année d’un poste permanent d’ingénieur(e) Inria. Profil recherché en développement logiciel spécialiste en calcul scientifique pour l’apprentissage et le traitement du signal.
La première affectation au sein de notre équipe porte sur une durée de 4 ans renouvelable. La personne recrutée s’intègrera par ailleurs au collectif des ingénieurs permanents de l’institut, représenté au niveau d’un centre par le Service d’Expérimentation et de Développement (SED).
Poste ouvert dans un premier temps en mobilité fonction publique (date limite de candidature 6 mai 2022), puis le cas échéant sur concours de recrutement.
Pour des détails sur le poste, les contacts, et comment candidater, c’est par ici.
Sep 22
We will receive Thomas Debarre on Thursday 23th September for a seminar.
Title :
Total-Variation-Based Optimization: Theory and Algorithms for Minimal Sparsity
Abstract :
The total-variation (TV) norm for measures as a regularizer for continuous-domain inverse problems has been the subject of many recent works, both on the theoretical and algorithmic sides. Its sparsity-promoting effect is now well understood, particularly in the context of Dirac recovery. In this talk, I will present some of our TV-related work in the context of spline recovery, i.e., in the presence of a differential regularization operator. My emphasis will be on the study of the solution set of such problems, which is typically non unique, and more specifically on identifying their sparsest solution. I will also presents algorithmic aspects and results for spline reconstruction.
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