Benchopt: Reproducible, efficient and collaborative optimization benchmarks

Numerical validation is at the core of machine learning research as it allows to assess the actual impact of new methods, and to confirm the agreement between theory and practice. Yet, the rapid development of the field poses several challenges: researchers are confronted with a profusion of methods to compare, limited transparency and consensus on best practices, as well as tedious re-implementation work. As a result, validation is often very partial, which can lead to wrong conclusions that slow down the progress of research. We propose Benchopt, a collaborative framework to automate, reproduce and publish optimization benchmarks in machine learning across programming languages and hardware architectures. Benchopt simplifies benchmarking for the community by providing an off-the-shelf tool for running, sharing and extending experiments. To demonstrate its broad usability, we showcase benchmarks on three standard learning tasks: $\ell_2$-regularized logistic regression, Lasso, and ResNet18 training for image classification. These benchmarks highlight key practical findings that give a more nuanced view of the state-of-the-art for these problems, showing that for practical evaluation, the devil is in the details. We hope that Benchopt will foster collaborative work in the community hence improving the reproducibility of research findings.Comment: Accepted in proceedings of NeurIPS 22; Benchopt library documentation is available at https://benchopt.github.io

Global electricity network - Feasibility study

With the strong development of renewable energy sources worldwide, the concept of a global electricity network has been imagined in order to take advantage of the diversity from different time zones, seasons, load patterns and the intermittency of the generation, thus supporting a balanced coordination of power supply of all interconnected countries. The TB presents the results of the feasibility study performed by WG C1.35. It addresses the challenges, benefits and issues of uneven distribution of energy resources across the world. The time horizon selected is 2050. The study finds significant potential benefits of a global interconnection, identifies the most promising links, and includes sensitivity analyses to different factors, such as wind energy capacity factors or technology costs

La molécule d'acide gras. Quelques résultats d'une étude cristallographique

L'étude de la structure et du polymorphisme des acides gras permet d'établir ou de confirmer un certain nombre de conclusions concernant la forme des molécules, leur répartition et leurs équilibres dans la maille cristalline. La forme se maintient à peu près invariable. La répartition et les équilibres se modifient au contraire, lorsqu'on fait varier les conditions physiques et spécialement la température. L'étude de ces modifications — notamment des discontinuités qui se présentent dans la série des diacides — montre l'importance relative des groupements acides COOH dans le réseau cristallin

Science et Humanisme

Dupré La Tour F. Science et Humanisme. In: Bulletin de l'Association Guillaume Budé,n°12, décembre 1950. pp. 16-36

Benchopt: Reproducible, efficient and collaborative optimization benchmarks

International audienceNumerical validation is at the core of machine learning research as it allows to assess the actual impact of new methods, and to confirm the agreement between theory and practice. Yet, the rapid development of the field poses several challenges: researchers are confronted with a profusion of methods to compare, limited transparency and consensus on best practices, as well as tedious re-implementation work. As a result, validation is often very partial, which can lead to wrong conclusions that slow down the progress of research. We propose Benchopt, a collaborative framework to automate, reproduce and publish optimization benchmarks in machine learning across programming languages and hardware architectures. Benchopt simplifies benchmarking for the community by providing an off-the-shelf tool for running, sharing and extending experiments. To demonstrate its broad usability, we showcase benchmarks on three standard learning tasks: ℓ 2-regularized logistic regression, Lasso, and ResNet18 training for image classification. These benchmarks highlight key practical findings that give a more nuanced view of the state-of-the-art for these problems, showing that for practical evaluation, the devil is in the details. We hope that Benchopt will foster collaborative work in the community hence improving the reproducibility of research findings

Qu'est-ce qui change ? 38 idées reçues sur le changement climatique en montagne

Ce livret vient en complément du premier livret sur les idées reçues sur les risques naturels en montagne, paru en 2013. Il aborde la question des idées reçues sur le changement climatique en montagne, sur la base de connaissances acquises dans les projets de recherche du centre Irstea de Grenoble. Les risques en montagne vont-ils augmenter ou au contraire disparaître ? Y aura-t-il toujours de la neige ? Que vont devenir les marmottes ? Vous en saurez plus en parcourant ces pages conçues pour aider à démêler le vrai du faux