L’application Pl@ntNet au service de l’i-écologie – The Conversation – 19 octobre 2022

Comment l’application Pl@ntNet est au service de l’i-écologie.
Lire ici  l’excellent article du 19 octobre 2022 du site d’information The Conversation.

Permanent link to this article: https://team.inria.fr/zenith/lapplication-plntnet-au-service-de-li-ecologie-selon-le-site-the-conversation-19-octobre-2022/

Zenith HPDaSc seminar Monday 7 November 2022

Zenith  HPDaSc seminar, Monday 7 November 2022, 10h15-11h45
BAT5-01.124, Campus Saint Priest

Dbfication or Dbfiction in Deep Learning Activities
Marta Mattoso
COPPE/UFRJ, Rio de Janeiro, Brazil

Database management techniques have a lot to contribute to generating and selecting a deep learning model. In this talk, we present current initiatives of the database community towards using data management techniques to improve deep learning activities, to discuss pros and cons. Then, we will focus on provenance-based user steering to evaluate different neural network execution configurations. Provenance data adds semantics to the metrics of each configuration, which can help humans in evaluating and reproducing the models proposed by automatic tools.

An introduction to physics-informed neural networks
Alvaro Coutinho
COPPE/UFRJ, Rio de Janeiro, Brazil

In this talk we will give a brief introduction to Physics-Informed Neural Networks (PINN), that are neural networks that encode model equations, like Partial Differential Equations (PDE), as a component of the neural network itself. PINNs are nowadays used to solve PDEs, fractional equations, integral-differential equations, and stochastic problems. PINN fits observed data while reducing a PDE residual. We will discuss the basic math and algorithmic steps and show some current advanced applications.

Permanent link to this article: https://team.inria.fr/zenith/zenith-hpdasc-seminar-monday-7-november-2022/

Seminar by Dennis Shasha, NYU “BugDoc: Algorithms to Debug Computational Processes”, 25 May 2022

Journée Zenith, Golfe de Coulondres, 25 May 2022

BugDoc: Algorithms to Debug Computational Processes

Dennis Shasha, New York University, USA (joint work with Raoni Lourenco and Juliana Freire)

Data analysis for scientific experiments and enterprises, large-scale simulations, and machine learning tasks all entail the use of complex computational pipelines to reach quantitative and qualitative conclusions. If some of the activities in a pipeline produce erroneous outputs, the pipeline may fail to execute or produce incorrect results. Inferring the root cause(s) of such failures is challenging, usually requiring time and much human thought, while still being error-prone. We propose a new approach that makes use of iteration and provenance to automatically infer the root causes and derive succinct explanations of failures, including in data inputs. Through a detailed experimental evaluation, we assess the cost, precision, and recall of our approach compared to the state of the art. Our experimental data and processing software is available for use, reproducibility, and enhancement.

Permanent link to this article: https://team.inria.fr/zenith/seminar-by-dennis-shasha-nyu-bugdoc-algorithms-to-debug-computational-processes-25-may-2022/

Seminar by Fabio Porto, LNCC “ML Model Management in Gypscie”, 25 May 2022

Journée Zenith, Golfe de Coulondres, 25 May 2022

ML Model Management in Gypscie

Fabio Porto, LNCC, Petropolis, Brazil

To realize the full potential of data science, ML models (or models for short) must be built, combined and ensembled, which can be very complex as there can be many models to select from. Furthermore, they should be shared and reused, in particular, in different execution environments such as HPC or Spark clusters. To address this problem, we propose Gypscie, a new framework that supports the entire ML lifecycle and enables model reuse and import from other frameworks. The approach behind Gypscie is to combine several rich capabilities for model and data management, and model execution, which are typically provided by different tools, in a unique framework. Finally, Gypscie interfaces with multiple execution environments to run ML tasks, e.g., an HPC system such as the Santos Dumont supercomputer at LNCC or a Spark cluster.

Permanent link to this article: https://team.inria.fr/zenith/gypscie-25-may-2022/

Sixth Workshop of the HPDaSc project, 15 August 2022, LNCC, Petropolis, Brazil

See the program here.

Permanent link to this article: https://team.inria.fr/zenith/sixth-workshop-of-the-hpdasc-project-15-august-2022-lncc-petropolis-brazil/

Seminar at CEFET, Rio de Janeiro, by Patrick Valduriez “Innovation : startup strategies” 5 August 2022.

See the announcement here.

Permanent link to this article: https://team.inria.fr/zenith/seminar-at-cefet-rio-de-janeiro-by-patrick-valduriez-innovation-startup-strategies-5-august-2022/

ICML 2022: paper by Camille Garcin et al.

The paper “Stochastic smoothing of the top-K calibrated hinge loss for deep imbalanced classification” by Camille Garcin, Maximilien Servajean, Alexis Joly, Joseph Salmon has been accepted for presentation at ICML 2022 (Acceptante rate: 21% of total number (5630) of submissions).

Abstract: In modern classification tasks, the number of labels is getting larger and larger, as is the size of the datasets encountered in practice. As the number of classes increases, class ambiguity and class imbalance become more and more problematic to achieve high top-1 accuracy. Meanwhile, Top-K metrics (metrics allowing K guesses) have become popular, especially for performance reporting. Yet, proposing top-K losses tailored for deep learning remains a challenge, both theoretically and practically. In this paper we introduce a stochastic top-K hinge loss inspired by recent developments on top-K calibrated losses. Our proposal is based on the smoothing of the top-K operator building on the flexible “perturbed optimizer” framework. We show that our loss function performs very well in the case of balanced datasets, while benefiting from a significantly lower computational time than the state-of-the-art top-K loss function. In addition, we propose a simple variant of our loss for the imbalanced case. Experiments on a heavy-tailed dataset show that our loss function significantly outperforms other baseline loss functions.

Permanent link to this article: https://team.inria.fr/zenith/icml-2022-paper-by-camille-garcin-et-al/

Habilitation (HDR) defense of Antoine Liutkus, 11 Feb. 2022, at 14h.

Antoine Liutkus will defend his habilitation (HDR) on February 11th at 2PM (UTC+1). The defense will take place at LIRMM, room 02.022.

 

The defense will be about the following topic:
Probabilistic and deep models for the processing of mixtures of waveforms
In this presentation, I will start by presentint a summary of the research I did in the past 15 years. Doing so, I will first present my effort on probabilistic audio modeling, in­cluding the separation of Gaussian and α­-stable stochastic processes. Second, I will mention my work on deep learning applied to audio, which rapidly turned into a large effort for community service.
As a conclusion, I will mention my research programme, that involves a theoretical revolving around probabilistic machine learning, and an applied part that concerns the processing of time series arising in both audio and life sciences.”
Committee:
Christian Jutten, Emeritus Professor, Grenoble Univ.
Rémi Gribonval, Research Director, Inria Lyon
Cédric Févotte, Research Director, CNRS IRIT Toulouse
Laurent Daudet, Chief Scientific Officier lighton.ai and Professor, Univ. Paris Diderot
Tuomas Virtanen, Professor Tampere University
Alexey Ozerov, Research Scientist, Ava, Rennes

Permanent link to this article: https://team.inria.fr/zenith/habilitation-hdr-defense-of-antoine-liutkus-11-feb-2022-at-14h/

Pl@ntnet numéro 2 des logiciels Inria les plus connus des entreprises, 10 juin 2021.

Selon le sondage de Inria Academy sur les logiciels diffusés par Inria et ses partenaires et les besoins des entreprises. Scikit-learn, Pl@ntNet, Coq, OpenVibe et Pharo arrivent dans le top 5 des logiciels les plus connus des entreprises !

Et Pl@ntnet arrive second derrière Scikit-learn.

Permanent link to this article: https://team.inria.fr/zenith/plntnet-numero-2-des-logiciels-inria-les-plus-connus-des-entreprises-10-juin-2021/

“Making the Right Move to Senior Researcher”, by P. Valduriez, May 2021.

Check out this short article  “Making the Right Move to Senior Researcher”, to  appear in the May 2021 issue of ACM SIGMOD Record, in a new series managed by Professor Tamer Özsu which seeks to provide advice to mid-career researchers.

Permanent link to this article: https://team.inria.fr/zenith/check-out-this-short-article-in-acm-sigmod-record-2021/