The seminar of OBELIX team is currently held on thursdays 11:30 am, every two weeks, at the IRISA lab, Tohannic campus (bat. ENSIBS). Usually, the presentation lasts 30 min and is followed by a discussion with the team.
The seminar is coordinated by Yann CABANES: Please contact me for any information or if you want to present your work to our team.
Previous seminars
2019 / 2019-20 / 2020-21 / 2021-22 / 2022-23 / 2023-24 / 2024-25
Upcoming seminars (2024-25)
- Date: Thursday, November 28th – 11:30
- Room: A102, ENSIBS Vannes
- Speaker: Titouan Vayer (researcher Inria–OCKHAM, ENS Lyon)
- Title: Learning graphs with precision matrices: statistical estimators, compressive approches and unrolled neural networks
- Abstract: Learning a graph of relationships between variables from signals is a fundamental problem, particularly for analyzing geographic data or EEG brain data. Such a graph provides insights into the statistical dependencies between key variables. In this presentation, I will first review standard statistical approaches and convex algorithms or solving this problem, then introduce two distinct works: one focused on inverse problems, and the other on data-driven neural network approaches. The first work demonstrates how to estimate such a graph from a sketch of the data, which is a small summary built from random projections. I will show that, from an information-theoretic perspective, it is possible to recover the graph from a limited number of measurements, under sparsity assumptions. In the second part, I will present a novel neural network architecture, called SpodNet (Schur’s Positive-Definite Network), which tackles the graph estimation problem in a data-driven manner.
- Date: WEDNESDAY, November 13th – afternoon
- Room : in Rouen
- Speaker: Guillaume MAHEY (IRISA Obelix) — Ph.D. defense
- Title : Unbalanced and Linear Optimal Transport for Reliable Estimation of the Waserstein Distance
- Abstract: Despite being very elegant in theory, optimal transport (OT) might suffer from several drawbacks in practice. Notably, the computational burden, the risk of over fitting and its sensibility to artifacts motivated the introduction of variants for the OT loss in the ML community. In this thesis, we propose such variants in order to, first, reduce computational and statistical burden and second the sensibility to artifacts of sampling of the OT loss. To do that, we built on the top of proxies distributions introduced both by the Linear and the Unbalanced OT variants.spaces for representation learning by proposing a new classification method in hyperbolic spaces. Our method which is hierarchically-informed improves the performance in hierarchical classification.
as Guillaume’s defense will be held in Rouen, he will repeat his presentation in Vannes on Friday, 13th december at 11:00 am / Room A102 (ENSIBS building)
- Date: FRIDAY, December 13th – afternoon
- Room : coming soon
- Speaker: Paul BERG (IRISA Obelix) — Ph.D. defense
- Title : Contributions to Representation Learning in Computer Vision and Remote Sensing
- Abstract : Deep Learning has become an ubiquitous tool for the resolution of image analysis tasks, notably in the remote sensing domain. As such, the need for annotated data have largely increased. But, annotating data can be costly and time consuming. Therefore, a whole field of the literature is dedicated to image representation learning by decreasing the dependence to annotations using so called self-supervised methods. The learnt representations are then usable in downstream tasks because of their discriminative nature with respect to the labels. In this context, we evaluate in this thesis how these methods can be exploited in the remote sensing domain by investigating tasks such as multi-modal scene classification for which we propose a self-supervised framework. We leverage the optimal transport problem to model several problems et propose new methodological contributions to contrastive learning. Finally, we propose to go beyond Euclidean spaces for representation learning by proposing a new classification method in hyperbolic spaces. Our method which is hierarchically-informed improves the performance in hierarchical classification.
