Louis THIRY

• 2018-2021: Doctorat sur "L'efficacité des méthodes locales pour la classification d'images et la regression d'énergie en physique", effectué au sein de l'équipe DATA du département d'informatique de l'ENS Paris, sous la direction de Stéphane Mallat.
• 2021-...: Postdoc sur la modélisation stochastique et l'assimilation de donnés pour les écoulement océaniques au sein de l'équipe FLUMINANCE de l'INRIA Rennes.

Enseignement (Teaching)

• Cours statistiques et probabilités, 1A Info, ENS Rennes, 2022.

Présentations orales (Talks)

• Efficiency of patch representations for image classification, séminaire Deep Learning des equipes INRIA Sirocco et Serpicon, INRIA Rennes, 21 mars 2022, slides.
• Soutenance de These (PhD defense), ENS Paris, 9 juillet 2021, slides.
• The unreasonable effectiveness of patches in convolutional kernel methods, séminaire de l'équipe MLIA du LIP6, laboratoire d’informatique de Sorbonne Université, 26 avril 2021, slides.
Efficienslides/cy of local methods in image classification and energy regression in physics, séminaire dslides/e statistiques de l'IRMAR, université Rennes 1, 19 mars 2021, slides.
• Image clslides/assification with patches neighborhood encoding, équipe PANAslides/MA, INRIA Rennes, 12 novembre 2020, slides.
• Interactslides/ive neural style transfer with artists, ICCC 2020, slides/virtual conference, 7-11 sept. 2020, slides, link.
• Décomposition des images par les algorithmes et conséquences sur leur circulation, Colloque IMAGO-ENS, La circulation des images, Paris (virtuel), 15-18 juin 2020, video (english), link.
• Deep Network Classification by Scattering and Homotopy Dictionary Learning, Séminaire Images Optimisation et Probabilités, Universite de Bordeaux, 30 jan. 2020, link, slides.
• Image classification with Scattering and Dictionary learning, INRIA Parietal Team Seminar, Saclay, France, 22 oct. 2019, slides.
• Solid harmonic scattering transform for atomization energy regression, CECAM discussion meeting “Coarse-graining with Machine Learning in molecular dynamics”, Tuesday, December 4 – Thursday, December 6, 2018 slides, link.
• Solid Harmonic Wavelet Scattering for Predictions of Molecule Properties, Cermics Lab, ENPC, Paris, 17 May 2018 slides, link.

Publications

• Classification d'images (Image classification)

• The Unreasonable Effectiveness of Patches in Deep Convolutional Kernels Methods, International Conference of Learing Representation (ICLR) 2021.
• Deep network classification by Scattering and homotopy dictionary learning, International Conference of Learing Representation (ICLR) 2020.

• Apprentissage en chimie quantique et physique des materiaux (Machine learning applied to quantum chemistry and condensed matter physics)

• Pushing the frontiers of density functionals by solving the fractional electron problem, Science, 9 Dec 2021, Vol 374, Issue 6573, pp. 1385-1389.
• Machine learning surrogate models for prediction of point defect vibrational entropy, Physical Review Materials 4,063802, 2020.
• Solid harmonic wavelet scattering for predictionsof molecule properties, J. Chem. Phys. 148, 241732 (2018).

• Interaction avec des artistes (Interaction with artists)

• Diptychs of human and machine perceptions, NeurIPS 2020 workshop "Machine Learning for Creativity and Design".
• Interactive Neural Style Transfer with Artists, International Conference of Computational Creativity (ICCC) 2020, [photo pairs].
• Dialog on a canvas with a machine, NeurIPS 2019 workshop "Machine Learning for Creativity and Design".

• Développement logiciel (Software)

• Kymatio: Scattering Transforms in Python, Journal of Machine Learning Research, 21(60):1−6, 2020.

Implémentations open-source (open-source implementations)

• Python (numpy-scipy) implementation of Angular Fourier Series (AFS) descriptors.
• PyTorch implementation of 3D solid-harmonic Scattering Transform.
• PyTorch implementation of Style Transfer with Relaxed Optimal Transport and Self-Similarity (STROTSS), additional Sinkhorn Earth Mover's distance implemented.

E-mail:prenom.nom@ens.fr
Bureau:Haut di DI - 4