Note: not all seminars are recorded, the main seminar webpage is there. To receive the TAU seminar announcements, you can subscribe to the associated mailing-list by clicking here. To receive the announcements of the GT Deep Net seminars, you can subscribe to the associated mailing-list by clicking there.
Seminar videos:
A Principle of Least Action for the Training of Neural Networks, by Ahmed Skander Karkar (Criteo), Tuesday 13th of October 2020
The Incomplete Rosetta Stone Problem: Multi-View Nonlinear ICA, with applications to neuroimaging, by Luigi Gresele (Max Planck Institute for Intelligent Systems and Biological Cybernetics, Tübingen), Friday 29 of November 2019
DeepType: résolution référentiell'entités multilingues par 'évolution de systèmes de types neuronaux, by Jonathan Raiman (OpenAI / TAU), Tuesday 12 of November 2019
Dynamic Time Lag Regression: Predicting What & When, by Mandar Chandorkar (TAU / Centrum Wiskunde & Informatica (CWI), Amsterdam), Friday 08 of November 2019
Perspectives for causal inference on time series in Earth system sciences, by Jakob Runge (German Aerospace Center, Institute of Data Science, Jena), Tuesday 1st of October
From abstract items to latent space to observed data and back: Compositional Variational Auto-Encoder, by Victor Berger (TAU), Thursday 12 of September 2019
Réseaux Neuronaux Aléatoires - Solutions en Forme Produit, Apprentissage et Apprentissage Profond, Applications, by Erol Gelenbe (Imperial College), Wednesday 15th of May 2019
DeepInsight - An examination of the class decision functions learned by deep nets, by Saumya Jetley (University of Oxford), Tuesday, 26th of March 2019
A short introduction to formal methods and their applications for Robust Deep networks, by Julien Girard-Satabin (TAU/CEA-list), Thursday 13th of December 2018
Artificial creative intelligence: variational inference and deep learning for modeling musical creativity, by Philippe Esling (IRCAM), Thursday 15th of November 2018
Statistical inference for high-dimensional data & application to brain imaging, by Bertrand Thirion (Parietal team, Neurospin, INRIA/CEA), Tuesday 17th of April 2018
TrackML : The High Energy Physics Tracking Challenge, by David Rousseau (Laboratoire de l'Accélérateur Linéaire (LAL), Orsay), Tuesday 13th of March 2018
Linear-time Divergence Measures with Applications in Hypothesis Testing, by Zoltan Szabo (CMAP & DSI, École Polytechnique), Tuesday 13th of February 2018
Learning and Exploiting Deep Tractable Probabilistic Models, by Antonio Vergari (LACAM, University of Bari 'Aldo Moro', Italy), Tuesday 19th of December 2017
Mean-Field Framework for Unsupervised Learning with Boltzmann Machines, by Marylou Gabrié (ENS Paris, Laboratoire de Physique Statistique), Wednesday 22nd of November 2017
Machine Learning Algorithms for Climate Informatics, Sustainability, and Social Good, by Claire Monteleoni (CNRS-LAL / George Washington University), Thursday 9th of November 2017
A quasi-Bayesian perspective to NMF: theory and applications,
by Benjamin Guedj (MODAL project-team of Inria Lille [MOdels for Data
Analysis and Learning] / Laboratoire Paul Painlevé, University of
Lille), Tuesday 24th of October 2017
Perceiving complexity: examples from the mouse auditory system by Brice Bathellier, and Inferring neuronal electrical activity from calcium signals: model-based vs. learning methods (MLspike) by Thomas Deneux, Tuesday 4th of July 2017
