Videos, slides and posters

[Geo-Sci-Info]

INTRODUCTION LECTURES

• Introduction and presentation of the conferences by Frederic Barbaresco. VIDEO

• Presentation of Geometric Sciences of Information and GSI 2021 by Frederic Barbaresco. VIDEO

LECTURES (90 min)

1. Langevin Dynamics

• 1.1 Langevin Dynamics: old and news : Eric Moulines . Part 1 : introduction to Markov chain Monte Carlo Methods VIDEO, Part 2 VIDEO

2. Computational Information Geometry:

• 2.1. Information Manifold modeled with Orlicz Spaces : Giovanni Pistone . VIDEO

• 2.2. Recent contributions to Distances and Information Geometry: a computational viewpoint : Frank Nielsen . VIDEO - SLIDES

3. Non-Equilibrium Thermodynamic Geometry

• 3.1. A variational perspective of closed and open systems: François Gay-Balmaz
• 3.2. Geometry of Non-Equilibrium Thermodynamics: a homogeneous Symplectic approach : Arjan Van Der Schaft . VIDEO- SLIDES

4. Geometric Mechanics

• 4.1. Galilean Mechanics and Thermodynamics of continua : Géry de Saxcé. VIDEO - SLIDES

• 4.2. Souriau-Casimir Lie Groups Thermodynamics and Machine Learning : Frederic Barbaresco. VIDEO - SLIDES

5. "Structure des Systèmes Dynamiques" (SSD) Jean-Marie Souriau’s book 50th Birthday Wikipedia page

• 5.1. Souriau Familly and "structure of motion": Jean-Marie Souriau, Michel Souriau, Paul Souriau and Etienne Souriau : Frederic Barbaresco . VIDEO - SLIDES

• 5.2. SSD Jean-Marie Souriau’s book 50th birthday: Géry de Saxcé SLIDES

KEYNOTES (60 min)

• Learning Physics from Data : Francisco Chinesta . VIDEO VIDEO - SLIDES

• Information Geometry and Integrable Systems : Jean-Pierre Françoise. VIDEO VIDEO - SLIDES

• Learning with Few Labeled Data : Pratik Chaudhari . VIDEO - SLIDES

• Information Geometry and Quantum Fields : Kevin Grosvenor SLIDES

• Port Thermodynamic Systems Control : Bernhard Maschke . VIDEO - SLIDES

• Dirac Structures in Nonequilibrium Thermodynamics : Hiroaki Yoshimura . VIDEO - SLIDES

• Thermodynamic efficiency implies predictive inference : Susanne Still . VIDEO - SLIDES

• Computational dynamics of reduced coupled multibody-fluid system in Lie group setting : Zdravko Terze . VIDEO - SLIDES

• Exponential Family by Representation Theory : Koichi Tojo . VIDEO - SLIDES

• Deep Learning as Optimal Control Problems and Structure Preserving Deep Learning : Elena Celledoni . VIDEO - SLIDES

• Contact geometry and thermodynamical systems : Manuel de León. VIDEO - SLIDES

• Diffeological Fisher Metric : Hông Vân Lê. VIDEO - SLIDES

• Mechanics of the probability simplex : Luigi Malagò. VIDEO - SLIDES

• Covariant Momentum Map Thermodynamics : Goffredo Chirco. VIDEO - SLIDES

• Sampling and statistical physics via symmetry : Steve Huntsman. VIDEO - SLIDES

• Geometry of Measure-preserving Flows and Hamiltonian Monte Carlo : Alessandro Barp. VIDEO - SLIDES

• Schroedinger's problem, Hamilton-Jacobi-Bellman equations and regularized Mass Transportation : Jean-Claude Zambrini. VIDEO - SLIDES

POSTERS

PDF of posters:

• Viscoelastic flows of Maxwell fluids with conservation laws - Sébastien Boyaval - POSTER
• Bayesian Inference on Local Distributions of Functions and Multi-dimensional Curves with Spherical HMC Sampling - Anis Fradi and Chafik Samir - POSTER
• Material modeling via Thermodynamics-based Artificial Neural Networks - Filippo Masi Ioannis Stefanou, Paolo Vannucci, Victor Maffi-Berthier - POSTER
• LEARNING THE LOW-DIMENSIONAL GEOMETRY OF THE WIRELESS CHANNEL - Paul Ferrand, Alexis Decurninge, Luis Garcia Ordóñez and Maxime Guillaud - POSTER
• A Hyperbolic approach for learning communities on graphs - Hatem Hajri, Thomas Gerald and Hadi Zaatiti - POSTER
• UNSUPERVISED OBJECT DETECTION FOR TRAFFIC SCENE ANALYSIS - Bruno Sauvalle (superviseur: ARNAUD DE LA FORTELLE) - POSTER
• Hard Shape-Constrained Kernel Regression - Pierre-Cyril Aubin-Frankowski and Zoltán Szabó - POSTER
• CONSTRAINT-BASED REGULARIZATION OF NEURAL NETWORKS - Benedict Leimkuhler, Timothée Pouchon, Tiffany Vlaar, Amos Storkey - POSTER
• CONNECTING STOCHASTIC OPTIMIZATION WITH SCHRÖDINGER EVOLUTION WITH RESPECT TO NON HERMITIAN HAMILTONIANS - C. Couto, J. Mourão, J.P. Nunes and P. Ribeiro - POSTER
• Geomstats: A Python Package for Geometry in Machine Learning and Information Geometry - Nina Miolane, Nicolas Guigui1, Alice Le Brigant, Hadi Zaatiti, Christian Shewmake, Hatem Hajri, Johan Mathe, Benjamin Hou, Yann Thanwerdas, Stefan Heyder, Olivier Peltre, Niklas Koep, Yann Cabanes, Thomas Gerald, Paul Chauchat, Daniel Brooks, Bernhard Kainz, Claire Donnat, Susan Holmes, Xavier Pennec - POSTER
• Fast High-order Tensor Learning Based on Grassmann Manifold - O.KARMOUDA, R.BOYER and J.BOULANGER - POSTER
• A Geometric Interpretation of Stochastic Gradient Descent in Deep Learning and Bolzmann Machines - Rita Fioresi and Pratik Chaudhari - POSTER