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
