Joint Structures and Common Foundations of
Statistical Physics, Information Geometry and Inference for Learning (SPIGL'20)

Date: 26th July to 31st July 2020
(The workshop starts on the morning of 27th July and close on 31st July 4pm)

Location: Ecole de Physique des Houches
Ecole de Physique des Houches
https://houches.univ-grenoble-alpes.fr/
149 Chemin de la Côte, F-74310 Les Houches, France
(+33/0) 4 57 04 10 40

What is new:

  • (March 18) Poster of Les Houches SPIGL (pdf)
  • (March 11) Scientific tentative programme released

    (pdf)

    Documentation/information (pdf)

    Practical information (pdf)


    View in a new tab the video presentation: Video of Les Houches

    Download the poster in pdf


    [Registration] [registration fee] [Arrival/Departure] [Programme] [Excursion]



    Scientific rationale:

    In the middle of the last century, Léon Brillouin in "The Science and The Theory of Information" or André Blanc-Lapierre in "Statistical Mechanics" forged the first links between the Theory of Information and Statistical Physics as precursors. In the context of Artificial Intelligence, machine learning algorithms use more and more methodological tools coming from the Physics or the Statistical Mechanics. The laws and principles that underpin this Physics can shed new light on the conceptual basis of Artificial Intelligence. Thus, the principles of Maximum Entropy, Minimum of Free Energy, Gibbs-Duhem's Thermodynamic Potentials and the generalization of François Massieu's notions of characteristic functions enrich the variational formalism of machine learning. Conversely, the pitfalls encountered by Artificial Intelligence to extend its application domains, question the foundations of Statistical Physics, such as the construction of stochastic gradient in large dimension, the generalization of the notions of Gibbs densities in spaces of more elaborate representation like data on homogeneous differential or symplectic manifolds, Lie groups, graphs, tensors, .... Sophisticated statistical models were introduced very early to deal with unsupervised learning tasks related to Ising-Potts models (the Ising-Potts model defines the interaction of spins arranged on a graph) of Statistical Physics. and more generally the Markov fields. The Ising models are associated with the theory of Mean Fields (study of systems with complex interactions through simplified models in which the action of the complete network on an actor is summarized by a single mean interaction in the sense of the mean field). The porosity between the two disciplines has been established since the birth of Artificial Intelligence with the use of Boltzmann machines and the problem of robust methods for calculating partition function. More recently, gradient algorithms for neural network learning use large-scale robust extensions of the natural gradient of Fisher-based Information Geometry (to ensure reparameterization invariance), and stochastic gradient based on the Langevin equation (to ensure regularization), or their coupling called "Natural Langevin Dynamics". Concomitantly, during the last fifty years, Statistical Physics has been the object of new geometrical formalizations (contact or symplectic geometry, ...) to try to give a new covariant formalization to the thermodynamics of dynamic systems. We can mention the extension of the symplectic models of Geometric Mechanics to Statistical Mechanics, or other developments such as Random Mechanics, Geometric Mechanics in its Stochastic version, Lie Groups Thermodynamic, and geometric modeling of phase transition phenomena. Finally, we refer to Computational Statistical Physics, which uses efficient numerical methods for large-scale sampling and multimodal probability measurements (sampling of Boltzmann-Gibbs measurements and calculations of free energy, metastable dynamics and rare events, ...) and the study of geometric integrators (Hamiltonian dynamics, symplectic integrators, ...) with good properties of covariances and stability (use of symmetries, preservation of invariants, ...). Machine learning inference processes are just beginning to adapt these new integration schemes and their remarkable stability properties to increasingly abstract data representation spaces. Artificial Intelligence currently uses only a very limited portion of the conceptual and methodological tools of Statistical Physics. The purpose of this conference is to encourage constructive dialogue around a common foundation, to allow the establishment of new principles and laws governing the two disciplines in a unified approach. But, it is also about exploring new « chemins de traverse ».

    Organizers:

    Registration:

    For registration and for Poster Submission, please use the Easychair conference system:
    https://easychair.org/conferences/?conf=spig20
    Registration deadline : May 31st 2020.
    As Les Houches capacity is limited to 70 participants, preference will be given to first registrations and registrations with Poster.

    To submit a short paper or poster, please use the Easychair conference system:
    https://easychair.org/conferences/?conf=spig20 You need to add a cover page in your pdf with the following fields requires by Les Houches:

    - Last name:
    - First name:
    - Arrival date:
    - Departure date:
    - Organization:
    - City:
    - Country:
    - Email:
    - Participation type: oral speaker, poster presentation, participant (no talk/no poster), accompanying person
    - Gender:
    - Accompanying person (optional):
    - Food restrictions:
    - Mobility:
    - Remarks (optional):
    - Disabilities or reduced mobility: Yes/no. If yes, please precise.
    

  • Registration payment:

    Information for the payment of the registration fee (pdf)

    Registration fees for Summer Week is 450 euros, including catering (bedroom and 3 meals a day) and all accommodation on site: https://www.houches-school-physics.com/practical-information/facilities/ https://www.houches-school-physics.com/practical-information/your-stay/
    Registration will be paid at Les Houches reception desk at your arrival by credit card (or VAD payment of your lab).
    Any registration canceled less than two weeks before the arrival date will be due.
    The school has 5 double rooms only for accompanying persons. Accompanying persons should pay same registration fees.

    Arrival/Departure:

    The arrival is Sunday July 26th starting from 3:00 pm. On the day of arrival, only the evening meal is planned. On Sunday, the secretariat is open from 6:00 pm to 7:30 pm. Summer Week will be closed Friday July 31st at 4 pm.

    Access to Les Houches:

    https://www.houches-school-physics.com/practical-information/access/
    Ecole de Physique des Houches, 149 Chemin de la Côte, F-74310 Les Houches, France Les Houches is a village located in Chamonix valley, in the French Alps. Established in 1951, the Physics School is situated at 1150 m above sea level in natural surroundings, with breathtaking views on the Mont-Blanc mountain range. https://houches-school-physics.com

    Scientific Program:


    Scientific tentative programme

    Excursion:

    Wednesday afternoon is free. Excursion could be organized to The Mer de Glace (Sea of Ice), that is the largest glacier in France, 7 km long and 200m deep and is one of the biggest attractions in the Chamonix Valley: https://www.chamonix.net/english/leisure/sightseeing/mer-de-glace

    Last updated, Feb. 22 2020 by Frank.