The natural world is full of complex systems characterized by intricate relations between their components: from social interactions between individuals in a social network to electrostatic interactions between atoms in a protein. Topological Deep Learning (TDL) provides a framework to process and extract knowledge from data associated with these systems, such as predicting the social community to which an individual belongs or predicting whether a protein can be a reasonable target for drug development. By extending beyond traditional graph-based methods, TDL incorporates higher-order relational structures, providing a new lens to tackle challenges in applied sciences and beyond. This talk will introduce the core principles of TDL and provide a comprehensive review of its rapidly growing literature, with a particular focus on neural network architectures and their performance across various domains. I will present open-source implementations that make TDL methods more accessible and practical for real-world applications. All in all, this talk will showcase how TDL models can effectively capture and reason about the complexity of real-world systems, while highlighting the remaining challenges and exciting opportunities for future advancements in the field.
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Classical descriptions of matter present many fluid mechanical and kinetic theory dynamical systems. These include, e.g., the Navier-Stokes-Fourier system, the Cahn-Hilliard-Navier-Stokes system for multiphase fluid flow, and various types of collisional kinetic theories for gaseous and plasma modeling. A desirable feature of such modeling is thermodynamic consistency, i.e., conservation of energy and production of entropy, in agreement with the first and second laws of thermodynamics. Metriplectic dynamics is a kind of dynamical system (finite or infinite) that encapsulates in a geometrical formalism such thermodynamic consistency. An algorithmic procedure for building such theories is based on the metriplectic 4-bracket, a bracket akin to the Poisson bracket that maps phase space functions to another. However, the 4-bracket maps 4 such functions and has algebraic curvature symmetries. Metriplectic 4-brackets can be constructed using the Kulkarni-Nomizu product or via a pure Lie algebraic formalism based on the Koszul connection. The formalism algorithmically produces many known and new dynamical systems, and it provides a pathway for constructing structure preserving numerical algorithms.
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Many statistical learning and inference methods, from Bayesian inference to empirical risk minimization, can be unified through a variational perspective that balances empirical risk and prior knowledge. We introduce a new, general class of variational methods based on Fenchel-Young (FY) losses. These losses, derived using Fenchel conjugation (a central tool of convex analysis), generalize the Kullback-Leibler divergence and encompass Bayesian as well as classical variational learning. This FY framework provides generalized notions of free energy, evidence, evidence lower bound, and posterior, while still enabling standard optimization techniques like alternating minimization and gradient backpropagation. This allows learning a broader class of models than previous variational formulations. This talk will review FY losses and then detail this new generalized variational inference approach to machine learning.
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In this talk we show how the merging of different languages pertaining to K-theory, algebraic geometry, noncommutative geometry and gauge field theory can be fruitfully integrated to give a unified version of discrete differential geometry on graphs.
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The information geometry of parametric statistical models is built around two central constructions: the Fisher-Rao metric and the family of dual $\alpha$-connections. Both have non-parametric counterparts, which we discuss in this talk. We introduce the $L^p$-Fisher-Rao metrics, a family of Finsler metrics that generalize the Fisher-Rao metric, and study their links with the $\alpha$-connections. We show that their geodesics coincide on the space of smooth densities, for $p=2/(1-\alpha)$, while they differ on the space of probability densities. This gives a new variational interpretation of $\alpha$-geodesics as being energy minimizing curves. Joint work with Martin Bauer, Yuxiu Lu and Cy Maor.
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Sadi Carnot stands as a symbol of the “Ingénieur-Savant” who have profoundly enriched the fertile ground of French innovation. The discipline of thermodynamics, which he founded, today underpins information theory, the development of future quantum computers, and artificial intelligence, as well as climate science, pillars upon which rest both our industrial policies and the future of our societies. Sadi Carnot formulated the second law of thermodynamics in his treatise, “Reflections on the Motive Power of Fire “(1824), that initially received little attention, but emerged from obscurity years later thanks to the works of Émile Clapeyron, William Thomson (later Lord Kelvin), and Rudolf Clausius. Sadi Carnot drew upon a remarkably wide range of knowledge, nourished by an eclectic and inquisitive mind. Taken by illness only a few years after publishing his Reflections, he remains forever preserved in the brilliance of his genius. The explanation of thermodynamics through geometric models was initiated by seminal figures such as Gibbs, Reeb, Carathéodory and Souriau. We shall trace the narrative of the geometric models, from Carathéodory (1909) to Souriau (1969), that have sought to re-establish Sadi Carnot’s thermodynamics upon new foundational variational principles. As observed by Vladimir Arnold, “Every mathematician knows it is impossible to understand an elementary course in thermodynamics. The reason is that thermodynamics is based, as Gibbs has explicitly proclaimed, on a rather complicated mathematical theory, on the contact geometry« . A seminal contribution of Carathéodory lies in the introduction of a differential equation that governs the infinitesimal changes in state functions as the system undergoes infinitesimal transitions between states. This equation is of paramount importance as it embodies the first and second laws of thermodynamics in a single, unified mathematical expression. Carathéodory’s axiomatization is intricately linked with the language of differential geometry, later interpretated as contact geometry and has greatly influenced Misha Gromov geometer who introduced the concept of Carnot-Carathéodory spaces. One of the pivotal results of Carathéodory’s axiomatization is his theorem, which asserts that, given certain conditions the second law of thermodynamics, as formulated by Clausius, can be derived directly from the fundamental thermodynamic relation. While Carnot thermodynamics was primarily concerned with systems in equilibrium, Carathéodory’s axiomatization extends far beyond this, offering a more general framework that accommodates systems in non-equilibrium states. Only recently, however, has the Souriau’s Symplectic Foliation Model, introduced within the domain of geometric statistical mechanics, provided a geometric definition of entropy as an invariant Casimir function on symplectic leaves, specifically, the coadjoint orbits of the Lie group acting on the system, where these orbits are interpreted as level sets of entropy. We present a symplectic foliation interpretation of thermodynamics, based on Jean-Marie Souriau’s Lie Groups Thermodynamics. This model offers a Lie algebra cohomological characterisation of entropy, viewed as an invariant Casimir function in the coadjoint representation.
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