21 Mars 2024, Antoine Georges (Collège de France, Paris and Flatiron Institute, New York) Title: Applications of Machine Learning and Neural Networks to Quantum Systems Abstract: Applications of learning algorithms using deep neural networks have developed considerably recently, often with spectacular results. The physics of complex quantum systems is no exception, with multiple applications that constitute a new field of research. Examples include the representation and optimization of wave functions of quantum systems with large numbers of degrees of freedom (neural quantum states), the determination of wave functions from measurements (quantum tomography), and applications […]

04 Avril 2024, Lénaïc Chizat (EPFL) Title: A Formula for Feature Learning in Large Neural Networks Abstract: Deep learning succeeds by doing hierarchical feature learning, but tuning hyperparameters such as initialization scales, learning rates, etc., only give indirect control over this behavior. This calls for theoretical tools to predict, measure and control feature learning. In this talk, we will first review various theoretical advances (signal propagation, infinite width dynamics, etc) that have led to a better understanding of the subtle impact of hyperparameters and architectural choices on the training dynamics. We will then introduce […]

Michael Jordan (UC Berkeley and INRIA Paris) Collaborative Learning, Information Asymmetries, and Incentives This colloquium is organized around data sciences in a broad sense, with the goal of bringing together researchers with diverse backgrounds (including mathematics, computer science, physics, chemistry and neuroscience) but a common interest in dealing with complex, large scale, or high dimensional data. More information can be found on the web page of the seminar: https://data-ens.github.io/seminar/