Séminaire Data de l’ENS

Thursday December 15 2022, Wolfram Pernice (University of Münster) Title: Computing beyond Moore's law with photonic hardware Abstract: Conventional computers are organized around a centralized processing architecture, which is well suited to running sequential, procedure-based programs. Such an architecture is inefficient for computational models that are distributed, massively parallel and adaptive, most notably those used for neural networks in artificial intelligence. In these application domains demand for high throughput, low latency and low energy consumption is driving the development of not only new architectures, but also new platforms for information processing. Photonic circuits are […]

Thursday 2nd of February 2022, 12h00-13h00 (Paris time), room Amphi Jaures (29 Rue d'Ulm). Remi Gribonval (INRIA) Title: Rapture of the deep: highs and lows of sparsity in a world of depths Abstract: Promoting sparse connections in neural networks is natural to control their complexity. Besides, given its thoroughly documented role in inverse problems and variable selection, sparsity also has the potential to give rise to learning mechanisms endowed with certain interpretability guarantees. Through an overview of recent explorations around this theme, I will compare and contrast classical sparse regularization for inverse problems […]

29 March 2023, Thomas Serre (Brown University) Title: Feedforward and feedback processes in visual processing Abstract: Progress in deep learning has spawned great successes in many engineering applications. As a prime example, convolutional neural networks, a type of feedforward neural networks, are now approaching - and sometimes even surpassing - human accuracy on a variety of visual recognition tasks. In this talk, however, I will show that these neural networks (and recent extensions) exhibit a limited ability to solve seemingly simple visual reasoning problems. Our group has developed a computational neuroscience model of the […]

25 May 2023, 13h00-14h00 (Paris time), room Amphi Jaures (29 Rue d'Ulm). Valentin De Bortoli (CNRS and ENS) Title: Generative modelling with diffusion: theory and practice Abstract: Generative modeling is the task of drawing new samples from an underlying distribution known only via an empirical measure. There exists a myriad of models to tackle this problem with applications in image and speech processing, medical imaging, forecasting and protein modeling to cite a few. Among these methods score-based generative models (or diffusion models) are a new powerful class of generative models that exhibit remarkable […]

04 October 2023, 12h30-13h30 (Paris time), room Amphi Jaures (29 Rue d'Ulm). Julia Kempe (NYU Centre for Data Science and Courant Institute) Title: Towards Understanding Adversarial Robustness Abstract: Adversarial vulnerability of neural nets, their failure under small, imperceptible perturbations, and subsequent techniques to create robust models have attracted significant attention; yet we still lack a full understanding of this phenomenon. In this talk I will introduce the problem and current defenses and then explore how tools and insights coming from statistical physics, in particular certain infinite-width limits of neural nets, help shed more […]

Noah A. Smith (University of Washington) Breaking Down Language Models “Language models are the only thing we have in natural language processing that could be considered scientific.” A collaborator of mine said this more than a decade ago, long before LMs emerged as the single most important technology to come out of our field. In these exciting times, I seek both to make the study of LMs more scientific, and to make LMs more practically beneficial. In this talk, I’ll first draw from recent work from my UW group that starts […]

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/

Luca Biferale (Università degli Studi di Roma Tor Vergata) Title:Data driven tools for Lagrangian TurbulenceAbstract: We present a stochastic method for generating and reconstructing complex signals along the trajectories of small objects passively advected by turbulent flows . Our approach makes use of generative Diffusion Models, a recently proposed data-driven machine learning technique. We show applications to 3D tracers and inertial particles in highly turbulent flows, 2D trajectories from NOAA’s Global Drifter Program and dynamics of charged particles in astrophysics. Supremacy against linear decomposition and Gaussian Regression Processes is analyzed in terms […]

Jean-Rémi King (CNRS, ENS & Meta AI) Title:AI and Neuroscience: in search of the laws of intelligenceAbstract: In just a few years, AI has transitioned from a specialized field into a transformative force for industries and society. Beyond this technical progress, the development of AI provides a new paradigm to understand the intricate workings of the human brain. To illustrate this, we will delve into a series of experiments that systematically compare deep learning algorithms with the human brain in response to images, sounds, and texts. These comparisons consistently show a partial […]

Michele Ceriotti (EPFL) Title: Between physics and scaling: inductive biases in atomistic machine learningAbstract: Machine-learning techniques are often applied to perform "end-to-end" predictions, making black-box estimatesof a property of interest using only a coarse description of the corresponding inputs.In contrast, atomic-scale modeling of matter is most useful when it allows one to gather a mechanistic insightinto the microscopic processes that underlie the behavior of molecules and materials.In this talk I will provide an overview of the progress that has been made combining these two philosophies,using data-driven techniques to build surrogate models […]