Seminars - page 3

The DIG team holds a seminar about every two weeks with speakers either from the team, or invited.

You can add the seminars to your calendar with this ics file, and get emails about future seminars by subscribing to our mailing-list.

If you would like to present your work at our seminar, please contact Nils.

Upcoming Seminars

AI, Proofs, and Programs

Tuesday, September 08, 2026 11:45, 4A301

Thomas Bonald

AI is transforming mathematics, raising profound questions: Can we trust AI-generated proofs? Do we understand them? And what role will mathematicians play in this new era? This talk explores these questions through the lens of proof checking, formal systems, and the elegant Curry-Howard correspondence—a deep connection between logic, computation, and the nature of mathematical truth.

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Past Seminars

Tabular foundation models: priors for numbers and strings

Tuesday, March 25, 2025 11:45, 4A301

Gaël Varoquaux (INRIA)

Deep-learning typically does not outperform tree-based models on tabular data. Often this may be explained by the small size of such datasets. For images, sound, text, the solution has be pretrained models, leading to foundation models, adapted and reused for many tasks. I will discuss the challenges to bring these ideas to tabular learning, and the progress that we have made, building priors for tables, ie columns of different natures, with numbers and strings.

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Neuro-symbolic approaches for the knowledge graph lifecycle

Tuesday, March 18, 2025 11:45, 4A301

Pierre Monnin (INRIA)

In the Web of Data, an increasing number of knowledge graphs (KGs) are concurrently published, edited, and accessed by human and software agents. Their wide adoption makes essential the tasks of their lifecycle: construction, refinement (e.g., matching, link prediction), mining, and usage to support applications (e.g., explainable AI, recommender systems). However, all these tasks require facing the inherent heterogeneity of KGs, e.g., in terms of granularities, vocabularies, and completeness. Besides, scalability issues arise due to their increasing size and combinatorial nature. In my talk, I will present my research on neuro-symbolic approaches for the KG lifecycle, intertwining domain knowledge from ontologies, deductive reasoning, analogical reasoning, and machine learning models. Throughout my presentation, I will show that such approaches enhance models by improving their semantic awareness, frugality, and the semantic interpretability of their latent representation space.

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