7 papers
MSAlign: Aligning Molecule and Mass Spectra Foundation Models for Metabolite Identification
Paul Krzakala, Gabriel Melo, Camille Lançon +4
Accurately identifying metabolites i.e. small molecules from mass spectrometry data remains a core challenge in metabolomics, with broad applications in drug discovery, environment…
Finetuning-Free Diffusion Model with Adaptive Constraint Guidance for Inorganic Crystal Structure Generation
Auguste de Lambilly, Vladimir Baturin, David Portehault +4
Generative diffusion models have emerged as powerful tools for the discovery of inorganic crystal structures, yet steering their sampling process toward user-defined physical and c…
Conformal Graph Prediction with Z-Gromov-Wasserstein Distances
Gabriel Melo, Thibaut de Saivre, Anna Calissano +1
Supervised graph prediction addresses regression problems where the outputs are structured graphs. Although several approaches exist for graph-valued prediction, principled uncerta…
The quest for the GRAph Level autoEncoder (GRALE)
Paul Krzakala, Gabriel Melo, Charlotte Laclau +2
Although graph-based learning has attracted a lot of attention, graph representation learning is still a challenging task whose resolution may impact key application fields such as…
Learning Differentiable Surrogate Losses for Structured Prediction
Junjie Yang, Matthieu Labeau, Florence d'Alché-Buc
Structured prediction involves learning to predict complex structures rather than simple scalar values. The main challenge arises from the non-Euclidean nature of the output space,…
Restyling Unsupervised Concept Based Interpretable Networks with Generative Models
Jayneel Parekh, Quentin Bouniot, Pavlo Mozharovskyi +2
Developing inherently interpretable models for prediction has gained prominence in recent years. A subclass of these models, wherein the interpretable network relies on learning hi…