5 papers
Graph Matching Relaxations and Amortization for Supervised Graph Prediction
Federico Méndez, Paul Krzakala, Gabriel Melo +3
End-to-end Supervised Graph Prediction (SGP) requires a permutation-invariant loss to compare predicted and target graphs with arbitrary node orderings. Such losses typically invol…
MSAlign: Aligning Molecule and Mass Spectra representations 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…
SOTAlign: Semi-Supervised Alignment of Unimodal Vision and Language Models via Optimal Transport
Simon Roschmann, Paul Krzakala, Sonia Mazelet +2
The Platonic Representation Hypothesis posits that neural networks trained on different modalities converge toward a shared statistical model of the world. Recent work exploits thi…
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…
Any2Graph: Deep End-To-End Supervised Graph Prediction With An Optimal Transport Loss
Paul Krzakala, Junjie Yang, Rémi Flamary +3
We propose Any2graph, a generic framework for end-to-end Supervised Graph Prediction (SGP) i.e. a deep learning model that predicts an entire graph for any kind of input. The frame…