96 citations · 121 across the 34 of their papers we have counts for
21 papers · 1 filter
Look Before You Lift: Visual and Quantitative Diagnostics for Topological Deep Learning
Mathilde Papillon, Guillermo Bernárdez, Álvaro Ballón Barreiro +4
Topological deep learning (TDL) methods rely on lifting raw data into higher-order discrete domains such as simplicial complexes, cell complexes, and hypergraphs. In practice, this…
OgBench: A Framework for Evaluating Graph Neural Networks on Omics Data
Louisa Cornelis, Johan Mathe, Louis Van Langendonck +2
Graph Neural Networks (GNNs) have become the dominant framework for inductive graph-level learning. Yet most benchmarks focus on the regime , where the number of graphs $n…
bispectrum: Selective -Bispectra Made Practical
Johan Mathe, Adele Myers, Simon Mataigne +1
Many machine learning tasks are invariant under the action of a group of transformations: signal classification can be invariant under translations, image classification under…
Sequential Group Composition: A Window into the Mechanics of Deep Learning
Giovanni Luca Marchetti, Daniel Kunin, Adele Myers +2
How do neural networks trained over sequences acquire the ability to perform structured operations, such as arithmetic, geometric, and algorithmic computation? To gain insight into…
GraphUniverse: Synthetic Graph Generation for Evaluating Inductive Generalization
Louis Van Langendonck, Guillermo Bernárdez, Nina Miolane +1
A fundamental challenge in graph learning is understanding how models generalize to new, unseen graphs. While synthetic benchmarks offer controlled settings for analysis, existing…
Alternating Gradient Flows: A Theory of Feature Learning in Two-layer Neural Networks
Daniel Kunin, Giovanni Luca Marchetti, Feng Chen +5
What features neural networks learn, and how, remains an open question. In this paper, we introduce Alternating Gradient Flows (AGF), an algorithmic framework that describes the dy…