22 papers
Emergent topological structure in spontaneous brain-organoid activity
Eve Bodnia, Margaux Basart, Sofie Hai +5
Neural activity is widely held to organize on low-dimensional structure embedded in a high-dimensional state space. Persistent homology reads such structure directly from the patte…
Selective Disk Bispectrum: A Complete and Rotation Invariant Image Descriptor
Adele Myers Lantow, Nina Miolane
Rotation invariance is a fundamental requirement across many computer vision tasks. Historically, this inductive bias has been encoded through hand-crafted rotation-invariant repre…
HOPSE: Scalable Higher-Order Positional and Structural Encoder for Combinatorial Representations
Guillermo Bernárdez, Marco Montagna, Louis Van Langendonck +7
While Graph Neural Networks (GNNs) have proven highly effective at modeling relational data, pairwise connections cannot fully capture multi-way relationships naturally present in…
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…
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…
Projecting Latent RL Actions: Towards Generalizable and Scalable Graph Combinatorial Optimization
Franco Terranova, Guillermo Bernardez, Albert Cabellos-Aparicio +2
Graph combinatorial optimization (GCO) has attracted growing interest, as many NP-hard problems naturally admit graph formulations, yet their combinatorial explosion renders exact…