activity
20242026
collaborators

9 papers

cs.LG2026

The Boolean Power of ReLU

Pablo Barceló, Floris Geerts, Matthias Lanzinger +2

We prove that, on finite simple undirected graphs equipped with a single Boolean node feature, the Boolean queries expressible in -MPLang, for any collection of eventually c…

cs.LG2026

AutoGrable: What Is a Good Graph for a Table?

Tamara Cucumides, Floris Geerts

Graph learning presupposes a graph, and tables and relational databases do not come with one. Applying a GNN to them requires deciding which entities become nodes, which of them to…

cs.CL2026

Latent Bridges for Multi-Table Question Answering

Simone Varriale, Tamara Cucumides, Floris Geerts +1

We introduce GRAB, a constructor-encoder-bridge pipeline for table question answering. Our method lifts relational data into an heterogeneous graph, encodes it via message passing,…

quant-ph2026

Quadratic Sums-of-Powers for Fixed-Parameter Tractable Quantum-Circuit Simulation

Alexis de Colnet, Floris Geerts, Rihan Hai +4

Strongly simulating a quantum circuit, that is, computing an output amplitude, can be done by summing the circuit's Feynman paths: a weighted count over assignments to Boolean path…

cs.LG2026

Which Algorithms Can Graph Neural Networks Learn?

Solveig Wittig, Antonis Vasileiou, Robert R. Nerem +4

In recent years, there has been growing interest in understanding neural architectures' ability to learn to execute discrete algorithms, a line of work often referred to as neural…

cs.AI2025

DeepGraphLog for Layered Neurosymbolic AI

Adem Kikaj, Giuseppe Marra, Floris Geerts +2

Neurosymbolic AI (NeSy) aims to integrate the statistical strengths of neural networks with the interpretability and structure of symbolic reasoning. However, current NeSy framewor…