35 citations · 36 across the 8 of their papers we have counts for
4 papers · 1 filter
Going with the Flow: Approximating Banzhaf Values via Graph Neural Networks
Benjamin Kempinski, Tal Kachman
Computing the Banzhaf value in network flow games is fundamental for quantifying agent influence in multi-agent systems, with applications ranging from cybersecurity to infrastruct…
Explainability Techniques for Chemical Language Models
Stefan Hödl, William Robinson, Yoram Bachrach +2
Explainability techniques are crucial in gaining insights into the reasons behind the predictions of deep learning models, which have not yet been applied to chemical language mode…
Diffusion models with location-scale noise
Alexia Jolicoeur-Martineau, Kilian Fatras, Ke Li +1
Diffusion Models (DMs) are powerful generative models that add Gaussian noise to the data and learn to remove it. We wanted to determine which noise distribution (Gaussian or non-G…
Neural Payoff Machines: Predicting Fair and Stable Payoff Allocations Among Team Members
Daphne Cornelisse, Thomas Rood, Mateusz Malinowski +2
In many multi-agent settings, participants can form teams to achieve collective outcomes that may far surpass their individual capabilities. Measuring the relative contributions of…