We present a novel generative model based on a graph neural network and slot-attention components, which exceeds the performance of pre-existing baselines.

Nov 11, 2022 · We present a novel generative model based on a graph neural network and slot-attention components, which exceeds the performance of pre-existing baselines.

We split the task into two-step generative procedure of cardinality prediction followed by conditional set generation and choose appropriate permutation- ...

Nov 21, 2023 · To accelerate this task, we present a novel generative model based on a graph neural network and slot-attention components, which exceeds the ...

The simulation of particle physics data is a fundamental but computationally intensive ingredient for physics analysis at the Large Hadron Collider, ...

Dec 1, 2023 · The simulation of particle physics data is a fundamental but computationally intensive ingredient for physics analysis at the large Hadron ...

Nov 19, 2024 · In this study, we evaluate the performance of two set-conditional set generation approaches. Both methods rely on graph neural networks, as they ...

Set-Conditional Set Generation for Particle Physics · 1 Weizmann Institute of Science · 2 Technical University of Munich · 3 INFN and University of Genova · 4 ICEPP ...

Oct 14, 2023 · The simulation of particle physics data is a fundamental but computationally intensive ingredient for physics analysis at the Large Hadron ...

Nov 1, 2024 · To make this possible, we advance set-conditional set generation with diffusion models. Using a realistic, generic, and public detector ...