Papers › 19 Parameters Is All You Need: Tiny Neural Networks for Particle Physics
19 Parameters Is All You Need: Tiny Neural Networks for Particle Physics
Alexander Bogatskiy, Timothy Hoffman, Jan T. Offermann
As particle accelerators increase their collision rates, and deep learning solutions prove their viability, there is a growing need for lightweight and fast neural network architectures for low-latency tasks such as triggering. We examine the potential of one recent Lorentz- and permutation-symmetric architecture, PELICAN, and present its instances with as few as 19 trainable parameters that outperform generic architectures with tens of thousands of parameters when compared on the binary classification task of top quark jet tagging.
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Code
Syntology Ran 17 of 17 code samples harvested from 1 repository linked to this paper; 0 have no recorded run. Of those that ran: 3 ran · our draft was wrong; 1 ran · fixture could not drive it; 13 ran with no contract checked.
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Code Syntology ran Syntology
17 samples harvested; 17 ran; 0 honoured the contract we drafted; 0 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 3D Assembly | DeepCAD | A | 1-1 | mm | #1 of 1 | Archive leaderboard | report |
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