How Far Can Foundation Models Be Distilled for Efficient Jet Tagging?
cs.LG, hep-ex, hep-ph, physics.data-an
Submitted: 2026-10-21
Updated: 2026-10-21
Comments: 34 pages, 16 figures, 23 tables. Code and model weights: https://github.com/JayAgarwal25/distilling-jet-taggers
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Related papers
- Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
- AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
- Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
- Convergence issues in Relational Concept Analysis based on AOC-posets
- Beliefs Beyond Posteriors: The Predictive Cost of ELBO Binding
- Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks