Reconstructing short-lived particles using hypergraph representation learning

arXiv:2402.10149 · hep-ph, cs.LG, hep-ex · Submitted 2024-02-15 · Read on arXiv

hep-ph, cs.LG, hep-ex

Submitted: 2024-02-15

Updated: 2025-02-12

Code: https://github.com/tzuhanchang/HyPER

Terminology

• Hypergraph — 39× in this paper · explained in 2 episodes
• Representation Learning — 10× in this paper · explained in 2 episodes
• Accuracy — 6× in this paper · explained in 1 episode
• Transformer — 5× in this paper · explained in 2 episodes
• Standard Model — 4× in this paper
• Aggregation — 3× in this paper · explained in 1 episode
• Parameter Efficiency — 3× in this paper · explained in 3 episodes
• Adam — 2× in this paper · explained in 2 episodes
• Attention Mechanism — 2× in this paper · explained in 2 episodes
• Benchmark — 2× in this paper · explained in 2 episodes
• Covariant Particle Transformer — 2× in this paper
• Embeddings — 2× in this paper · explained in 2 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Activation Function — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Clustering — 1× in this paper · explained in 2 episodes
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Pruning — 1× in this paper · explained in 4 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Resource Constraints — 1× in this paper · explained in 3 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes

Sources

• Deep Hypergraph Structure Learning
• How Attentive are Graph Attention Networks?
• Deep Learning using Rectified Linear Units (ReLU)
• Understanding deep learning requires rethinking generalization
• Neural Message Passing for Quantum Chemistry
• Relational inductive biases, deep learning, and graph networks
• Relational inductive bias for physical construction in humans and machines
• Graph networks as learnable physics engines for inference and control
• Improving neural networks by preventing co-adaptation of feature detectors

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