Papers with Code 아카이브
Jet Tagging
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Jet tagging is the process of identifying the type of elementary particle that initiates a "jet", i.e., a collimated spray of outgoing particles. It is essentially a classification task that aims to distinguish jets arising from particles of interest, such as the Higgs boson or the top quark, from other less interesting types of jets.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/AnnikaStein/Adversarial-Training-for-Jet-Tagging
공식 코드Improving Robustness of Jet Tagging Algorithms with Adversarial Training
https://github.com/ViniciusMikuni/PCT_HEP
공식 코드https://github.com/aaronw5/Interpreting-Transformers-for-Jet-Tagging
공식 코드https://github.com/abogatskiy/pelican-nano
공식 코드19 Parameters Is All You Need: Tiny Neural Networks for Particle Physics
https://github.com/calad0i/HGQ
공식 코드Gradient-based Automatic Mixed Precision Quantization for Neural Networks On-Chip
https://github.com/calad0i/HGQ-demos
공식 코드Gradient-based Automatic Mixed Precision Quantization for Neural Networks On-Chip
https://github.com/farakiko/xai4hep
공식 코드Do graph neural networks learn traditional jet substructure?
https://github.com/fdreyer/LundNet
공식 코드https://github.com/giorgiocerro/jetlov
공식 코드JetLOV: Enhancing Jet Tree Tagging through Neural Network Learning of Optimal LundNet Variables
https://github.com/hftsoi/symbolnet
공식 코드SymbolNet: Neural Symbolic Regression with Adaptive Dynamic Pruning for Compression
https://github.com/hqucms/weaver-core
공식 코드https://github.com/jet-universe/particle_transformer
공식 코드https://github.com/mikelcasals/ggc_4_qgnns
공식 코드https://github.com/ml4sci/qmlhep
공식 코드https://github.com/murnanedaniel/geometricattention
공식 코드Graph Structure from Point Clouds: Geometric Attention is All You Need
https://github.com/royforestano/2023_gsoc_ml4sci_qmlhep_gnn
공식 코드A Comparison Between Invariant and Equivariant Classical and Quantum Graph Neural Networks
https://github.com/ryanliu30/kd4jets
공식 코드Efficient and Robust Jet Tagging at the LHC with Knowledge Distillation
https://github.com/taolicheng/VAE-Jet
공식 코드https://github.com/uhh-pd-ml/omnijet_alpha
공식 코드OmniJet-$α$: The first cross-task foundation model for particle physics
https://github.com/yvsemlani/pcn-jet-tagging
공식 코드https://github.com/Jai2500/particlenet
https://github.com/StefReck/MEdgeConv
https://github.com/WangYueFt/dgcnn
https://github.com/deepjets/deepjets
https://github.com/hqucms/ParticleNet
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.