Transfer learning boosts AI model efficiency in high-energy physics

Researchers have explored transfer learning techniques to improve machine learning model performance in high-energy physics. By pre-training models on computationally cheaper, fast-simulated data and then adapting them to more realistic, fully simulated datasets, they found significant improvements. This approach typically halved the amount of target-domain training data required across various tasks like classification and jet tagging, demonstrating the value of reusable scientific assets. AI

影响 Enables more efficient training of AI models for scientific discovery by reducing data requirements.

排序理由 The cluster contains an academic paper detailing a new methodology and experimental results in a scientific domain. [lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

• ATLAS Open Data
• Graph Neural Networks
• High-Energy Physics
• LHC
• Machine Learning
• Transfer Learning
• Transformer-based architectures
• Dense Neural Networks
• Full Simulation

• 论文
• 其他

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