How to scale your HEP ML models: A recipe for robust architecture comparisons at scale
Much of the recent progress in machine learning domains such as language models has come from scaling laws that predict performance as a function of training effort. In high-energy physics (HEP) similar behavior has now been observed. To aid further study, we present a systematic procedure to derive robust scaling laws...
M. Vigl, N. Pond, J. Barr et al.
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