In PBT, a population of workers concurrently train their respective neural networks. The workers regularly explore the hyperparameter space by mutating their hyperparameters while training. With the same amount of resources, PBT can outperform random hyperparameter search on many important problems [13, 6, 12].
Sep 28, 2021
Sep 28, 2021 · PBT trains a population of neural networks concurrently, frequently mutating their hyperparameters throughout their training. However, the ...
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Faster Improvement Rate PBT is presented, which derives a novel fitness metric and uses it to make some of the population members focus on long-term ...
PBT trains a population of neural networks concurrently, frequently mutating their hyperparameters throughout their training. However, the decision mechanisms ...
Oct 5, 2021 · A DeepMind research team proposes Faster Improvement Rate PBT (FIRE PBT) for Population Based Training (PBT), an automated hyperparameter ...
Aug 12, 2023 · PBT appears to drastically improve optimization across the board at the cost of one or more of batch size/training steps/model complexity/other compute ...
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Oct 5, 2021 · A DeepMind research team proposes Faster Improvement Rate PBT (FIRE PBT) for Population Based Training (PBT), an automated hyperparameter ...
Recently, several works have showed that the performance of Deep Learning methods can be improved by training a population of neural networks at the same time ...
Nov 27, 2017 · This technique - known as Population Based Training (PBT) - trains and optimises a series of networks at the same time, allowing the optimal set-up to be ...
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This paper introduces two new innovations in PBT-style methods that employ trust-region based Bayesian Optimization, enabling full coverage of the ...