Scrap Your Schedules with PopDescent

• URL: http://arxiv.org/abs/2310.14671v2
• Date: Wed, 24 Apr 2024 18:18:30 GMT
• Title: Scrap Your Schedules with PopDescent
• Authors: Abhinav Pomalapally, Bassel El Mabsout, Renato Mansuco,
• Abstract summary: Population Descent (PopDescent) is a memetic, population-based search technique. We show that PopDescent converges faster than existing search methods, finding model parameters with test-loss values up to 18% lower. Our trials on standard machine learning vision tasks show that PopDescent converges faster than existing search methods, finding model parameters with test-loss values up to 18% lower.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In contemporary machine learning workloads, numerous hyper-parameter search algorithms are frequently utilized to efficiently discover high-performing hyper-parameter values, such as learning and regularization rates. As a result, a range of parameter schedules have been designed to leverage the capability of adjusting hyper-parameters during training to enhance loss performance. These schedules, however, introduce new hyper-parameters to be searched and do not account for the current loss values of the models being trained. To address these issues, we propose Population Descent (PopDescent), a progress-aware hyper-parameter tuning technique that employs a memetic, population-based search. By merging evolutionary and local search processes, PopDescent proactively explores hyper-parameter options during training based on their performance. Our trials on standard machine learning vision tasks show that PopDescent converges faster than existing search methods, finding model parameters with test-loss values up to 18% lower, even when considering the use of schedules. Moreover, we highlight the robustness of PopDescent to its initial training parameters, a crucial characteristic for hyper-parameter search techniques.

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