Summary of Reducing Hyperparameter Tuning Costs in Ml, Vision and Language Model Training Pipelines Via Memoization-awareness, by Abdelmajid Essofi et al.
Reducing Hyperparameter Tuning Costs in ML, Vision and Language Model Training Pipelines via Memoization-Awareness
by Abdelmajid Essofi, Ridwan Salahuddeen, Munachiso Nwadike, Elnura Zhalieva, Kun Zhang, Eric Xing, Willie Neiswanger, Qirong Ho
First submitted to arxiv on: 6 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed “memoization-aware” Bayesian Optimization algorithm, EEIPU, significantly reduces the cost of hyperparameter tuning for machine learning, vision, and language models. By exploiting pipeline structures, EEIPU evaluates more hyperparameter candidates per GPU-day than other algorithms, achieving better-quality hyperparameters or reduced search time to reach the same quality. The results are demonstrated on various pipelines, including model ensembles, convolutional architectures, and T5 architecture. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses a new way of training models that makes it faster and more efficient. It’s like having a superpower for searching through lots of combinations to find the best one. The researchers developed an algorithm called EEIPU that helps find the right settings for machine learning models, making it possible to get better results or complete the search faster. |
Keywords
* Artificial intelligence * Hyperparameter * Machine learning * Optimization * T5
