New method adapts optimizer selection during deep learning training

Researchers have developed a new method called Repeated Optimizer Resampling (ROR) to improve the selection of optimizers for deep neural network training. Instead of choosing a single optimizer upfront, ROR allows the system to adaptively switch optimizers during the training process. This approach involves periodic scouting by candidate optimizers, with the best-performing scout continuing the main training run. Evaluations on various datasets, including MNIST and Fashion-MNIST, showed that ROR variants achieved performance close to the best fixed optimizers while requiring significantly less aggregate training time. AI

IMPACT This adaptive optimizer selection method could reduce training time and improve model performance in deep learning applications.

RANK_REASON The cluster contains an academic paper detailing a new method for machine learning optimization. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• arXiv
• DagsHub
• Fashion-MNIST
• Hugging Face
• IArxiv
• MNIST database
• Ruby on Rails

• paper
• infra

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →