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New research systematically evaluates learning rate schedulers for diverse neural network architectures

By PulseAugur Editorial · [2 sources] ·

A new research paper published on arXiv details a systematic evaluation of learning rate scheduling strategies for neural networks. The study applied 25 scheduler configurations across 3,938 model variants, utilizing 30 different architectures from convolutional and transformer families. The findings indicate that the optimal scheduler is highly dependent on the specific architecture, with CosineAnnealingWarmRestarts and CyclicLR outperforming simpler decay methods. The research contributes a comprehensive accuracy landscape to the LEMUR neural network dataset, offering a practical guide for selecting appropriate schedulers. AI

IMPACT Provides a practical reference for selecting optimal learning rate schedulers, potentially improving training efficiency and accuracy across diverse neural network architectures.

RANK_REASON The cluster contains a research paper detailing a systematic evaluation of learning rate scheduling strategies for neural networks.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New research systematically evaluates learning rate schedulers for diverse neural network architectures

COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Hafsa Mateen, Radu Timofte, Dmitry Ignatov ·

    Systematic Evaluation of Learning Rate Scheduling Strategies Across Heterogeneous Architectures

    arXiv:2607.08511v1 Announce Type: new Abstract: Choosing a learning rate scheduling strategy is critical to neural network training, but manual selection is costly and rarely exhaustive. While classical AutoML approaches often treat the scheduler as a secondary hyperparameter, we…

  2. arXiv cs.LG TIER_1 English(EN) · Dmitry Ignatov ·

    Systematic Evaluation of Learning Rate Scheduling Strategies Across Heterogeneous Architectures

    Choosing a learning rate scheduling strategy is critical to neural network training, but manual selection is costly and rarely exhaustive. While classical AutoML approaches often treat the scheduler as a secondary hyperparameter, we systematically investigate its impact on classi…

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