SGDR: Stochastic Gradient Descent with Warm Restarts.¶
Loshchilov, I., & Hutter, F. (2017). SGDR: Stochastic Gradient Descent with Warm Restarts. International Conference on Learning Representations (ICLR).
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Primes¶
- Annealing
- Machine learning: learning-rate schedules (cosine annealing, warm restarts, polynomial decay) and the noise schedules of diffusion-model training are explicit anneal-down protocols.
This sourceIntroduces cosine annealing of the learning rate with warm restarts, an explicit anneal-down schedule for deep-network training in which the schedule, not the peak rate, governs the quality of the minimum reached.
- Machine learning: learning-rate schedules (cosine annealing, warm restarts, polynomial decay) and the noise schedules of diffusion-model training are explicit anneal-down protocols.
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