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A Generalizable Approach to Learning Optimizers

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 نشر من قبل Diogo Almeida
 تاريخ النشر 2021
  مجال البحث الهندسة المعلوماتية
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A core issue with learning to optimize neural networks has been the lack of generalization to real world problems. To address this, we describe a system designed from a generalization-first perspective, learning to update optimizer hyperparameters instead of model parameters directly using novel features, actions, and a reward function. This system outperforms Adam at all neural network tasks including on modalities not seen during training. We achieve 2x speedups on ImageNet, and a 2.5x speedup on a language modeling task using over 5 orders of magnitude more compute than the training tasks.



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