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• W2945697643 abstract "We propose NovoGrad, an adaptive stochastic gradient descent method with layer-wise gradient normalization and decoupled weight decay. In our experiments on neural networks for image classification, speech recognition, machine translation, and language modeling, it performs on par or better than well tuned SGD with momentum and Adam or AdamW. Additionally, NovoGrad (1) is robust to the choice of learning rate and weight initialization, (2) works well in a large batch setting, and (3) has two times smaller memory footprint than Adam." @default.
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• W2945697643 date "2019-05-27" @default.
• W2945697643 modified "2023-09-27" @default.
• W2945697643 title "Stochastic Gradient Methods with Layer-wise Adaptive Moments for Training of Deep Networks" @default.
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• W2945697643 hasPublicationYear "2019" @default.
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