May 19, 2018 · In our study, we observe that there are benefits of weighting more of the past gradients when designing the adaptive learning rate. We therefore ...
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In our study, we observe that there are benefits of weighting more of the past gradients when designing the adaptive learning rate. We therefore propose an.
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... Nostalgic Adam (NosAdam), which places bigger weights on the past gradients than the recent gradients when designing the adaptive learning rate. This is a ...
In this study, we investigate novel adaptive learning rate strategies at different levels based on the hyper-gradient descent framework and propose a method ...
A new algorithm is proposed, called Nostalgic Adam (NosAdam), which places bigger weights on the past gradients than the recent gradients when designing the ...
Google Scholar. Nostalgic Adam: Weighing more of the past gradients when designing the adaptive learning rate. Published in IJCAI 2019, 2019. Download paper ...
NosAdam can be regarded as a fix to the non-convergence issue of Adam in alternative to the recent work ofReddi et al., 2018 and preliminary numerical ...
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Nostalgic Adam. Code and supplements for "Nostalgic Adam: Weighting more of the past gradients when designing the adaptive learning rate". Haiwen Huang, Chang ...
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May 19, 2018 · In this paper, we propose a new algorithm, called Nostalgic Adam (NosAdam), which places bigger weights on the past gradients than the recent ...
Nostalgic Adam: Weighting more of the past gradients when designing the adaptive learning rate. H Huang, C Wang, B Dong. Proceedings of the Twenty-Eighth ...
Missing: Weighing | Show results with:Weighing