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michael-lewis.com | ||
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www.paepper.com
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| | | | | Today's paper: Rethinking 'Batch' in BatchNorm by Wu & Johnson BatchNorm is a critical building block in modern convolutional neural networks. Its unique property of operating on "batches" instead of individual samples introduces significantly different behaviors from most other operations in deep learning. As a result, it leads to many hidden caveats that can negatively impact model's performance in subtle ways. This is a citation from the paper's abstract and the emphasis is mine which caught my attention. Let's explore these subtle ways which can negatively impact your model's performance! The paper of Wu & Johnson can be found on arxiv. | |
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questionableengineering.com
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| | | | | John W Grun AbstractIn this paper, a manually implemented LeNet-5 convolutional NN with an Adam optimizer written in Numpy will be presented. This paper will also cover a description of the data use | |
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www.khanna.law
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| | | | | You want to train a deep neural network. You have the data. It's labeled and wrangled into a useful format. What do you do now? | |
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www.wjst.de
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| | | But let your communication be Yea, yea; Nay, nay. For whatsoever is more than these cometh of evil. | ||
