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fix weight standardization link
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@ -94,7 +94,7 @@ This avoids outputs of nodes from always falling beyond the active range of the
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<p><em><a href="https://arxiv.org/abs/1903.10520">Refer to the paper for proofs</a></em>.</p>
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<p>Here is <a href="experiment.html">the training code</a> for training
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a VGG network that uses weight standardization to classify CIFAR-10 data.
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This uses a <a href="../conv2d.html">2D-Convolution Layer with Weight Standardization</a>.</p>
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This uses a <a href="conv2d.html">2D-Convolution Layer with Weight Standardization</a>.</p>
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<p><a href="https://colab.research.google.com/github/lab-ml/nn/blob/master/labml_nn/normalization/weight_standardization/experiment.ipynb"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg" /></a>
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<a href="https://app.labml.ai/run/f4a783a2a7df11eb921d0242ac1c0002"><img alt="View Run" src="https://img.shields.io/badge/labml-experiment-brightgreen" /></a>
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<a href="https://wandb.ai/vpj/cifar10/runs/3flr4k8w"><img alt="WandB" src="https://img.shields.io/badge/wandb-run-yellow" /></a></p>
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@ -40,7 +40,7 @@ This avoids outputs of nodes from always falling beyond the active range of the
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Here is [the training code](experiment.html) for training
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a VGG network that uses weight standardization to classify CIFAR-10 data.
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This uses a [2D-Convolution Layer with Weight Standardization](../conv2d.html).
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This uses a [2D-Convolution Layer with Weight Standardization](conv2d.html).
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[](https://colab.research.google.com/github/lab-ml/nn/blob/master/labml_nn/normalization/weight_standardization/experiment.ipynb)
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[](https://app.labml.ai/run/f4a783a2a7df11eb921d0242ac1c0002)
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