<story><title>Batch Normalization: Accelerating Deep Network Training [pdf]</title><url>http://arxiv.org/pdf/1502.03167v1.pdf</url><text></text></story><parent_chain></parent_chain><comment><author>lars</author><text>Since this paper along with [1] both beat human level performance on ImageNet, using seemingly unrelated techniques, it seems like there&amp;#x27;s a clear path to achieve the next state of the art result: Implement and train a network that uses both Batch Normalization and PReLU&amp;#x27;s at the same time. Right?&lt;p&gt;1: &lt;a href=&quot;http://arxiv.org/pdf/1502.01852v1.pdf&quot; rel=&quot;nofollow&quot;&gt;http:&amp;#x2F;&amp;#x2F;arxiv.org&amp;#x2F;pdf&amp;#x2F;1502.01852v1.pdf&lt;/a&gt;</text></comment>
<story><title>Batch Normalization: Accelerating Deep Network Training [pdf]</title><url>http://arxiv.org/pdf/1502.03167v1.pdf</url><text></text></story><parent_chain></parent_chain><comment><author>hyperbovine</author><text>The phrase &amp;quot;exceeding the accuracy of human raters&amp;quot; is a bit puzzling--if a human misclassifies something whose true class was determined by another human, who is in error?</text></comment>