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1,321 claims from 825 papers are on the record. 46 have been checked so far; the other 1,275 have no check with a result yet.

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Keyword: deep neural network training ×Clear all

6 claims from 3 papers

1. Computer Science › Advanced Neural Network Applications

   Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

   Ioffe and Szegedy · arXiv (Cornell University) · 2015

   The paper introduces Batch Normalization, which normalizes layer inputs within each training mini-batch to speed up deep network training and improve image classification accuracy on ImageNet.

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   1. UncheckedThe authors say Batch Normalization lets neural networks be trained with much higher learning rates and with less care over how the starting parameters are set.“Batch Normalization allows us to use much higher learning rates and be less careful about initialization.”

2. Computer Science › Advanced Neural Network Applications

   Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification

   He, Zhang, Ren and Sun · arXiv (Cornell University) · 2015

   The paper proposes PReLU, a generalised rectifier, and a initialisation method for rectifier networks, reaching 4.94% top-5 error on ImageNet 2012, which it says is the first result to surpass human-level performance.

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   1. UncheckedThe authors say PReLU, a adjustable rectifier unit, improves how well a network fits its data at almost no extra computing cost and with little risk of overfitting.“PReLU improves model fitting with nearly zero extra computational cost and little overfitting risk.”
   2. UncheckedA new initialisation method designed for rectifier units lets very deep rectified networks be trained from scratch, and allows deeper or wider designs to be explored.“This method enables us to train extremely deep rectified models directly from scratch and to investigate deeper or wider network architectures.”
   3. UncheckedUsing PReLU networks, the authors report a 4.94% top-5 test error on the ImageNet 2012 image classification dataset.“Based on our PReLU networks (PReLU-nets), we achieve 4.94% top-5 test error on the ImageNet 2012 classification dataset.”

3. Computer Science › Advanced Neural Network Applications

   Sparse Networks from Scratch: Faster Training without Losing Performance

   Dettmers and Zettlemoyer · arXiv (Cornell University) · 2019

   The paper presents sparse momentum, a method that trains neural networks with mostly zero weights throughout training, reaching dense-network performance while training up to 5.61x faster.

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   1. Unchecked“We demonstrate state-of-the-art sparse performance on MNIST, CIFAR-10, and ImageNet, decreasing the mean error by a relative 8%, 15%, and 6% compared to other sparse algorithms.”
   2. UncheckedAblation tests in the paper suggest that momentum-based redistribution and growth of weights help more as a network gets deeper and larger.“In our analysis, ablations show that the benefits of momentum redistribution and growth increase with the depth and size of the network.”