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Nov, 2016
稀疏神经网络训练
Training Sparse Neural Networks
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Suraj Srinivas, Akshayvarun Subramanya, R. Venkatesh Babu
TL;DR
本研究介绍了一种使用稀疏计算的神经网络训练和构建方法,通过引入额外的门变量来执行参数选择,并在小型和大型网络上进行实验验证,证明了我们的方法在稀疏神经网络模型的压缩方面取得了最先进的结果。
Abstract
deep neural networks
with lots of parameters are typically used for large-scale
computer vision
tasks such as image classification. This is a result of using dense matrix multiplications and convolutions. However
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