论文标题
将可区分的稀疏性嵌入深度神经网络中
Embedding Differentiable Sparsity into Deep Neural Network
论文作者
论文摘要
在本文中，我们提出将稀疏性嵌入到深神经网络的结构中，其中模型参数在随机梯度下降过程中的训练过程中可能完全为零。因此，它可以同时学习稀疏结构和网络的权重。提出的方法可以学习结构化以及非结构化的稀疏性。
In this paper, we propose embedding sparsity into the structure of deep neural networks, where model parameters can be exactly zero during training with the stochastic gradient descent. Thus, it can learn the sparsified structure and the weights of networks simultaneously. The proposed approach can learn structured as well as unstructured sparsity.
