The Expressive Power of Neural Networks: A View from the Width

時間 2021-01-11

標籤 neural networks 简体版

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文章目錄概主要內容定理1 定理2 定理3 定理4 定理1的證明 Lu Z, Pu H, Wang F, et al. The expressive power of neural networks: a view from the width[C]. neural information processing systems, 2017: 6232-6240. @article{lu2017

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1. The Unreasonable Effectiveness of Recurrent Neural Networks
2. On the Number of Linear Regions of Deep Neural Networks
3. Exploring the teaching of deep learning in neural networks
4. the english of the simple view
5. Xavier——Understanding the difficulty of training deep feedforward neural networks
6. [paper]Towards Evaluating the Robustness of Neural Networks(C&W)
7. Why LSTMs Stop Your Gradients From Vanishing: A View from the Backwards Pass
8. On the difficulty of training Recurrent Neural Networks
9. The Rise of Meta Learning
10. Applications of Graph Neural Networks

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