【ML&DL】【skimming】The Loss Surfaces of Multilayer Networks
補了一下Yann LeCun的經典工作The Loss Surfaces of Multilayer Networks[1] 論文一覽： 痛點 文章假設並且陸續證明了這樣一些事情： 1）對於大網絡(large size network)而言，絕大多數局部極小值在test上的表現是差不多的，且這些local minima跟global minima的表現也是差不多的。 2）小網絡找到差的局部極小值(
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相關文章
- 1. 【ML&DL】【skimming】On the saddle point problem for non-convex optimization
- 2. Multilayer Neural Networks and the Backpropagation Learning Algorithm
- 3. The Multilinear Structure of ReLU Networks
- 4. Multilayer in-place learning networks for modeling functional layers in the laminar cortex筆記
- 5. The Unreasonable Effectiveness of Recurrent Neural Networks
- 6. Exploring the teaching of deep learning in neural networks
- 7. On the Number of Linear Regions of Deep Neural Networks
- 8. In Defense of the Triplet Loss for Person Re-Identification
- 9. Deep Learning 101 - Part 2: Multilayer Perceptrons
- 10. The Lovasz-Softmax loss: A tractable surrogate for the optimization of the ´ intersection-over-union