Theory II: Landscape of the Empirical Risk in Deep Learning
Previous theoretical work on deep learning and neural network optimization tend to focus on avoiding saddle points and local minima. However, the practical observation is that, at least for the most successful Deep Convolutional Neural Networks (DCNNs) for visual processing, practitioners can always...
| Main Authors: | , |
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| Format: | Technical Report |
| Language: | en_US |
| Published: |
Center for Brains, Minds and Machines (CBMM), arXiv
2017
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| Subjects: | |
| Online Access: | http://hdl.handle.net/1721.1/107787 |