【ML&DL】【skimming】Global Optimality in Neural Network Training
補了一下2017年的CVPR，Global Optimality in Neural Network Training[1] 論文一覽： 痛點 深度學習取得了很大的成功，但是對其成功原因的數學解釋卻還是一個難點。很大一個原因是對深度網絡的參數學習是一個非凸的過程。而優化算法有陷入非global minima的可能。凸優化問題的局部極小值也是全局極小值，因此學習問題的凸公式通常更可取，因爲它們有助於
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相關文章
- 1. Training Recurrent Neural Network
- 2. Lecture 6 Training Neural Network (2)
- 3. Network in Network----Global Average Pooling
- 4. CS231N-Lecture6 Training Neural Network part-2
- 5. Lecture 6 Training Neural Network (1)
- 6. Tips for Training Deep Neural Network
- 7. Principles of training multi-layer neural network using backpropagation
- 8. 1804.03235-Large scale distributed neural network training through online distillation.md
- 9. 論文筆記《Barren plateaus in quantum neural network training landscapes》
- 10. Distilling the Knowledge in a Neural Network