悲痛 发表于 2025-3-28 15:38:01

Geometry of Deep Neural Networks neural network learn? How does a deep neural network, especially a CNN, accomplish these goals? The full answer to these basic questions is still a long way off. Here are some of the insights we’ve obtained while traveling towards that destination. In particular, we explain why the classic approach

CLEAR 发表于 2025-3-28 20:00:28

Deep Learning Optimizationally gradient-based local update schemes. However, the biggest obstacle recognized by the entire community is that the loss surfaces of deep neural networks are extremely non-convex and not even smooth. This non-convexity and non-smoothness make the optimization unaffordable to analyze, and the main

异常 发表于 2025-3-28 22:54:33

http://reply.papertrans.cn/39/3839/383804/383804_43.png

生命 发表于 2025-3-29 03:53:22

Summary and Outlookrevolution”. Despite the great successes of deep learning in various areas, there is a tremendous lack of rigorous mathematical foundations which enable us to understand why deep learning methods perform well.

ARENA 发表于 2025-3-29 08:27:54

http://reply.papertrans.cn/39/3839/383804/383804_45.png

规范要多 发表于 2025-3-29 13:42:43

http://reply.papertrans.cn/39/3839/383804/383804_46.png

Melatonin 发表于 2025-3-29 17:59:58

tworks are extremely non-convex and not even smooth. This non-convexity and non-smoothness make the optimization unaffordable to analyze, and the main concern was whether popular gradient-based approaches might fall into local minimizers.

CLEAR 发表于 2025-3-29 23:09:37

http://reply.papertrans.cn/39/3839/383804/383804_48.png

malign 发表于 2025-3-30 00:04:54

http://reply.papertrans.cn/39/3839/383804/383804_49.png

conscribe 发表于 2025-3-30 05:14:25

http://reply.papertrans.cn/39/3839/383804/383804_50.png
页: 1 2 3 4 [5] 6
查看完整版本: Titlebook: Geometry of Deep Learning; A Signal Processing Jong Chul Ye Textbook 2022 The Editor(s) (if applicable) and The Author(s), under exclusive