【ML&DL】【skimming】On the saddle point problem for non-convex optimization
補一下經典，2014年Yoshua Bengio的On the saddle point problem for non-convex optimization。 論文一覽: 痛點 深度學習一個主要問題就是要在一個連續高維的空間中去最小化非凸的損失函數。 人們使用的方法經常是梯度下降或者擬牛頓法，這些方法找到全局最小值的主要困難源自局部極小值的擴散，其誤差遠高於全局最小值。但是本文認爲，真正需要擔
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相關文章
- 1. How to Escape Saddle Points Efficiently
- 2. Trajectory Optimization for The Block-move Example Based on the Direct Single Shooting Method
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- 4. optimization on macOS
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- 6. On the Optimization of Deep Networks: Implicit Acceleration by Overparameterization
- 7. The problem of Titanium
- 8. Leetcode: The Skyline Problem
- 9. Compiler Optimization on VLIW Instruction Scheduling for Low Power
- 10. Gentle Introduction to the Adam Optimization Algorithm for Deep Learning