Towards understanding why Lookahead generalizes better than SGD and beyond
To train networks, lookahead algorithm [1] updates its fast weights k times via an inner-loop optimizer before updating its slow weights once by using the latest fast weights. Any optimizer, e.g. SGD, can serve as the inner-loop optimizer, and the derived lookahead generally enjoys remarkable test p...
Saved in:
Main Authors: | ZHOU, Pan, YAN, Hanshu, YUAN, Xiaotong, FENG, Jiashi, YAN, Shuicheng |
---|---|
格式: | text |
語言: | English |
出版: |
Institutional Knowledge at Singapore Management University
2021
|
主題: | |
在線閱讀: | https://ink.library.smu.edu.sg/sis_research/8987 https://ink.library.smu.edu.sg/context/sis_research/article/9990/viewcontent/2021_NeurIPS_lookahead.pdf |
標簽: |
添加標簽
沒有標簽, 成為第一個標記此記錄!
|
機構: | Singapore Management University |
語言: | English |
相似書籍
-
Towards theoretically understanding why SGD generalizes better than ADAM in deep learning
由: ZHOU, Pan, et al.
出版: (2020) -
Understanding generalization and optimization performance of deep CNNs
由: ZHOU, Pan, et al.
出版: (2018) -
Towards understanding convergence and generalization of AdamW
由: ZHOU, Pan, et al.
出版: (2024) -
Empirical risk landscape analysis for understanding deep neural networks
由: ZHOU, Pan, et al.
出版: (2018) -
Faster first-order methods for stochastic non-convex optimization on Riemannian manifolds
由: ZHOU, Pan, et al.
出版: (2019)