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...
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| Main Authors: | , , , , |
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| Format: | text |
| Language: | English |
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Institutional Knowledge at Singapore Management University
2021
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| Online Access: | 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 |
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| Institution: | Singapore Management University |
| Language: | English |