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《SWALP:Stochastic Weight Averaging in Low-Precision Training》
時間 2020-12-20
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摘要:這種方式可以使得所有數字均量化至8-bit,同時表示爲SWALP任意收斂於二次目標的最優解,在強凸條件下使噪聲球漸近小於低精度SGD。 SWALP的處理將梯度累加模塊也變爲8-bit (Stochastic Weight Averaging)SWA的介紹: https://blog.csdn.net/leviopku/article/details/84037946 多次求平均值,SWA
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相關文章
1.
Stochastic Weight Averaging in PyTorch
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From Hashing to CNNs: Training Binary Weight Networks via Hashing
3.
Quaternion Weighted Averaging Proofs
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