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Tsinghua U Proposes Stochastic Scheduled Sharpness-Aware Minimization for Efficient DNN Training
March 23, 2022, 3:47 p.m. | Synced
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A Tsinghua University research team proposes Stochastic Scheduled SAM (SS-SAM), a novel and efficient DNN training scheme that achieves comparable or better model training performance with much lower computation cost compared to baseline sharpness-aware minimization (SAM) training schema.
The post Tsinghua U Proposes Stochastic Scheduled Sharpness-Aware Minimization for Efficient DNN Training first appeared on Synced.
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