Training deep networks on large-scale datasets is computationally challenging. This work explores the problem of "how to accelerate adaptivegradientalgorithms in a general manner", and proposes an effectiv...
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Training deep networks on large-scale datasets is computationally challenging. This work explores the problem of "how to accelerate adaptivegradientalgorithms in a general manner", and proposes an effective Weight-decay-Integrated Nesterov acceleration (Win) to accelerate adaptivealgorithms. Taking AdamW and Adam as examples, per iteration, we construct a dynamical loss that combines the vanilla training loss and a dynamic regularizer inspired by proximal point method, and respectively minimize the first- and second-order Taylor approximations of dynamical loss to update variable. This yields our Win acceleration that uses a conservative step and an aggressive step to update, and linearly combines these two updates for acceleration. Next, we extend Win into Win2 which uses multiple aggressive update steps for faster convergence. Then we apply Win and Win2 to the popular LAMB and SGD optimizers. Our transparent derivation could provide insights for other accelerated methods and their integration into adaptivealgorithms. Besides, we theoretically justify the faster convergence of Win- and Win2-accelerated AdamW, Adam and LAMB to their non-accelerated counterparts. Experimental results demonstrates the faster convergence speed and superior performance of our Win- and Win2-accelerated AdamW, Adam, LAMB and SGD over their vanilla counterparts on vision classification and language modeling tasks.
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