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 optimizer. We model gradient updates as a probabilistic model and utilize stochastic variational inference (SVI) to derive an efficient and effective update rule. Further%2C we show how our VSGD method relates to other adaptive gradient-based optimizers like Adam. Lastly%2C we carry out experiments on two image classification datasets and four deep neural network architectures%2C where we show that VSGD outperforms Adam and SGD.?quality=80&w=800)
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