%20optimizer.%20We%20model%20gradient%20updates%20as%20a%20probabilistic%20model%20and%20utilize%20stochastic%20variational%20inference%20(SVI)%20to%20derive%20an%20efficient%20and%20effective%20update%20rule.%20Further%2C%20we%20show%20how%20our%20VSGD%20method%20relates%20to%20other%20adaptive%20gradient-based%20optimizers%20like%20Adam.%20Lastly%2C%20we%20carry%20out%20experiments%20on%20two%20image%20classification%20datasets%20and%20four%20deep%20neural%20network%20architectures%2C%20where%20we%20show%20that%20VSGD%20outperforms%20Adam%20and%20SGD.)
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