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1 papers

2015International Conference on Learning Representations (ICLR 2015) — arXiv:1412.6980, https://arxiv.org/abs/1412.6980

Adam: A Method for Stochastic Optimization

Jimmy Ba, Diederik P. Kingma

Finding: Adam combines the advantages of AdaGrad (handles sparse gradients) and RMSProp (works well in non-stationary settings), and performs well empirically across problems.

optimization gradient-descent