Follow the Moving Leader in Deep Learning

Deep networks are highly nonlinear and difficult to optimize. During training, the parameter iterate may move from one local basin to another, or the data distribution may even change... (read more)

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Methods used in the Paper


METHOD TYPE
Affine Coupling
Bijective Transformation
Normalizing Flows
Distribution Approximation
Adam
Stochastic Optimization
RMSProp
Stochastic Optimization