Parameter Continuation Methods for the Optimization of Deep Neural Networks

There are many extant methods for approximating the solutions of non-convex optimization problems arising in deep neural networks, including stochastic gradient descent, RMSProp, AdaGrad, and ADAM. In this paper, we propose a novel training strategy for deep neural networks based on ideas from numerical parameter continuation methods... (read more)

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