AN EFFICIENT HOMOTOPY TRAINING ALGORITHM FOR NEURAL NETWORKS
We present a Homotopy Training Algorithm (HTA) to solve optimization problems arising from neural networks. The HTA starts with several decoupled systems with low dimensional structure and tracks the solution to the high dimensional coupled system. The decoupled systems are easy to solve due to the low dimensionality but can be connected to the original system via a continuous homotopy path guided by the HTA. We have proved the convergence of HTA for the non-convex case and existence of the homotopy solution path for the convex case. The HTA has provided a better accuracy on several examples including VGG models on CIFAR-10. Moreover, the HTA would be combined with the dropout technique to provide an alternative way to train the neural networks.
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