Convergent Block Coordinate Descent for Training Tikhonov Regularized Deep Neural Networks

NeurIPS 2017 Ziming ZhangMatthew Brand

By lifting the ReLU function into a higher dimensional space, we develop a smooth multi-convex formulation for training feed-forward deep neural networks (DNNs). This allows us to develop a block coordinate descent (BCD) training algorithm consisting of a sequence of numerically well-behaved convex optimizations... (read more)

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