We present DANTE, a novel method for training neural networks using the alternating minimization principle. DANTE provides an alternate perspective to traditional gradient-based backpropagation techniques commonly used to train deep networks. It utilizes an adaptation of quasi-convexity to cast training a neural network as a bi-quasi-convex optimization problem. We show that for neural network configurations with both differentiable (e.g. sigmoid) and non-differentiable (e.g. ReLU) activation functions, we can perform the alternations effectively in this formulation. DANTE can also be extended to networks with multiple hidden layers. In experiments on standard datasets, neural networks trained using the proposed method were found to be promising and competitive to traditional backpropagation techniques, both in terms of quality of the solution, as well as training speed.

我们提出了DANTE，这是一种使用交替最小化原理训练神经网络的新颖方法。DANTE为传统的基于梯度的反向传播技术（通常用于训练深层网络）提供了另一种视角。它利用准凸度的适应性将神经网络训练为双准凸优化问题。我们表明，对于具有可区分（例如S型）和不可区分（例如ReLU）激活函数的神经网络配置，我们可以在此公式中有效地执行替换。DANTE也可以扩展到具有多个隐藏层的网络。在标准数据集上进行的实验中，使用该方法训练的神经网络在解决方案的质量，