Convergence Rates of Training Deep Neural Networks via Alternating Minimization Methods

Training deep neural networks (DNNs) is an important and challenging optimization problem in machine learning due to its non-convexity and non-separable structure. The alternating minimization (AM) approaches split the composition structure of DNNs and have drawn great interest in the deep learning...

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Hauptverfasser:	Xu, Jintao, Bao, Chenglong, Xing, Wenxun
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science - Learning Mathematics - Optimization and Control
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