# A priori guarantees of finite-time convergence for Deep Neural Networks

@article{Rankawat2020APG, title={A priori guarantees of finite-time convergence for Deep Neural Networks}, author={Anushree Rankawat and Mansi Rankawat and Harshal B. Oza}, journal={ArXiv}, year={2020}, volume={abs/2009.07509} }

In this paper, we perform Lyapunov based analysis of the loss function to derive an a priori upper bound on the settling time of deep neural networks. While previous studies have attempted to understand deep learning using control theory framework, there is limited work on a priori finite time convergence analysis. Drawing from the advances in analysis of finite-time control of non-linear systems, we provide a priori guarantees of finite-time convergence in a deterministic control theoretic…

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