Expressivity of Deep Neural Networks

• URL: http://arxiv.org/abs/2007.04759v1
• Date: Thu, 9 Jul 2020 13:08:01 GMT
• Title: Expressivity of Deep Neural Networks
• Authors: Ingo G\"uhring, Mones Raslan, Gitta Kutyniok
• Abstract summary: In this review paper, we give a comprehensive overview of the large variety of approximation results for neural networks. While the mainbody of existing results is for general feedforward architectures, we also depict approximation results for convolutional, residual and recurrent neural networks.
• Score: 2.7909470193274593
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this review paper, we give a comprehensive overview of the large variety of approximation results for neural networks. Approximation rates for classical function spaces as well as benefits of deep neural networks over shallow ones for specifically structured function classes are discussed. While the mainbody of existing results is for general feedforward architectures, we also depict approximation results for convolutional, residual and recurrent neural networks.

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