Jun 16, 2016 · We propose a new approach to the problem of neural network expressivity, which seeks to characterize how structural properties of a neural network family ...
Our findings show that: (1) The complexity of the computed function grows exponentially with depth (2) All weights are not equal: trained networks are more ...
We propose a new approach to the problem of neural network expressivity, which seeks to char- acterize how structural properties of a neural net-.
Apr 27, 2017 · Derives and explains the exponential depth sensitivity of different expressivity measures for deep neural networks, and explores consequences during and ...
Our findings show that: (1) The complexity of the computed function grows exponentially with depth (2) All weights are not equal: trained net- works are more ...
May 29, 2019 · This paper proposes the dimension of this variety as a precise measure of the expressive power of polynomial neural networks.
We propose a new approach to the problem of neural network expressivity, which seeks to characterize how structural properties of a neural network family ...
We propose a new approach to the problem of neural network expressivity, which seeks to characterize how structural properties of a neural network family ...
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Recent results from linear algebra stating that any matrix can be decomposed into products of diagonal and circulant matrices has lead to the design of ...
We study deep neural networks with polynomial activations, particularly their expressive power. For a fixed architecture and activation degree, a polynomial.