Dec 12, 2015 · Formally demonstrates that depth -- even if increased by 1 -- can be exponentially more valuable than width for standard feedforward neural networks.
These results demonstrate the value of depth for arbitrarily deep, standard ReLU networks, for a single dimension and using functions which have an ...
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May 9, 2016 · Abstract. We show that there is a simple (approximately radial) function on Rd, expressible by a small 3-layer feedforward neural networks ...
693 Citations · The power of deeper networks for expressing natural functions · Expressivity of Shallow and Deep Neural Networks for Polynomial Approximation.
Duration: 12:37
Posted: Jul 19, 2016
Posted: Jul 19, 2016
Oct 14, 2018 · The deeper the network gets, the more functions we are applying and the more we mould and transform the input to something else; perhaps in ...
Jul 8, 2019 · Increasing the depth of a neural network will let you approximate functions with increased non-linearity. · At the same time, this comes with a ...
With the increase in network depth, learning more abstract cascade correlation, where once the error stops changing, candidate units can be added to the same ...
Feb 14, 2022 · This post is the first of a three-part series in which we set out to derive the mathematics behind feedforward neural networks.
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May 3, 2024 · The Power of Depth for Feedforward Neural Networks.