2 citations · 3 across the 4 of their papers we have counts for
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stat.ML2019
Analytic expressions for the output evolution of a deep neural network
Anastasia Borovykh
We present a novel methodology based on a Taylor expansion of the network output for obtaining analytical expressions for the expected value of the network weights and output under…
stat.ML2019
Generalisation in fully-connected neural networks for time series forecasting
Anastasia Borovykh, Cornelis W. Oosterlee, Sander M. Bohte
In this paper we study the generalization capabilities of fully-connected neural networks trained in the context of time series forecasting. Time series do not satisfy the typical…
stat.ML2018
A Gaussian Process perspective on Convolutional Neural Networks
Anastasia Borovykh
In this paper we cast the well-known convolutional neural network in a Gaussian process perspective. In this way we hope to gain additional insights into the performance of convolu…