Jul 3, 2021 · The distribution of the slope of a well-trained neural network classifier is generally independent of the width of the layers in a fully connected network.
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Sep 7, 2024 · It also behaves as predicted in rescaling examples. We discuss possible applications of the slope concept, such as using it as a part of the ...
Calling such properties geometric properties, we focus in this paper on what we call the slope, essentially the largest speed with which f moves its input at a ...
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This repo contains the code to reproduce the results in the paper "Slope and generalization properties of neural networks".
This paper develops theoretical results for the estimation of radial basis function neural network specifications, for dependent data, that do not require ...
Mar 27, 2020 · Here we study the main factors governing the applicability of NQS to frustrated magnets by training neural networks to approximate ground states.
Missing: Slope | Show results with:Slope
Sep 1, 2024 · In this paper, we study the generalization gap of neural networks using methods from topological data analysis.
Oct 25, 2024 · We study generalization of neural network predictions in settings where statistical properties of test data and training data are different.
Missing: Slope | Show results with:Slope
Explore all code implementations available for Slope and generalization properties of neural networks.
Nov 18, 2024 · Significant progress has been made recently in understanding the generalization of neural networks (NNs) trained by gradient descent (GD) ...