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Convergence analysis and application for high-order neural networks based on gradient descent learning algorithm via smooth regularization. [PDF]

Sci Rep
Mohamed KS, Adam A, Mohammed YS, Xiong Y.   +3 more
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Federated two-edge graph attention network with weighted global aggregation for electricity consumption demand forecasting. [PDF]

Sci Rep
Yang M, Ren J, Zeng L, Yang X, Li S, Ai Y, Zheng K.   +6 more
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Calibrating deep classifiers with dynamic confidence propagation and adaptive normalization. [PDF]

Sci Rep
He P, Fu W, Wang L, Fu T.
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Expanding the Capabilities of Portable Mapping in Macroscopic External Reflection FT-IR through a Targeted Data-Driven Spectral Enhancement and Denoising Strategy. [PDF]

ACS Meas Sci Au
Li Z, Catelli E, Stergar J, Milanič M, Sáez-Hernández R, Prati S, Oliveri P, Sciutto G.   +7 more
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Smoothing Parameter and Model Selection for General Smooth Models Comment

, 2018
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Smoothing parameter selection for smoothing splines: a simulation study

Computational Statistics & Data Analysis, 2003
Smoothing splines are a popular method for performing nonparametric regression. Most important in the implementation of this method is the choice of the smoothing parameter. This article provides a simulation study of several smoothing parameter selection methods, including two so-called risk estimation methods.
Thomas C M Lee
openaire   +3 more sources