5 citations · 6 across the 2 of their papers we have counts for
5 papers
Differentiable Multiple Shooting Layers
Stefano Massaroli, Michael Poli, Sho Sonoda +4
We detail a novel class of implicit neural models. Leveraging time-parallel methods for differential equations, Multiple Shooting Layers (MSLs) seek solutions of initial value prob…
Ridge Regression with Over-Parametrized Two-Layer Networks Converge to Ridgelet Spectrum
Sho Sonoda, Isao Ishikawa, Masahiro Ikeda
Characterization of local minima draws much attention in theoretical studies of deep learning. In this study, we investigate the distribution of parameters in an over-parametrized…
Fast Approximation and Estimation Bounds of Kernel Quadrature for Infinitely Wide Models
Sho Sonoda
An infinitely wide model is a weighted integration of feature maps. This model excels at handling an infinite number of features, and thus it has been adopted…
The global optimum of shallow neural network is attained by ridgelet transform
Sho Sonoda, Isao Ishikawa, Masahiro Ikeda +4
We prove that the global minimum of the backpropagation (BP) training problem of neural networks with an arbitrary nonlinear activation is given by the ridgelet transform. A series…
Transportation analysis of denoising autoencoders: a novel method for analyzing deep neural networks
Sho Sonoda, Noboru Murata
The feature map obtained from the denoising autoencoder (DAE) is investigated by determining transportation dynamics of the DAE, which is a cornerstone for deep learning. Despite t…