Sho Sonoda | DeepAI

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∙ 02/12/2023

Koopman-Based Bound for Generalization: New Aspect of Neural Networks Regarding Nonlinear Noise Filtering

We propose a new bound for generalization of neural networks using Koopm...

0 Yuka Hashimoto, et al. ∙

∙ 01/27/2023

Quantum Ridgelet Transform: Winning Lottery Ticket of Neural Networks with Quantum Computation

Ridgelet transform has been a fundamental mathematical tool in the theor...

0 Hayata Yamasaki, et al. ∙

∙ 05/30/2022

Universality of group convolutional neural networks based on ridgelet analysis on groups

We investigate the approximation property of group convolutional neural ...

0 Sho Sonoda, et al. ∙

∙ 03/03/2022

Fully-Connected Network on Noncompact Symmetric Space and Ridgelet Transform based on Helgason-Fourier Analysis

Neural network on Riemannian symmetric space such as hyperbolic space an...

0 Sho Sonoda, et al. ∙

∙ 02/10/2022

Deep Learning in Random Neural Fields: Numerical Experiments via Neural Tangent Kernel

A biological neural network in the cortex forms a neural field. Neurons ...

20 Kaito Watanabe, et al. ∙

∙ 06/16/2021

Exponential Error Convergence in Data Classification with Optimized Random Features: Acceleration by Quantum Machine Learning

Random features are a central technique for scalable learning algorithms...

0 Hayata Yamasaki, et al. ∙

∙ 06/09/2021

Ghosts in Neural Networks: Existence, Structure and Role of Infinite-Dimensional Null Space

Overparametrization has been remarkably successful for deep learning stu...

0 Sho Sonoda, et al. ∙

∙ 06/07/2021

Differentiable Multiple Shooting Layers

We detail a novel class of implicit neural models. Leveraging time-paral...

0 Stefano Massaroli, et al. ∙

∙ 08/19/2020

On the Approximation Lower Bound for Neural Nets with Random Weights

A random net is a shallow neural network where the hidden layer is froze...

17 Sho Sonoda, et al. ∙

∙ 07/07/2020

Gradient Descent Converges to Ridgelet Spectrum

Deep learning achieves a high generalization performance in practice, de...

0 Sho Sonoda, et al. ∙

∙ 04/22/2020

Fast Quantum Algorithm for Learning with Optimized Random Features

Kernel methods augmented with random features give scalable algorithms f...

0 Hayata Yamasaki, et al. ∙

∙ 02/02/2019

Numerical Integration Method for Training Neural Network

We propose a new numerical integration method for training a shallow neu...

0 Sho Sonoda, et al. ∙

∙ 05/19/2018

Integral representation of the global minimizer

We have obtained an integral representation of the shallow neural networ...

0 Sho Sonoda, et al. ∙

∙ 12/12/2017

Transportation analysis of denoising autoencoders: a novel method for analyzing deep neural networks

The feature map obtained from the denoising autoencoder (DAE) is investi...

0 Sho Sonoda, et al. ∙

∙ 05/10/2016

Decoding Stacked Denoising Autoencoders

Data representation in a stacked denoising autoencoder is investigated. ...

0 Sho Sonoda, et al. ∙

∙ 05/14/2015

Neural Network with Unbounded Activation Functions is Universal Approximator

This paper presents an investigation of the approximation property of ne...

0 Sho Sonoda, et al. ∙

∙ 12/23/2013

Nonparametric Weight Initialization of Neural Networks via Integral Representation

A new initialization method for hidden parameters in a neural network is...

0 Sho Sonoda, et al. ∙

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