20 citations · 59 across the 20 of their papers we have counts for
11 papers · 1 filter
Universal Approximation Property of Neural Ordinary Differential Equations
Takeshi Teshima, Koichi Tojo, Masahiro Ikeda +2
Neural ordinary differential equations (NODEs) is an invertible neural network architecture promising for its free-form Jacobian and the availability of a tractable Jacobian determ…
A global universality of two-layer neural networks with ReLU activations
Naoya Hatano, Masahiro Ikeda, Isao Ishikawa +1
In the present study, we investigate a universality of neural networks, which concerns a density of the set of two-layer neural networks in a function spaces. There are many works…
Small data blow-up for the weakly coupled system of the generalized Tricomi equations with multiple propagation speeds
Masahiro Ikeda, Jiayun Lin, Ziheng Tu
In the present paper, we study the Cauchy problem for the weakly coupled system of the generalized Tricomi equations with multiple propagation speeds. Our aim of this paper is to p…
Boundedness of composition operators on Morrey spaces and weak Morrey spaces
Naoya Hatano, Masahiro Ikeda, Isao Ishikawa +1
In this study, we investigate the boundedness of composition operators acting on Morrey spaces and weak Morrey spaces. The primary aim of this study is to investigate a necessary a…
Kernel Mean Embeddings of Von Neumann-Algebra-Valued Measures
Yuka Hashimoto, Isao Ishikawa, Masahiro Ikeda +2
Kernel mean embedding (KME) is a powerful tool to analyze probability measures for data, where the measures are conventionally embedded into a reproducing kernel Hilbert space (RKH…
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…