20 citations · 30 across the 7 of their papers we have counts for
14 papers
Universal approximation property of invertible neural networks
Isao Ishikawa, Takeshi Teshima, Koichi Tojo +3
Invertible neural networks (INNs) are neural network architectures with invertibility by design. Thanks to their invertibility and the tractability of Jacobian, INNs have various m…
Generalized eigenvalues of the Perron-Frobenius operators of symbolic dynamical systems
Hayato Chiba, Masahiro Ikeda, Isao Ishikawa
The generalized spectral theory is an effective approach to analyze a linear operator on a Hilbert space with a continuous spectrum. The generalized spectrum is compu…
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…
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…