4 citations · 11 across the 11 of their papers we have counts for
6 papers · 1 filter
Spectral Truncation Kernels: Noncommutativity in -algebraic Kernel Machines
Yuka Hashimoto, Ayoub Hafid, Masahiro Ikeda +1
A central question in vector- and function-valued learning is how to design kernels that capture both local and non-local interactions while remaining computationally tractable. Ex…
Deep Ridgelet Transform and Unified Universality Theorem for Deep and Shallow Joint-Group-Equivariant Machines
Sho Sonoda, Yuka Hashimoto, Isao Ishikawa +1
We present a constructive universal approximation theorem for learning machines equipped with joint-group-equivariant feature maps, called the joint-equivariant machines, based on…
Boundedness of composition operator in Orlicz-Morrey spaces
Masahiro Ikeda, Isao Ishikawa, Ryota Kawasumi
In this paper, we investigate necessary and sufficient conditions on the boundedness of composition operators on the Orlicz-Morrey spaces. The results of boundedness include Lebesg…
Koopman operators with intrinsic observables in rigged reproducing kernel Hilbert spaces
Isao Ishikawa, Yuka Hashimoto, Masahiro Ikeda +1
This paper presents a novel approach for estimating the Koopman operator defined on a reproducing kernel Hilbert space (RKHS) and its spectra. We propose an estimation method, what…
A unified Fourier slice method to derive ridgelet transform for a variety of depth-2 neural networks
Sho Sonoda, Isao Ishikawa, Masahiro Ikeda
To investigate neural network parameters, it is easier to study the distribution of parameters than to study the parameters in each neuron. The ridgelet transform is a pseudo-inver…
-Algebraic Machine Learning: Moving in a New Direction
Yuka Hashimoto, Masahiro Ikeda, Hachem Kadri
Machine learning has a long collaborative tradition with several fields of mathematics, such as statistics, probability and linear algebra. We propose a new direction for machine l…