101 citations · 312 across the 33 of their papers we have counts for
12 papers · 1 filter
Modeling Nonlinear Dynamics in Continuous Time with Inductive Biases on Decay Rates and/or Frequencies
Tomoharu Iwata, Yoshinobu Kawahara
We propose a neural network-based model for nonlinear dynamics in continuous time that can impose inductive biases on decay rates and/or frequencies. Inductive biases are helpful f…
Many-body Approximation for Non-negative Tensors
Kazu Ghalamkari, Mahito Sugiyama, Yoshinobu Kawahara
We present an alternative approach to decompose non-negative tensors, called many-body approximation. Traditional decomposition methods assume low-rankness in the representation, r…
Reproducing kernel Hilbert C*-module and kernel mean embeddings
Yuka Hashimoto, Isao Ishikawa, Masahiro Ikeda +3
Kernel methods have been among the most popular techniques in machine learning, where learning tasks are solved using the property of reproducing kernel Hilbert space (RKHS). In th…
Neural Dynamic Mode Decomposition for End-to-End Modeling of Nonlinear Dynamics
Tomoharu Iwata, Yoshinobu Kawahara
Koopman spectral analysis has attracted attention for understanding nonlinear dynamical systems by which we can analyze nonlinear dynamics with a linear regime by lifting observati…
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…
Analysis via Orthonormal Systems in Reproducing Kernel Hilbert -Modules and Applications
Yuka Hashimoto, Isao Ishikawa, Masahiro Ikeda +3
Kernel methods have been among the most popular techniques in machine learning, where learning tasks are solved using the property of reproducing kernel Hilbert space (RKHS). In th…