22 citations · 30 across the 8 of their papers we have counts for
8 papers
Kalman filters as the steady-state solution of gradient descent on variational free energy
Manuel Baltieri, Takuya Isomura
The Kalman filter is an algorithm for the estimation of hidden variables in dynamical systems under linear Gauss-Markov assumptions with widespread applications across different fi…
Quadratic speedup of global search using a biased crossover of two good solutions
Takuya Isomura
The minimisation of cost functions is crucial in various optimisation fields. However, identifying their global minimum remains challenging owing to the huge computational cost inc…
Dimensionality reduction to maximize prediction generalization capability
Takuya Isomura, Taro Toyoizumi
Generalization of time series prediction remains an important open issue in machine learning, wherein earlier methods have either large generalization error or local minima. We dev…
Computational cost for determining an approximate global minimum using the selection and crossover algorithm
Takuya Isomura
This work examines the expected computational cost to determine an approximate global minimum of a class of cost functions characterized by the variance of coefficients. The cost f…
Suppression of macroscopic oscillations in mixed populations of active and inactive oscillators coupled through lattice Laplacian
Ikuhiro Yamaguchi, Takuya Isomura, Hiroya Nakao +3
We consider suppression of macroscopic synchronized oscillations in mixed populations of active and inactive oscillators with local diffusive coupling, described by a lattice compl…
On the achievability of blind source separation for high-dimensional nonlinear source mixtures
Takuya Isomura, Taro Toyoizumi
For many years, a combination of principal component analysis (PCA) and independent component analysis (ICA) has been used for blind source separation (BSS). However, it remains un…
Objective and efficient inference for couplings in neuronal networks
Yu Terada, Tomoyuki Obuchi, Takuya Isomura +1
Inferring directional couplings from the spike data of networks is desired in various scientific fields such as neuroscience. Here, we apply a recently proposed objective procedure…
Inferring neuronal couplings from spiking data using a systematic procedure with a statistical criterion
Yu Terada, Tomoyuki Obuchi, Takuya Isomura +1
Recent remarkable advances in the experimental techniques have provided a background for inferring neuronal couplings from point process data that includes a great number of neuron…