2 citations · 8 across the 11 of their papers we have counts for
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Koopman analysis of combinatorial optimization problems with replica exchange Monte Carlo method
Tatsuya Naoi, Tatsuya Kishimoto, Jun Ohkubo
Combinatorial optimization problems play crucial roles in real-world applications, and many studies from a physics perspective have contributed to specialized hardware for high-spe…
Aspects of importance sampling in parameter selection for neural networks using ridgelet transform
Hikaru Homma, Jun Ohkubo
The choice of parameters in neural networks is crucial in the performance, and an oracle distribution derived from the ridgelet transform enables us to obtain suitable initial para…
Improvement of system identification of stochastic systems via Koopman generator and locally weighted expectation
Yuki Tahara, Kakutaro Fukushi, Shunta Takahashi +2
The estimation of equations from data is of interest in physics. One of the famous methods is the sparse identification of nonlinear dynamics (SINDy), which utilizes sparse estimat…
Compression of the Koopman matrix for nonlinear physical models via hierarchical clustering
Tomoya Nishikata, Jun Ohkubo
Machine learning methods allow the prediction of nonlinear dynamical systems from data alone. The Koopman operator is one of them, which enables us to employ linear analysis for no…
Extraction of nonlinearity in neural networks with Koopman operator
Naoki Sugishita, Kayo Kinjo, Jun Ohkubo
Nonlinearity plays a crucial role in deep neural networks. In this paper, we investigate the degree to which the nonlinearity of the neural network is essential. For this purpose,…