5 papers · 1 filter
Extraction of linearized models from pre-trained networks via knowledge distillation
Fumito Kimura, Jun Ohkubo
Recent developments in hardware, such as photonic integrated circuits and optical devices, are driving demand for research on constructing machine learning architectures tailored f…
Koopman operator-based discussion on partial observation in stochastic systems
Jun Ohkubo
It is sometimes difficult to achieve a complete observation for a full set of observables, and partial observations are necessary. For deterministic systems, the Mori-Zwanzig forma…
Integrated utilization of equations and small dataset in the Koopman operator: applications to forward and inverse problems
Ichiro Ohta, Shota Koyanagi, Kayo Kinjo +1
In recent years, there has been a growing interest in data-driven approaches in physics, such as extended dynamic mode decomposition (EDMD). The EDMD algorithm focuses on nonlinear…
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…
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,…