6 papers
Task-Dependent Weighted Average Energy Controllability Score for Network Intervention
Kazuhiro Sato
Controllability scores provide principled information on where intervention should be applied in large-scale network systems when explicit control design is difficult. Two represen…
Data-Driven Regularized Time-Limited h2 Model Reduction from Noisy Impulse Responses
Hiroki Sakamoto, Kazuhiro Sato
This paper develops a data-driven time-limited h2 model reduction method for discrete-time linear time-invariant systems. Specifically, we formulate and solve a regularized time-li…
A Deep State-Space Model Compression Method using Upper Bound on Output Error
Hiroki Sakamoto, Kazuhiro Sato
We study deep state-space models (Deep SSMs) that contain linear quadratic-output (LQO) systems as internal blocks and present a compression method with a provable output error gua…
Compression Method for Deep Diagonal State Space Model Based on Optimal Reduction
Hiroki Sakamoto, Kazuhiro Sato
Deep learning models incorporating linear SSMs have gained attention for capturing long-range dependencies in sequential data. However, their large parameter sizes pose challenges…
Model Compression Method for S4 with Diagonal State Space Layers using Balanced Truncation
Haruka Ezoe, Kazuhiro Sato
To implement deep learning models on edge devices, model compression methods have been widely recognized as useful. However, it remains unclear which model compression methods are…
Data-driven h2 model reduction for linear discrete-time systems
Hiroki Sakamoto, Kazuhiro Sato
We present a data-driven framework for -optimal model reduction for linear discrete-time systems. Our main contribution is to create optimal reduced-order models in the $h^{…