5 papers
Attractor reconstruction in attracting subspaces: Slow-spectrum preshaping for reservoir computing under partial observation
Satoshi Oishi, Hiroshi Yamashita, Hideyuki Suzuki +1
Data-driven reproduction of chaotic dynamics under partial observation remains a challenge despite its practical importance. Reservoir computing (RC) and other data-driven approach…
Convolutional Formulation of Large-Scale Quadratic Unconstrained Binary Optimization with Dense Interactions
Hiroshi Yamashita, Hideyuki Suzuki
The spatial photonic Ising machine (SPIM) is a promising optical hardware solver for large-scale combinatorial optimization problems with dense interactions. As the SPIM can repres…
Designing Zero-Mean Feature Functions for Multimodal Distributions
Hiroshi Yamashita, Hideyuki Suzuki
To improve the accuracy of Monte Carlo estimation of expectations, a set of zero-mean feature functions, known as control variates, can be used. They can be used as feature functio…
Stabilizing chaotic dynamical system reproduction in reservoir computing
Satoshi Oishi, Hiroshi Yamashita, Hideyuki Suzuki +1
Reservoir Computing (RC), a type of recurrent random neural network, is a powerful framework for modeling complex and chaotic dynamics. However, its autonomous (closed-loop) operat…
Deterministic Discrete Denoising
Hideyuki Suzuki, Wataru Kurebayashi, Hiroshi Yamashita
We propose a deterministic denoising algorithm for discrete-state diffusion models. The key idea is to derandomize the generative reverse Markov chain by introducing a variant of t…