15 citations · 15 across the 5 of their papers we have counts for
6 papers
Slow-spectrum preshaping for reservoir computing: Attractor reconstruction in attracting submanifolds 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…
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…
Inducing fast-slow separation enables robust learning of complex dynamics
Satoshi Oishi, Hiroshi Yamashita, Hideyuki Suzuki +1
Many real-world systems share a fast-slow structure: most degrees of freedom relax quickly, while a few slow modes govern the long-term evolution. Their dynamics often collapse ont…
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…
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…
Spatial-photonic Ising machine by space-division multiplexing with physically tunable coefficients of a multi-component model
Takumi Sakabe, Suguru Shimomura, Yusuke Ogura +4
This paper proposes a space-division multiplexed spatial-photonic Ising machine (SDM-SPIM) that physically calculates the weighted sum of the Ising Hamiltonians for individual comp…