no code implementations • 5 Mar 2024 • Shumpei Kobayashi, Quoc Hoan Tran, Kohei Nakajima
The echo state property (ESP) represents a fundamental concept in the reservoir computing (RC) framework that ensures output-only training of reservoir networks by being agnostic to the initial states and far past inputs.
1 code implementation • 2 Dec 2023 • Thomas Geert de Jong, Nozomi Akashi, Tomohiro Taniguchi, Hirofumi Notsu, Kohei Nakajima
We provide high-speed implementations for simulating reservoirs described by $N$-coupled spin-torque oscillators.
no code implementations • 19 Sep 2022 • Katsushi Kagaya, Tomoyuki Kubota, Kohei Nakajima
Self-organized criticality is a principle explaining avalanche-like phenomena obeying power-laws in integrate-and-fire type dynamical systems.
no code implementations • 1 Sep 2022 • Quoc Hoan Tran, Sanjib Ghosh, Kohei Nakajima
Current technologies in quantum-based communications bring a new integration of quantum data with classical data for hybrid processing.
no code implementations • 16 Jul 2022 • Tomoyuki Kubota, Yudai Suzuki, Shumpei Kobayashi, Quoc Hoan Tran, Naoki Yamamoto, Kohei Nakajima
We demonstrate this ability in several typical benchmarks and investigate the information processing capacity to clarify the framework's processing mechanism and memory profile.
no code implementations • 6 Jul 2022 • Yansong Li, Kai Hu, Kohei Nakajima, Yongping Pan
Echo state network (ESN), a kind of recurrent neural networks, consists of a fixed reservoir in which neurons are connected randomly and recursively and obtains the desired output only by training output connection weights.
no code implementations • 1 Apr 2022 • Mitsumasa Nakajima, Katsuma Inoue, Kenji Tanaka, Yasuo Kuniyoshi, Toshikazu Hashimoto, Kohei Nakajima
In addition, we can emulate and accelerate the computation for this training on a simple and scalable physical system.
no code implementations • 6 Jun 2021 • Katsuma Inoue, Soh Ohara, Yasuo Kuniyoshi, Kohei Nakajima
A Lite BERT (ALBERT) is literally characterized as a lightweight version of BERT, in which the number of BERT parameters is reduced by repeatedly applying the same neural network called Transformer's encoder layer.
no code implementations • 25 Mar 2021 • Quoc Hoan Tran, Kohei Nakajima
Quantifying and verifying the control level in preparing a quantum state are central challenges in building quantum devices.
no code implementations • 1 Sep 2020 • Takahiro Goto, Quoc Hoan Tran, Kohei Nakajima
This feature map provides opportunities to incorporate quantum advantages into machine learning algorithms to be performed on near-term intermediate-scale quantum computers.
1 code implementation • 16 Jun 2020 • Quoc Hoan Tran, Kohei Nakajima
Quantum reservoir computing (QRC) is an emerging paradigm for harnessing the natural dynamics of quantum systems as computational resources that can be used for temporal machine learning tasks.
no code implementations • 3 May 2020 • Kohei Nakajima
Understanding the fundamental relationships between physics and its information-processing capability has been an active research topic for many years.
no code implementations • 24 Dec 2019 • Taichi Haruna, Kohei Nakajima
The ability of discrete-time nonlinear recurrent neural networks to store time-varying small input signals is investigated by mean-field theory.
no code implementations • 11 Jun 2019 • Tomoyuki Kubota, Hirokazu Takahashi, Kohei Nakajima
First, we establish a connection between the IPC for time-invariant systems and PC expansion, which is a type of polynomial expansion using orthogonal functions of input history as bases.
no code implementations • 14 Feb 2018 • Hisashi Iwade, Kohei Nakajima, Takuma Tanaka, Toshio Aoyagi
The inherent transient dynamics of recurrent neural networks (RNNs) have been exploited as a computational resource in input-driven RNNs.
no code implementations • 26 Feb 2016 • Keisuke Fujii, Kohei Nakajima
Quantum computer has an amazing potential of fast information processing.