Fast-Converging Simulated Annealing for Ising Models Based on Integral Stochastic Computing.
- DOI
- 10.1109/tnnls.2022.3159713
- Published
- 2023 Dec
- Container
- IEEE transactions on neural networks and learning systems
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1109/tnnls.2022.3159713,
title = {Fast-Converging Simulated Annealing for Ising Models Based on Integral Stochastic Computing.},
author = {Onizawa N and Katsuki K and Shin D and Gross WJ and Hanyu T},
year = {2023},
journal = {IEEE transactions on neural networks and learning systems},
doi = {10.1109/tnnls.2022.3159713},
url = {https://doi.org/10.1109/tnnls.2022.3159713}
}RIS
TY - JOUR TI - Fast-Converging Simulated Annealing for Ising Models Based on Integral Stochastic Computing. AU - Onizawa N AU - Katsuki K AU - Shin D AU - Gross WJ AU - Hanyu T PY - 2023 JO - IEEE transactions on neural networks and learning systems DO - 10.1109/tnnls.2022.3159713 UR - https://doi.org/10.1109/tnnls.2022.3159713 ER -
APA
N, O., K, K., D, S., WJ, G., & T, H. (2023). Fast-Converging Simulated Annealing for Ising Models Based on Integral Stochastic Computing.. IEEE transactions on neural networks and learning systems. https://doi.org/10.1109/tnnls.2022.3159713
Source records
- pubmed · retrieved 2026-11-10T12:14:46.954Z