Cargando…
Physical deep learning with biologically inspired training method: gradient-free approach for physical hardware
Ever-growing demand for artificial intelligence has motivated research on unconventional computation based on physical devices. While such computation devices mimic brain-inspired analog information processing, the learning procedures still rely on methods optimized for digital processing such as ba...
Autores principales: | Nakajima, Mitsumasa, Inoue, Katsuma, Tanaka, Kenji, Kuniyoshi, Yasuo, Hashimoto, Toshikazu, Nakajima, Kohei |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9792515/ https://www.ncbi.nlm.nih.gov/pubmed/36572696 http://dx.doi.org/10.1038/s41467-022-35216-2 |
Ejemplares similares
-
Designing spontaneous behavioral switching via chaotic itinerancy
por: Inoue, Katsuma, et al.
Publicado: (2020) -
Skeletonizing the Dynamics of Soft Continuum Body from Video
por: Inoue, Katsuma, et al.
Publicado: (2022) -
Brain inspired hardware architectures - Can they be used for particle physics ?
por: Meier, Karlheinz
Publicado: (2016) -
Deep learning and physics
por: Tanaka, Akinori, et al.
Publicado: (2021) -
Brain-Inspired Hardware Solutions for Inference in Bayesian Networks
por: Bagheriye, Leila, et al.
Publicado: (2021)