Cargando…
Symbolic Representation and Learning With Hyperdimensional Computing
It has been proposed that machine learning techniques can benefit from symbolic representations and reasoning systems. We describe a method in which the two can be combined in a natural and direct way by use of hyperdimensional vectors and hyperdimensional computing. By using hashing neural networks...
Autores principales: | Mitrokhin, Anton, Sutor, Peter, Summers-Stay, Douglas, Fermüller, Cornelia, Aloimonos, Yiannis |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7805681/ https://www.ncbi.nlm.nih.gov/pubmed/33501231 http://dx.doi.org/10.3389/frobt.2020.00063 |
Ejemplares similares
-
Editorial: Brain-inspired Hyperdimensional Computing: Algorithms, models, and architectures
por: Jiao, Xun, et al.
Publicado: (2022) -
GradTac: Spatio-Temporal Gradient Based Tactile Sensing
por: Ganguly, Kanishka, et al.
Publicado: (2022) -
Symbolic, Distributed, and Distributional Representations for Natural Language Processing in the Era of Deep Learning: A Survey
por: Ferrone, Lorenzo, et al.
Publicado: (2020) -
Symbolic-Based Recognition of Contact States for Learning Assembly Skills
por: Al-Yacoub, Ali, et al.
Publicado: (2019) -
Symbolic Learning and Reasoning With Noisy Data for Probabilistic Anchoring
por: Zuidberg Dos Martires, Pedro, et al.
Publicado: (2020)