Cargando…
A Survey on Probabilistic Models in Human Perception and Machines
Extracting information from noisy signals is of fundamental importance for both biological and artificial perceptual systems. To provide tractable solutions to this challenge, the fields of human perception and machine signal processing (SP) have developed powerful computational models, including Ba...
Autores principales: | Li, Lux, Rehr, Robert, Bruns, Patrick, Gerkmann, Timo, Röder, Brigitte |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7805657/ https://www.ncbi.nlm.nih.gov/pubmed/33501252 http://dx.doi.org/10.3389/frobt.2020.00085 |
Ejemplares similares
-
Human-Guided Learning for Probabilistic Logic Models
por: Odom, Phillip, et al.
Publicado: (2018) -
Using Probabilistic Movement Primitives in Analyzing Human Motion Differences Under Transcranial Current Stimulation
por: Xue, Honghu, et al.
Publicado: (2021) -
Symbolic Learning and Reasoning With Noisy Data for Probabilistic Anchoring
por: Zuidberg Dos Martires, Pedro, et al.
Publicado: (2020) -
A Survey for Machine Learning-Based Control of Continuum Robots
por: Wang, Xiaomei, et al.
Publicado: (2021) -
Perception is Only Real When Shared: A Mathematical Model for Collaborative Shared Perception in Human-Robot Interaction
por: Matarese, Marco, et al.
Publicado: (2022)