Cargando…
Heterogeneous Ensemble-Based Spike-Driven Few-Shot Online Learning
Spiking neural networks (SNNs) are regarded as a promising candidate to deal with the major challenges of current machine learning techniques, including the high energy consumption induced by deep neural networks. However, there is still a great gap between SNNs and the few-shot learning performance...
Autores principales: | Yang, Shuangming, Linares-Barranco, Bernabe, Chen, Badong |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9124799/ https://www.ncbi.nlm.nih.gov/pubmed/35615277 http://dx.doi.org/10.3389/fnins.2022.850932 |
Ejemplares similares
-
Efficient Spike-Driven Learning With Dendritic Event-Based Processing
por: Yang, Shuangming, et al.
Publicado: (2021) -
SAM: A Unified Self-Adaptive Multicompartmental Spiking Neuron Model for Learning With Working Memory
por: Yang, Shuangming, et al.
Publicado: (2022) -
Robust Spike-Based Continual Meta-Learning Improved by Restricted Minimum Error Entropy Criterion
por: Yang, Shuangming, et al.
Publicado: (2022) -
An Event-Driven Classifier for Spiking Neural Networks Fed with Synthetic or Dynamic Vision Sensor Data
por: Stromatias, Evangelos, et al.
Publicado: (2017) -
Cross Modal Few-Shot Contextual Transfer for Heterogenous Image Classification
por: Chen, Zhikui, et al.
Publicado: (2021)