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Bayesian continual learning via spiking neural networks
Nicolas Skatchkovsky
, Hyeryung Jang
, Osvaldo Simeone
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Contribution to journal
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Article
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peer-review
16
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Scopus citations
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Keyphrases
Spiking Neural Networks
100%
Uncertainty Quantification
100%
Continual Learning
100%
Adaptation
66%
Postsynaptic Density Protein 95 (PSD-95)
66%
Energy Efficiency
33%
Energy Efficient
33%
Learning Framework
33%
Online Learning
33%
Computing Paradigm
33%
Risk Management
33%
Producing Well
33%
Time-based
33%
Distribution Parameter
33%
Intel
33%
Learning Task
33%
Neuromorphic System
33%
Adaptive Management
33%
Neuromorphic Engineering
33%
Learning Rule
33%
Lava
33%
Biological Brain
33%
Frequentist
33%
Epistemic Uncertainty
33%
Biological Intelligence
33%
Rule Update
33%
Continuous Adaptation
33%
Computer Science
Neural Network
100%
Synaptic Weight
100%
Energy Efficient
50%
Energy Efficiency
50%
computing paradigm
50%
Experimental Result
50%
Learning Framework
50%
Online Learning
50%
Risk Management
50%
Engineering
Uncertainty Quantification
100%
Synaptic Weight
66%
Engineering
33%
Energy Engineering
33%
Energy Efficiency
33%
Energy Conservation
33%
Observed Data
33%
Experimental Result
33%
Learning Task
33%
Neuromorphic System
33%
Learning Rule
33%
Distribution Parameter
33%
Epistemic Uncertainty
33%
Chemical Engineering
Neural Network
100%