TNAS-ER uses an ANN-assisted evolutionary search to optimize TTFS SNN architectures, achieving high emotion recognition performance with improved energy efficiency on neuromorphic hardware.
Emotion recognition in human-computer in- teraction.IEEE Signal processing magazine, 18(1):32–80,
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A Beta distribution framework models annotator consensus in continuous affect prediction by estimating mean and variance parameters to recover variability, skewness, and quantiles.
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Neural Architecture Search of Time-to-First-Spike-Coded Spiking Neural Networks for Efficient Eye-based Emotion Recognition
TNAS-ER uses an ANN-assisted evolutionary search to optimize TTFS SNN architectures, achieving high emotion recognition performance with improved energy efficiency on neuromorphic hardware.
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Beyond the Mean: Modelling Annotation Distributions in Continuous Affect Prediction
A Beta distribution framework models annotator consensus in continuous affect prediction by estimating mean and variance parameters to recover variability, skewness, and quantiles.