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Baseline Drift Tolerant Signal Encoding for ECG Classification with Deep Learning
Robustness to artefacts was assessed by corrupting ECG data with sinusoidal baseline drift, shift, rescaling and noise, before encoding.
Stochastic Spiking Attention: Accelerating Attention with Stochastic Computing in Spiking Networks
Spiking Neural Networks (SNNs) have been recently integrated into Transformer architectures due to their potential to reduce computational demands and to improve power efficiency.
Bayesian Inference Accelerator for Spiking Neural Networks
Bayesian neural networks offer better estimates of model uncertainty compared to frequentist networks.
Bayesian Inference on Binary Spiking Networks Leveraging Nanoscale Device Stochasticity
Bayesian Neural Networks (BNNs) can overcome the problem of overconfidence that plagues traditional frequentist deep neural networks, and are hence considered to be a key enabler for reliable AI systems.
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