Practical Bayesian Inference for Speech SNNs: Uncertainty and Loss-Landscape Smoothing
Spiking Neural Networks (SNNs) are naturally suited for speech processing tasks due to their specific dynamics, which allows them to handle temporal data. However, the threshold-based generation of spikes in SNNs intuitively causes an angular or irregular predictive landscape. We explore the effect of using the Bayesian learning approach for the weights on the irregular predictive landscape. For the surrogate-gradient SNNs, we also explore the application of the Improved Variational Online Newton (IVON) approach, which is an efficient variational approach. The performance of the proposed approach is evaluated on the Heidelberg Digits and Speech Commands datasets. The hypothesis is that th...
| Authors | Yesmine Abdennadher, Philip N. Garner |
|---|---|
| Primary category | cs.LG |
| Published | 9 April 2026 |
| Last updated at source | 9 April 2026 |
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