用贝叶斯方法让脉冲神经网络的预测更平滑，提升语音识别稳定性。
Practical Bayesian Inference for Speech SNNs: Uncertainty and Loss-Landscape Smoothing
- 引入贝叶斯学习优化权重，缓解脉冲网络的不规则预测地形。
- 在海德堡数字和语音命令数据集上，负对数似然与Brier得分均提升。
- 适合关注模型不确定性与鲁棒性的语音处理研究者。
脉冲神经网络（SNNs）因其动态特性天然适用于语音处理任务，但其基于阈值的脉冲生成机制导致预测景观呈现角状或不规则特征。本文研究了使用贝叶斯学习方法对权重进行建模对预测景观的影响。针对代理梯度训练的SNN，采用高效的变分在线牛顿法（IVON）进行优化。在海德堡数字和语音命令数据集上的实验表明，该方法使预测景观更加平滑和规则，且在负对数似然和Brier评分指标上优于确定性方法。结果验证了贝叶斯方法能有效改善不规则预测空间的结构。
原文摘要 · Abstract (English)
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 the Bayesian approach will result in a smoother and more regular predictive landscape, given the angular nature of the deterministic predictive landscape. The experimental evaluation of the proposed approach shows improved performance on the negative log-likelihood and Brier score. Furthermore, the proposed approach has resulted in a smoother and more regular predictive landscape compared to the deterministic approach, based on the one-dimensional slices of the weight space
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