AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This study revisits SNN robustness through the lens of temporal granularity, emphasizing the distinct behaviors that occur at individual time steps and proposes a Temporal Granularity Regu-larization term that constrains the TSV across all time steps, thereby improving the model’s overall robustness.
This work revisits SNN robustness from an information-theoretic perspective and reveals the pivotal role of temporal characteristics, and proposes a Temporal Mutual Information (TMI) regularizer that explicitly exploits temporal characteristics to enhance robustness.
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