AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Audio annotation is particularly costly and prone to errors due to the temporal nature and semantic ambiguity in audio perception. Active learning (AL) addresses this by iteratively selecting the most informative samples from an unlabeled pool for expert labeling, thereby maximizing model performance with minimal annot...
This paper examines audio self-supervised learning through the alignment between pretraining objectives, architectural inductive biases, and downstream applications, and relates these demands to the biases of CNNs, recurrent and State Space Models, Transformers, and hybrid architectures.
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