Cheng Yang

From Objectives to Applications: Aligning Architectural Biases in Audio Self-Supervised Learning

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.