Derives tighter Koopman-based generalization bounds for multi-task DNNs by assuming small weight-matrix condition numbers and using a tailored Sobolev space as hypothesis class.
In: Advances in Neural Information Pro- cessing Systems
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On the Koopman-Based Generalization Bounds for Multi-Task Deep Learning
Derives tighter Koopman-based generalization bounds for multi-task DNNs by assuming small weight-matrix condition numbers and using a tailored Sobolev space as hypothesis class.