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David Dahmen

2 accepted papers

2024

Critical feature learning in deep neural networks

ICML 2024poster

A key property of neural networks driving their success is their ability to learn features from data. Understanding feature learning from a theoretical viewpoint is an emerging field with many open questions. In this work we capture finite-width effects with a systematic theory of network kernels in…

Cited by 3SourcePDFScholar
2020

Unfolding recurrence by Green’s functions for optimized reservoir computing

NeurIPS 2020poster

Cortical networks are strongly recurrent, and neurons have intrinsic temporal dynamics. This sets them apart from deep feed-forward networks. Despite the tremendous progress in the application of deep feed-forward networks and their theoretical understanding, it remains unclear how the interplay of…

Cited by 5SourcePDFScholar