Deep Networks from the Principle of Rate Reduction

This work attempts to interpret modern deep (convolutional) networks from the principles of rate reduction and (shift) invariant classification. We show that the basic iterative gradient ascent scheme for optimizing the rate reduction of learned features naturally leads to a multi-layer deep network...

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Bibliographische Detailangaben

Hauptverfasser:	Chan, Kwan Ho Ryan, Yu, Yaodong, You, Chong, Qi, Haozhi, Wright, John, Ma, Yi
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science - Information Theory Computer Science - Learning Mathematics - Information Theory Mathematics - Optimization and Control Statistics - Machine Learning
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