AlgebraNets

Neural networks have historically been built layerwise from the set of functions in ${f: \mathbb{R}^n \to \mathbb{R}^m }$, i.e. with activations and weights/parameters represented by real numbers, $\mathbb{R}$. Our work considers a richer set of objects for activations and weights, and undertakes a...

Ausführliche Beschreibung

Bibliographische Detailangaben

Hauptverfasser:	Hoffmann, Jordan, Schmitt, Simon, Osindero, Simon, Simonyan, Karen, Elsen, Erich
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science - Learning Statistics - Machine Learning
Online-Zugang:	Volltext bestellen
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