Rediscovering Deep Neural Networks Through Finite-State Distributions

We propose a new way of thinking about deep neural networks, in which the linear and non-linear components of the network are naturally derived and justified in terms of principles in probability theory. In particular, the models constructed in our framework assign probabilities to uncertain realiza...

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Bibliographische Detailangaben

Hauptverfasser:	Marvasti, Amir Emad, Marvasti, Ehsan Emad, Atia, George, Foroosh, Hassan
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science - Learning Statistics - Machine Learning
Online-Zugang:	Volltext bestellen
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