Neural Variational Inference and Learning in Belief Networks

Proceedings of the 31st International Conference on Machine Learning (ICML), JMLR: W&CP volume 32, 2014 pgs 1791-1799 Highly expressive directed latent variable models, such as sigmoid belief networks, are difficult to train on large datasets because exact inference in them is intractable and no...

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Hauptverfasser:	Mnih, Andriy, Gregor, Karol
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science - Learning Statistics - Machine Learning
Online-Zugang:	Volltext bestellen
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