Open AccessDissertation
Learning Multiple Layers of Features from Tiny Images
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TLDR
In this paper, the authors describe how to train a multi-layer generative model of natural images, using a dataset of millions of tiny colour images, described in the next section.Abstract:
In this work we describe how to train a multi-layer generative model of natural images. We use a dataset of millions of tiny colour images, described in the next section. This has been attempted by several groups but without success. The models on which we focus are RBMs (Restricted Boltzmann Machines) and DBNs (Deep Belief Networks). These models learn interesting-looking filters, which we show are more useful to a classifier than the raw pixels. We train the classifier on a labeled subset that we have collected and call the CIFAR-10 dataset.read more
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References
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Journal ArticleDOI
WordNet: a lexical database for English
TL;DR: WordNet1 provides a more effective combination of traditional lexicographic information and modern computing, and is an online lexical database designed for use under program control.
Proceedings ArticleDOI
On the quantitative analysis of deep belief networks
Ruslan Salakhutdinov,Iain Murray +1 more
TL;DR: It is shown that Annealed Importance Sampling (AIS) can be used to efficiently estimate the partition function of an RBM, and a novel AIS scheme for comparing RBM's with different architectures is presented.
Proceedings Article
Unsupervised learning of distributions on binary vectors using two layer networks
Yoav Freund,David Haussler +1 more
TL;DR: It is shown that arbitrary distributions of binary vectors can be approximated by the combination model and shown how the weight vectors in the model can be interpreted as high order correlation patterns among the input bits, and how the combination machine can be used as a mechanism for detecting these patterns.