Mercurial > ift6266
comparison writeup/mlj_submission.tex @ 588:9a6abcf143e8
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author | fsavard |
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date | Thu, 30 Sep 2010 17:51:46 -0400 |
parents | b1be957dd1be f5a198b2854a |
children | 1538412ee69d 4672fb6b4385 |
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127 It is also only recently that successful algorithms were proposed to | 127 It is also only recently that successful algorithms were proposed to |
128 overcome some of these difficulties. All are based on unsupervised | 128 overcome some of these difficulties. All are based on unsupervised |
129 learning, often in an greedy layer-wise ``unsupervised pre-training'' | 129 learning, often in an greedy layer-wise ``unsupervised pre-training'' |
130 stage~\citep{Bengio-2009}. One of these layer initialization techniques, | 130 stage~\citep{Bengio-2009}. One of these layer initialization techniques, |
131 applied here, is the Denoising | 131 applied here, is the Denoising |
132 Auto-encoder~(DA)~\citep{VincentPLarochelleH2008-very-small} (see Figure~\ref{fig:da}), | 132 Auto-encoder~(DA)~\citep{VincentPLarochelleH2008} (see Figure~\ref{fig:da}), |
133 which | 133 which |
134 performed similarly or better than previously proposed Restricted Boltzmann | 134 performed similarly or better than previously proposed Restricted Boltzmann |
135 Machines in terms of unsupervised extraction of a hierarchy of features | 135 Machines in terms of unsupervised extraction of a hierarchy of features |
136 useful for classification. Each layer is trained to denoise its | 136 useful for classification. Each layer is trained to denoise its |
137 input, creating a layer of features that can be used as input for the next layer. | 137 input, creating a layer of features that can be used as input for the next layer. |