Mercurial > ift6266
comparison writeup/nips2010_submission.tex @ 512:6f042a71be23
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author | Yoshua Bengio <bengioy@iro.umontreal.ca> |
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date | Tue, 01 Jun 2010 14:02:04 -0400 |
parents | b8e33d3d7f65 |
children | 66a905508e34 |
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88 unsupervised learning and unlabeled examples~\citep{Bengio-2009,WestonJ2008-small} | 88 unsupervised learning and unlabeled examples~\citep{Bengio-2009,WestonJ2008-small} |
89 and multi-task learning, not much has been done yet to explore the impact | 89 and multi-task learning, not much has been done yet to explore the impact |
90 of {\em out-of-distribution} examples and of the multi-task setting | 90 of {\em out-of-distribution} examples and of the multi-task setting |
91 (but see~\citep{CollobertR2008}). In particular the {\em relative | 91 (but see~\citep{CollobertR2008}). In particular the {\em relative |
92 advantage} of deep learning for this settings has not been evaluated. | 92 advantage} of deep learning for this settings has not been evaluated. |
93 | 93 The hypothesis explored here is that a deep hierarchy of features |
94 % TODO: Explain why we care about this question. | 94 may be better able to provide sharing of statistical strength |
95 between different regions in input space or different tasks, | |
96 as discussed in the conclusion. | |
95 | 97 |
96 In this paper we ask the following questions: | 98 In this paper we ask the following questions: |
97 | 99 |
98 %\begin{enumerate} | 100 %\begin{enumerate} |
99 $\bullet$ %\item | 101 $\bullet$ %\item |