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https://issues.apache.org/jira/browse/SPARK-10408?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14735706#comment-14735706
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Debasish Das commented on SPARK-10408:
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[~avulanov] In MLP can we change BFGS to OWLQN and get L1 regularization ? That
way I can get sparse weights and clean up the network to avoid
overfitting...For the autoencoder did you experiment with graphx based design ?
I would like to work on it. Basically the idea is to come up with a N layer
deep autoencoder that can support similar prediction APIs like matrix
factorization.
> Autoencoder
> -----------
>
> Key: SPARK-10408
> URL: https://issues.apache.org/jira/browse/SPARK-10408
> Project: Spark
> Issue Type: Umbrella
> Components: ML
> Affects Versions: 1.5.0
> Reporter: Alexander Ulanov
> Priority: Minor
>
> Goal: Implement various types of autoencoders
> Requirements:
> 1)Basic (deep) autoencoder that supports different types of inputs: binary,
> real in [0..1]. real in [-inf, +inf]
> 2)Sparse autoencoder i.e. L1 regularization. It should be added as a feature
> to the MLP and then used here
> 3)Denoising autoencoder
> 4)Stacked autoencoder for pre-training of deep networks. It should support
> arbitrary network layers:
> References:
> 1-3.
> http://machinelearning.wustl.edu/mlpapers/paper_files/ICML2011Rifai_455.pdf
> 4. http://machinelearning.wustl.edu/mlpapers/paper_files/NIPS2006_739.pdf
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