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https://issues.apache.org/jira/browse/SPARK-10408?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16002326#comment-16002326
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Nick Pentreath edited comment on SPARK-10408 at 5/9/17 9:00 AM:
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What is the status here? I think it's fairly safe to say that it's unlikely
that Spark will support much in the way of deep learning within the core
itself.
That said there may still be an argument for adding the {{MLPRegressor}} and an
autoencoder - but I'm concerned we lack the review and maintenance bandwidth
currently.
was (Author: mlnick):
What is the status here? I think it's fairly safe to say that it's unlikely
that Spark will support much in the way of deep learning itself.
That said there may still be an argument for adding the {{MLPRegressor}} and an
autoencoder - but I'm concerned we lack the review and maintenance bandwidth
currently.
> Autoencoder
> -----------
>
> Key: SPARK-10408
> URL: https://issues.apache.org/jira/browse/SPARK-10408
> Project: Spark
> Issue Type: Improvement
> Components: ML
> Affects Versions: 1.5.0
> Reporter: Alexander Ulanov
> Assignee: Alexander Ulanov
>
> 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. Vincent, Pascal, et al. "Extracting and composing robust features with
> denoising autoencoders." Proceedings of the 25th international conference on
> Machine learning. ACM, 2008.
> http://www.iro.umontreal.ca/~vincentp/Publications/denoising_autoencoders_tr1316.pdf
>
> 2.
> http://machinelearning.wustl.edu/mlpapers/paper_files/ICML2011Rifai_455.pdf,
> 3. Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., and Manzagol, P.-A.
> (2010). Stacked denoising autoencoders: Learning useful representations in a
> deep network with a local denoising criterion. Journal of Machine Learning
> Research, 11(3371–3408).
> http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.297.3484&rep=rep1&type=pdf
> 4, 5, 6. Bengio, Yoshua, et al. "Greedy layer-wise training of deep
> networks." Advances in neural information processing systems 19 (2007): 153.
> http://www.iro.umontreal.ca/~lisa/pointeurs/dbn_supervised_tr1282.pdf
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