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https://issues.apache.org/jira/browse/SYSTEMML-540?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15198220#comment-15198220
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Mike Dusenberry commented on SYSTEMML-540:
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Update: I'm working on an experimental, layers-based framework directly in DML
to contain layer abstractions with simple forward/backward APIs for affine,
convolution (start with 2D), max-pooling, non-linearities (relu, sigmoid,
softmax, etc.), dropout, loss functions, and other layers. As part of this
experiment, I'm starting by implementing as much as possible in DML, and then
will move to built-in functions as necessary.
> Deep Learning
> -------------
>
> Key: SYSTEMML-540
> URL: https://issues.apache.org/jira/browse/SYSTEMML-540
> Project: SystemML
> Issue Type: Epic
> Reporter: Mike Dusenberry
> Assignee: Mike Dusenberry
>
> This epic covers the addition of deep learning to SystemML, including:
> * Core DML layer abstractions for deep (convolutional, recurrent) neural
> nets, with simple forward/backward API: affine, convolution (start with 2D),
> max-pooling, non-linearities (relu, sigmoid, softmax), dropout, loss
> functions.
> * Modularized DML optimizers: (mini-batch, stochastic) gradient descent (w/
> momentum, etc.).
> * Additional DML language support as necessary (tensors, built-in functions
> such as convolution, function pointers, list structures, etc.).
> * Integration with other deep learning frameworks (Caffe, Torch, Theano,
> TensoFlow, etc.) via automatic DML code generation.
> * etc.
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