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These models are often called “recursive neural networks” because one often has the output of a module go into a module of the same type. They are also sometimes called “tree-structured neural networks.”

Recursive neural networks have had significant successes in a number of NLP tasks. For example, Socher et al. (2013c) uses a recursive neural network to predict sentence sentiment:

(From Socher (2013c) )

One major goal has been to create a reversible sentence representation, a representation that one can reconstruct an actual sentence from, with roughly the same meaning. For example, we can try to introduce a disassociation module, D , that tries to undo A :

(From Bottou (2011) )

If we could accomplish such a thing, it would be an extremely powerful tool. For example, we could try to make a bilingual sentence representation and use it for translation.

Unfortunately, this turns out to be very difficult. Very very difficult. And given the tremendous promise, there are lots of people working on it.

Recently, Cho et al. (2014) have made some progress on representing phrases, with a model that can encode English phrases and decode them in French. Look at the phrase representations it learns!

Small section of the t-SNE of the phrase representation (From Cho (2014) )

I’ve heard some of the results reviewed above criticized by researchers in other fields, in particular, in NLP and linguistics. The concerns are not with the results themselves, but the conclusions drawn from them, and how they compare to other techniques.

I don’t feel qualified to articulate these concerns. I’d encourage someone who feels this way to describe the concerns in the comments.

The representation perspective of deep learning is a powerful view that seems to answer why deep neural networks are so effective. Beyond that, I think there’s something extremely beautiful about it: why are neural networks effective? Because better ways of representing data can pop out of optimizing layered models.

Deep learning is a very young field, where theories aren’t strongly established and views quickly change. That said, it is my impression that the representation-focused perspective of neural networks is presently very popular.

This post reviews a lot of research results I find very exciting, but my main motivation is to set the stage for a future post exploring connections between deep learning, type theory and functional programming. If you’re interested, you can subscribe to my rss feed so that you’ll see it when it is published.

(I would be delighted to hear your comments and thoughts: you can comment inline or at the end. For typos, technical errors, or clarifications you would like to see added, you are encouraged to make a pull request on github )

Click Configure Global Parameters .
Click New Parameter .
Type a Name for the parameter's key.
Enter a Value for the parameter.
Optionally select if you want the value to be hidden in the web UI.
Click Submit .

9.2.2.Configuring Smart Variables

The following procedure configures Smart Variables to override a value in a Puppet class.

Procedure9.3.To Configure Smart Variables:

Click Configure Puppet Classes .
Select a class from the list.
Click the Smart Variables tab. This displays a new screen. The left section contains a list of possible parameters the class supports. The right section contains the configuration options for the parameter selected. Click the Add Variable to add a new parameter. Otherwise, select a parameter from the left-hand list.
Type a name for the parameter in the Key field.
Edit the Description text box to add any plain text notes.
Select the Key type of data to pass. This is most commonly a string, but other data types are supported.
Enter a Default Value for the parameter to be sent to the Puppet Master if no host match occurs.
Optionally select Hidden value if the field contains data you do not want to be displayed while you are working.
Use the Optional Input Validator section to restrict the allowed values for the parameter. Choose a Validator type (either a list of comma separated values or a regular expression, regexp ) and input the allowed values or regular expression code in the Validator rule field.
The Prioritize attribute order section provides options for overriding values for specific hosts based upon conditional arguments. The attribute type and its value is known as a .
Set the Order of precedence in which the host attributes or Facts are to be evaluated against the matchers by arranging the entries in the list. You can add to the default list. To create a logical AND condition between matchers, arrange them on one line as a comma separated list.
Click Add Matcher to add a conditional argument. The attributes to match against should correspond to the entries in the Order list. If no matcher is configured then only the default value can be used for the override feature.
For example, if the desired value of the parameter to be supplied to the Puppet Master is for any host with a fully qualified domain name of , then specify the matcher as and the Value as .
If the matcher is a host attribute, use that.
If there are no attributes with that name, look for a matching host parameter (which is inherited according to the parameter hierarchy).
If there is still no match, check the host Facts.
Dynamic data is possible by using parameters and Puppet Facts in the Value field in ( ) template syntax. For example, to use a Puppet Fact as part of the value: To list available Puppet Facts navigate to Monitor Facts .
Click Submit to save your changes.
For further information on working with Puppet modules see Adding Puppet Modules to Red Hat Satellite 6 . For more information on ERB syntax see Leather THIGH HIGH BALLERINA Boots Spring/summer Rick Owens Outlet Wholesale Price QXIm4AE

9.2.3.Importing Parameterized Classes from a Puppet Master

The following procedure imports parameterized classes from your Puppet Master.
The import of parameterized classes happens automatically if your Puppet modules are managed via a Product and a Content View.

Procedure9.4.To Import Parameterized Classes:


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