Software Release Announcement: SNARLI, free/open-source Java package for neural nets

[Simon Levy]

Dear Connectionists,

I would like to announce the release of a free, open-source Java package 
that may be of interest to members of this list.  This package is 
currently available at http://snarli.sourceforge.net, and is described 
below.

Please feel free to download this package, and contact me with question, 
criticism, or suggestions.  I am especially interested in hearing from 
educators and researchers who find the package useful in their work, and 
anyone who has a feature or neural architecture that they would like to 
see implemented.

Thanks,
Simon

========================

Simon D. Levy
Assistant Professor
Computer Science Department
Washington & Lee University
Lexington, VA 24450

540-458-8419 (voice)
540-458-8479 (fax)
levys at wlu.edu
http://www.cs.wlu.edu/~levy

 
*SNARLI* (*/S/*imple */N/*eural */AR/*chitecture */LI/*brary) is a Java 
package containing two classes: BPLayer, a general back-prop layer 
class, and SOM, a class for the Kohonen Self-Organizing Map. BPLayer 
also supports sigma-pi connections and back-prop-through-time, allowing 
you to build just about any kind of back-prop network found in the 
literature.

*SNARLI* differs from existing neural-net packages in two important 
ways: First, it is /not/ GUI-based. Instead, it is meant as a code 
resource that can be linked directly to new or existing Java-based 
projects, for those who want to try a neural-network approach without 
having to write a lot of new code. Given the variety of platforms that 
currently interface to Java, from HTML to Matlab 
<http://www.mathworks.com/>, it made more sense to me to focus on the 
neural net algorithms, and leave the GUI development to others.

Second, *SNARLI* gets a great deal of mileage out of a single class 
(BPLayer), instead of adding a new class for each type of network. Using 
this class, my students and I have been able to construct a large 
variety of back-prop networks, from simple perceptrons through Pollack's 
RAAM <http://demo.cs.brandeis.edu/papers/long.html#raam>, with very 
little additional coding. We have used these networks successfully in 
coursework <http://www.cs.wlu.edu/%7Elevy/cs397/>, thesis projects 
<http://www.cs.wlu.edu/%7Elevy/grieco_thesis.pdf>, and research 
<http://www.cs.wlu.edu/%7Elevy/papers>.

Future versions of *SNARLI* may include classes to support other popular 
architectures, such as Support Vector Machines 
<http://research.microsoft.com/%7Ejplatt/svm.html> (SVMs), Hopfield Nets 
<http://documents.wolfram.com/applications/neuralnetworks/index9.html>, 
and Long Short-Term Memory 
<http://www.inf.ethz.ch/%7Eschraudo/NNcourse/lstm.html> (LSTM), as user 
interest <mailto:levys at wlu.edu> dictates.