contributed insights into the working of DNNs and experience in training them. His work helped Yu and teammates produce a paper called Context-Dependent Pre-trained Deep Neural Networks for Large Vocabulary Speech Recognition.
In October 2010, Yu presented the paper during a visit to Microsoft Research Asia. Seide was intrigued by the research results, and the two joined forces in a collaboration that has scaled up the new, DNN-based algorithms to thousands of hours of training data."
For people interested in some (currently) undocumented research code in python implementing DNNs that is also on my website. Although the code is only an initial release. I will improve it later, but if I waited until it wasn't embarrassing I would never release it, so I just posted it.
Overwhelmingly, it is my experience that researchers in computational disciplines publish papers with half-finished code "available on request" -- and requests are often ignored. It's refreshing to hear someone say, "Yes, the code needs work, but it should be available."
http://blogs.technet.com/b/inside_microsoft_research/archive...
"An intern at Microsoft Research Redmond, George Dahl, now at the University of Toronto,
http://www.cs.toronto.edu/~gdahl/
contributed insights into the working of DNNs and experience in training them. His work helped Yu and teammates produce a paper called Context-Dependent Pre-trained Deep Neural Networks for Large Vocabulary Speech Recognition.
http://research.microsoft.com/pubs/144412/DBN4LVCSR-TransASL...
In October 2010, Yu presented the paper during a visit to Microsoft Research Asia. Seide was intrigued by the research results, and the two joined forces in a collaboration that has scaled up the new, DNN-based algorithms to thousands of hours of training data."