Learning to Generate Natural Source Code

Add to your list(s) Download to your calendar using vCal

• Daniel Tarlow, Microsoft Research Cambridge
• Friday 15 November 2013, 12:00-13:00
• FW26, Computer Laboratory.

If you have a question about this talk, please contact Tamara Polajnar.

Natural source code is source code that is written by and meant to be understood by humans. I’ll talk about recent efforts to build generative models that (a) capture the structure present in source code, and (b) can be learned efficiently from large repositories of existing code. Our approach builds upon the fast training of neural probabilistic language models work of Mnih & Teh (2012), but incorporates hierarchical structure and much additional source code-specific structure. Empirically, our new models substantially outperform existing language models in terms of log probability of held out data, and samples from the learned models look more like real source code.

This talk is part of the NLIP Seminar Series series.

This talk is included in these lists:

• All Talks (aka the CURE list)
• Cambridge Centre for Data-Driven Discovery (C2D3)
• Cambridge Forum of Science and Humanities
• Cambridge Language Sciences
• Cambridge talks
• Chris Davis' list
• Computer Education Research
• Computing Education Research
• Department of Computer Science and Technology talks and seminars
• FW26, Computer Laboratory
• Graduate-Seminars
• Guy Emerson's list
• Interested Talks
• Language Sciences for Graduate Students
• NLIP Seminar Series
• PMRFPS's
• Simon Baker's List
• Trust & Technology Initiative - interesting events
• bld31
• ndk22's list
• ob366-ai4er
• rp587

Note that ex-directory lists are not shown.