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Great to see this picked up on HN - it's really interesting work. Terence is my colleague at USF (in fact he's the guy that got me to go there!) and it was grea
by jph00 10y ago
Great to see this picked up on HN - it's really interesting work. Terence is my colleague at USF (in fact he's the guy that got me to go there!) and it was great to see the journey from "could this possibly work" to "holy crap it does work!" over a period of many months. He's known for his work with parsers, of course (i.e. ANTLR), but recently he's been investing heavily in machine learning. This project is a great example of how domain experts can go into whole new areas by building on top of machine learning methods.
For anyone interested in getting into machine learning, I hope you'll consider the MSAN program at USF (https://www.usfca.edu/arts-sciences/graduate-programs/analytics https://www.usfca.edu/arts-sciences/graduate-programs/analyt...). Terence is the guy that made this program happen at USF and it's turned into something really special.
Disclaimer: I'm a deep learning researcher and teacher at USF.
- parrt 10y agoOne possible angle for improvement of this technology: use a deep learning net to conjure up a different feature vector than the one I handcrafted from language/grammar expertise. I believe this was your idea. :) Glad to have you on board teaching and doing research!
- nl 10y agoI'm pretty sure this would work really well. I've trained CharCNN on log files, and it generates really good examples files. To me that shows that even a comparatively simple model can capture syntax rules, so I'd imagine a LSTM would generate really good feature vectors.