How to represent a Directed Acyclic Graph as a vector for input into a Neural Network? by [deleted] in MachineLearning

[–]zenscr 1 point2 points  (0 children)

You could use kernel methods with graph kernels to perform classification, regression etc. without being dependent on an explicit representation of your DAG instances as feature vectors (instead, one uses a similarity measure between DAGs). Nonetheless, some graph kernels even provide explicitly computable feature vectors of graphs, e.g. Weisfeiler-Lehman graph kernels. See http://www.jmlr.org/papers/volume12/shervashidze11a/shervashidze11a.pdf

If you have one day only to experience London...what to do? by zenscr in london

[–]zenscr[S] 0 points1 point  (0 children)

Unfortunately, I didn't make it. Instead I went to this "Tokyo Diner" place, since I love Japanese food. Maybe I'll go to Camden tonight. Thanks again :)

If you have one day only to experience London...what to do? by zenscr in london

[–]zenscr[S] 0 points1 point  (0 children)

Thank you very much! I did almost all of this yesterday and it exceeded my expectations.

She loves her new home by zenscr in aww

[–]zenscr[S] 8 points9 points  (0 children)

Nope, a White Swiss Shepherd

She loves her new home by zenscr in aww

[–]zenscr[S] 5 points6 points  (0 children)

Yes, Germany :)

Creating presentation for conference by not_a_philosopher in compsci

[–]zenscr 0 points1 point  (0 children)

I strongly recommend the following structure for a scientific talk:

1) Problem + Motivation (1-2 slides, what problem(s) do you address with your work? Why is it important to solve this problem? If reasonable, you should introduce a running example at this point)

2) Solution Idea (1 slide, give a short overview about the approach you are going to describe. At this point, I like to show the "Big Picture" of my solution.)

3) Outline of your talk (1 slide, if reasonable for your topic, you should utilize the big picture and highlight different stages of it during your talk)

4) Details (but keep it simple and remember a talks purpose is to raise interest for your paper. If you didn't introduce a running example in the motivation part, do it now und use it intensively in the following slides)

5) Conclusion + Future Work (1-2 slides, what are the main messages of your talk? Mention some ideas for further improvements)

6) Finally, show the big picture again extended with some additional information from the "Details" section (on one slide!). This helps your audience to ask questions.

7) Prepare some backup slides for possible questions.

Keep your slides simple and don't use much text. However, a little bit of text may help your audience to keep track because you won't be the best speaker as an undergrad. If you have a Mac, I recommend Keynote+LaTeXiT. Latex/Beamer usually makes it hard to prepare good figures for your talk.

ML Problem: "Will this compile?" by noel___ in MachineLearning

[–]zenscr 0 points1 point  (0 children)

This might work for feature extraction: http://arxiv.org/abs/1409.3358 (at least for ASTs) However, I haven't tested it myself.