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bowlesbe

HN member
Joined
Sat, Nov 19, 2016, 12:16 AM UTC
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16 items

About bowlesbe

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Recent public activity

  1. comment
    Comment #12995296

    Could you elaborate? I'm not sure if I follow

  2. comment
    Comment #12995290

    haha, absolutely. It takes a lot of intelligence to detect non-informativeness. you might enjoy: http://journal.sjdm.org/15/15923a/jdm15923a.pdf

  3. comment
    Comment #12995265

    Are you sure about the padding? On page 1746, bottom right it says "padded as necessary". And intuitively it makes sense that all your inputs need to be the same size for a CNN.

  4. comment
    Comment #12995251

    This is a great point, would be worth further investigation. And I agree with your general interpretation. It would be interesting to look further at where CNN is failing to detect…

  5. comment
    Comment #12995228

    I think deep learning can be seen as a class of machine learning techniques with more flexibility and which uses neural networks (usually with quite a few layers).

  6. comment
    Comment #12995224

    I appreciate this!

  7. comment
    Comment #12995217

    This is actually a great point. Thanks for sharing. I should maybe considering removing LIME in that context or changing the wording.

  8. comment
    Comment #12995185

    Thanks, would you mind expanding? I also played around with some char CNNs. They had similar performance.

  9. comment
    Comment #12992174

    50K is not that tiny, but I'd say in the hundreds is pretty small. I'd say hundreds of training examples is pretty normal in academia, but typically quite small in industry, partic…

  10. comment
    Comment #12992163

    I would agree. Its kind of amazing that it indeed it does seem to work better than simpler models.

  11. comment
    Comment #12992157

    This is really great idea. Actually if there is something you can share along these lines, that would be amazing. I know Crowd Flower has a great "internal only" tool, which is kin…

  12. comment
    Comment #12992142

    Thanks! I'll check it out. I have also been reading about abstractive summarization - hard problem it seems!

  13. comment
    Comment #12991438

    Great question and I share your intuition but I think its all properly regularizing your model. I guess for neural networks, Dropout works really darn well as a regularization stra…

  14. comment
    Comment #12991134

    This is correct. We had 300-400 examples of each

  15. comment
    Comment #12991130

    Great point! I considering using fasttext as a baseline, however in practice fasttext really didn't work well at all with the small data set, much worse than the tfidf baseline. I …

  16. comment
    Comment #12991122

    Thanks for the comment! There were a few hundred sentences of each, collected internally from from a wide number of descriptions. Yes, I'd definitely agree- more data is what we ne…