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  1. Aug 2017
  2. www.reddit.com www.reddit.com
    Yann LeCun's proposal for a new peer review system. • r/MachineLearning
    1
    1. spector 16 Aug 2017
      Finally, any up vote based system is just going to push easy to understand, rather than good, papers up to the top.

      I don't think this is quite right. An upvote system like reddit's may result in this, but in OpenReview the only upvoting mechanism would be posting reviews. Those reviews could then be reviewed themselves, and if found to be a poor argument, the original review wouldn't affect the parent paper's score as much. We could require that people post an argument that directly addresses the parent post. We could score the post based on how "good" of an argument it is: http://www.paulgraham.com/disagree.html

      This could also provide a great way of gathering labeled data for argument mining: people refuting each other, and refuting refutations. It could lead to a virtuous cycle of learning how to mine for logical arguments in text.

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    reddit.com/r/MachineLearning/comments/16u8rm/yann_lecuns_proposal_for_a_new_peer_review_system/
  3. Oct 2016
  4. compsci697l.github.io compsci697l.github.io
    COMPSCI 697L Deep Learning
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    1. spector 30 Oct 2016
      C×(7×7×C)=49C2C×(7×7×C)=49C2C \times (7 \times 7 \times C) = 49 C^2 parameters

      Shouldn't this be K x (7 x 7 x C)?

      Or is it true that when they say "all the volumes have C channels," they mean that the input volume has C channels and there are K = C filters in the conv layer?

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    compsci697l.github.io/notes/convolutional-networks/
  5. Sep 2016
  6. compsci697l.github.io compsci697l.github.io
    COMPSCI 697L Deep Learning
    1
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    compsci697l.github.io/notes/optimization-1/
  7. Aug 2016
  8. www.deeplearningbook.org www.deeplearningbook.org
    Untitled document
    1
    1. spector 20 Aug 2016
      The directional derivative in direction u (a unit vector) is the slope of thefunctionfin directionu. In other words, the directional derivative is the derivativeof the functionf(x+αu) with respect toα, evaluated atα= 0. Using the chainrule, we can see that∂∂αf(x + αu) evaluates to u∇xf(x) when α = 0

      For a good explanation of this, see:

      https://www.khanacademy.org/math/multivariable-calculus/multivariable-derivatives/partial-derivative-and-gradient-articles/a/directional-derivatives-going-deeper

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    deeplearningbook.org/contents/numerical.html