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Meaningness's profile
David Chapman
David Chapman
David Chapman
@Meaningness

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David Chapman

@Meaningness

Better ways of thinking, feeling, and acting—around problems of meaning and meaninglessness; self and society; ethics, purpose, and value.

meaningness.com/about-my-sites
Joined September 2010

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    1. David Chapman‏ @Meaningness 11 Nov 2017
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      Replying to @The_Lagrangian

      When I first saw this a few days ago I thought “peculiar —wonder if it’s true.” Looking at this graph, wondered what happened at 8-9k epochs (sudden dive).

      1 reply 0 retweets 1 like
    2. David Chapman‏ @Meaningness 11 Nov 2017
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      Replying to @Meaningness @The_Lagrangian

      Skimmed bits of paper, got increasingly suspicious that result is artifact of an artificial task. Went looking for the task… found this.pic.twitter.com/MmN7Lq6U3I

      2 replies 0 retweets 2 likes
    3. Adam Strandberg‏ @The_Lagrangian 11 Nov 2017
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      Replying to @Meaningness

      they mention later on that they also do the analysis for cases with no symmetries

      1 reply 0 retweets 0 likes
    4. Adam Strandberg‏ @The_Lagrangian 11 Nov 2017
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      Replying to @The_Lagrangian @Meaningness

      the high signal -> high noise transition in training is also general afaict in the couple of DL projects I've tried, NNs do in general seem to keep improving after a clear signal is gone

      1 reply 0 retweets 1 like
    5. David Chapman‏ @Meaningness 11 Nov 2017
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      Replying to @The_Lagrangian

      Oh, well *that* is interesting. I would like to see it demonstrated on a real task. This paper has gotten a lot of hype, which doesn’t seem deserved.pic.twitter.com/eiulZpOhkK

      2 replies 0 retweets 2 likes
    6. David Chapman‏ @Meaningness 11 Nov 2017
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      Replying to @Meaningness @The_Lagrangian

      Now I will drink my first a.m. coffee and stop being such a grump.

      1 reply 0 retweets 0 likes
    7. Adam Strandberg‏ @The_Lagrangian 11 Nov 2017
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      Replying to @Meaningness

      grump is good! that said I think there's a couple things here: (1) it's hard to expand this analysis past toy-ish data since you need to be able to define an actual distribution over the inputs, and NNs are usually used in cases where we don't have one (images, for instance)

      1 reply 0 retweets 1 like
    8. Adam Strandberg‏ @The_Lagrangian 11 Nov 2017
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      Replying to @The_Lagrangian @Meaningness

      (2) I think the explanation accounts for both why NNs are so successful _and_ for why they're so unsuccessful: they can only compress in the dumbest way possible (by randomly jiggling weights)

      1 reply 0 retweets 1 like
    9. David Chapman‏ @Meaningness 11 Nov 2017
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      Replying to @The_Lagrangian

      I see… (2) would be exciting if true, because you could explicitly drive for max mutual info instead. (Hasn’t this been tried? I don’t know the literature, but seems a likely move)

      1 reply 0 retweets 0 likes
    10. Adam Strandberg‏ @The_Lagrangian 11 Nov 2017
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      Replying to @Meaningness

      not that i know of but I am not that familiar with the lit. hopefully one of my DL researcher followers can chime in! (also- goal is for hidden layers to minimize mutual info with input (compression) while maximizing with output (error reduction))

      1 reply 0 retweets 0 likes
      David Chapman‏ @Meaningness 11 Nov 2017
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      Replying to @The_Lagrangian

      yeah, min, max, whatevs

      8:36 AM - 11 Nov 2017
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