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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 27 Mar 2017
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      “Universal adversarial perturbations” seems most dramatic ML result in years; if so, not getting deserved attention https://arxiv.org/pdf/1610.08401v1.pdf …

      16 replies 338 retweets 674 likes
      David Chapman‏ @Meaningness 27 Mar 2017
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      Result: there is a *fixed*, human-invisible map you can add to *any* image, and it renders it unclassifiable by multiple DL systemspic.twitter.com/s4zMsPy6Jy

      2:45 PM - 27 Mar 2017
      • 490 Retweets
      • 650 Likes
      • i retweet gotes John rasmi Tamara Kat Chuang Madthing in ze Attic Richard Johnson Steve Tuck Alex Harrowell
      22 replies 490 retweets 650 likes
        1. New conversation
        2. David Chapman‏ @Meaningness 27 Mar 2017
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          Replying to @Meaningness

          I have long suspected that DL image classifiers depend mainly on texture and maybe color, making much less use of shape than vertebrates do…

          6 replies 44 retweets 98 likes
        3. David Chapman‏ @Meaningness 27 Mar 2017
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          Replying to @Meaningness

          The form of the universal adversarial perturbation is consistent with this hypothesis. It subtly screws up texture/color info:pic.twitter.com/tv3tRPp1UF

          8 replies 71 retweets 150 likes
        4. 6 more replies
        1. New conversation
        2. Steve Tuck‏ @tuck1s 31 Mar 2017
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          Replying to @Meaningness

          I looked for, but couldn't find, a lazy way to run code to generate those patterns (e.g.Matlab, Jupyter). Do you have one?

          1 reply 0 retweets 0 likes
        3. David Chapman‏ @Meaningness 31 Mar 2017
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          Replying to @tuck1s

          No… you could contact the authors of the paper I guess?

          1 reply 0 retweets 0 likes
        4. 2 more replies
        1. New conversation
        2. Issac Kelly‏ @issackelly 27 Mar 2017
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          Replying to @Meaningness

          @logodaedalus it might be jpeg artifacts but "human invisible" doesn't describe those images. I see a wood grain type pattern

          1 reply 0 retweets 1 like
        3. David Chapman‏ @Meaningness 27 Mar 2017
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          Replying to @issackelly @logodaedalus

          Yes, “invisible” is a bit of an exaggeration—clearest in the “parrot.” 140 chars makes precision difficult smtimes

          0 replies 0 retweets 6 likes
        4. End of conversation
        1. New conversation
        2. Jeff Hobbs‏ @jeffehobbs 27 Mar 2017
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          Replying to @Meaningness

          @fuzzleonard pitch: stenographically encoded hoodies/sportswear to deliberately confuse the upcoming TensorFlow drone swarm

          1 reply 8 retweets 12 likes
        3. This Is My Jam‏ @Marcos_El_Malo 27 Mar 2017
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          Replying to @jeffehobbs @Meaningness

          Drone 1: Is that Jeff Hobbes? Drone 2: Nah, it’s a philodendron walking its pet African elephant.

          0 replies 1 retweet 4 likes
        4. End of conversation
        1. New conversation
        2. Ryan Moulton  🌎 🌍 🌏 🌡 📈‏ @moultano 27 Mar 2017
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          Replying to @Meaningness

          @bklimt cool, but not surprising from earlier rslts. Basic issue is dot products on randomish vectors + Central limit theorem.

          1 reply 0 retweets 2 likes
        3. Ryan Moulton  🌎 🌍 🌏 🌡 📈‏ @moultano 27 Mar 2017
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          Replying to @moultano @Meaningness @bklimt

          causes real data to have small dot products, and adversarial data to have big dot products as dimensionality increases.

          0 replies 0 retweets 2 likes
        4. End of conversation

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