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AdamMarblestone's profile
Adam Marblestone
Adam Marblestone
Adam Marblestone
@AdamMarblestone

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Adam Marblestone

@AdamMarblestone

Technologist, Scientist

adammarblestone.org
Joined February 2009

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    1. Gary Marcus‏ @GaryMarcus 24 Dec 2019
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      Gary Marcus Retweeted Gary Marcus

      if nobody bites on question 3, it will make my next medium post a lot easier to write :)https://twitter.com/GaryMarcus/status/1209640096900812800 …

      Gary Marcus added,

      Gary Marcus @GaryMarcus
      four questions for the deep learning community: 1. How would you define deep learning? 2. What if anything are you committed to? 3. What if anything would you say would not count as deep learning? 4. What is the significance of the word "deep", in your definition? #AIDebate https://twitter.com/raamana_/status/1209266195678973952 …
      11 replies 3 retweets 14 likes
    2. Eli Sennesh‏ @EliSennesh 24 Dec 2019
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      Replying to @GaryMarcus

      3. Anything that doesn't operate over a vector space is not deep learning.

      1 reply 0 retweets 2 likes
    3. Adam Marblestone‏ @AdamMarblestone 25 Dec 2019
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      Replying to @EliSennesh @GaryMarcus

      3. RCN +CHMMs (Vicarious), Bayesian Program Learning (Lake), most probabilistic programming, pure spectral / PCA meths 4 extracting features, most evol. programming/GAs, certain “hybrid systems” like recent Tenenbaum... whatever doesn’t rely on backprop as core engine of learning

      2 replies 1 retweet 6 likes
      Adam Marblestone‏ @AdamMarblestone 25 Dec 2019
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      Replying to @AdamMarblestone @EliSennesh @GaryMarcus

      Deep learning is the use of backprop/auto-diff. to allow gradient based learning of free parameters in large networks of “parametrized functional modules” which may include functions or compositions thereof supporting attention+variables, and may include non-learned modules also.

      3:10 AM - 25 Dec 2019
      • 3 Likes
      • Gary Marcus Eli Sennesh deen-chan
      1 reply 0 retweets 3 likes
        1. New conversation
        2. Adam Marblestone‏ @AdamMarblestone 25 Dec 2019
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          Replying to @AdamMarblestone @EliSennesh @GaryMarcus

          When most or all free parameters in the system are being learned by backprop-enabled gradient descent, that’s deep learning.

          1 reply 0 retweets 2 likes
        3. deen-chan‏ @sir_deenicus 25 Dec 2019
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          Replying to @AdamMarblestone @EliSennesh @GaryMarcus

          Your definitions are good but I prefer clusters induced by definitions to be context dependent. For example, I can have a giant network where each function is the dot product & each edge a weight. It would be differentiable but it can be rewritten as a single linear transform.

          1 reply 0 retweets 1 like
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