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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. Tarin Ziyaee‏ @tarinziyaee 8 Dec 2018
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      Replying to @dileeplearning @tyrell_turing and

      Exactly.

      1 reply 0 retweets 0 likes
    2. Blake Richards‏ @tyrell_turing 9 Dec 2018
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      Replying to @tarinziyaee @dileeplearning and

      With all due respect, guys, you're not understanding my point. Yes, the statistics of the environment are key to understanding the computations in the brain, but as you note, we probably learn a lot of that.

      1 reply 0 retweets 0 likes
    3. Blake Richards‏ @tyrell_turing 9 Dec 2018
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      Replying to @tyrell_turing @tarinziyaee and

      So, the point I return to is: we should be trying to understand the learning algorithms in the brain, not simply cataloguing relationships between activity patterns and stimuli/behavior. That would provide more information for AI.

      1 reply 0 retweets 3 likes
    4. Tarin Ziyaee‏ @tarinziyaee 9 Dec 2018
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      Replying to @tyrell_turing @dileeplearning and

      @tyrell_turing Sure, the learning-algorithms is the ultimate goal. However the “representations to stimuli” as you say, can help us understand the algorithms being run. I’m not sure they can be cleanly decoupled, esp in the beginning. I’m also not sure why this is controversial.

      1 reply 0 retweets 2 likes
    5. Dileep George‏ @dileeplearning 9 Dec 2018
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      Replying to @tarinziyaee @tyrell_turing and

      I agree. It looks to me that Richard is looking to neuroscientists and saying 'just tell us how brain implements back-prop and we'll take it form there, thank you very much' :).

      1 reply 0 retweets 2 likes
    6. Blake Richards‏ @tyrell_turing 9 Dec 2018
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      Replying to @dileeplearning @tarinziyaee and

      No, you’re not getting my point... I’m not saying representations or learning can be fully decoupled, nor telling neuroscientists (of which I am one) just tell us how the brain does backprop.

      2 replies 0 retweets 2 likes
    7. Blake Richards‏ @tyrell_turing 9 Dec 2018
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      Replying to @tyrell_turing @dileeplearning and

      I’m saying: neuroscience needs to start thinking in terms of cost functions and optimization, rather than tuning curves and Hebbian plasticity.

      1 reply 0 retweets 7 likes
    8. Adam Marblestone‏ @AdamMarblestone 9 Dec 2018
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      Replying to @tyrell_turing @dileeplearning and

      These both seem like good research programs at our present state of understanding. 1) If the signals in the world have a particular (1/14)

      1 reply 0 retweets 3 likes
    9. Adam Marblestone‏ @AdamMarblestone 9 Dec 2018
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      Replying to @AdamMarblestone @tyrell_turing and

      structure, it may be possible to use probabilistic graphical models, with pre-built formats reflecting that structure, to learn to (2/14)

      1 reply 0 retweets 3 likes
    10. Adam Marblestone‏ @AdamMarblestone 9 Dec 2018
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      Replying to @AdamMarblestone @tyrell_turing and

      model those world signals, and it may be possible to so more efficiently than one could by backprop from even a well-chosen cost (3/14)

      2 replies 0 retweets 5 likes
      Adam Marblestone‏ @AdamMarblestone 9 Dec 2018
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      Replying to @AdamMarblestone @tyrell_turing and

      function. It would be very interesting if the brain did something like that and if so, it would be reflected in the (4/14)

      11:58 AM - 9 Dec 2018
      • 4 Likes
      • Thomas Mesnard Dileep George KordingLab 👨‍💻🧠∇🔬📈,🏋️‍♂️⛷️🏂🛹🕺⛰️☕🦖 Blake Richards
      1 reply 0 retweets 4 likes
        1. New conversation
        2. Adam Marblestone‏ @AdamMarblestone 9 Dec 2018
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          Replying to @AdamMarblestone @tyrell_turing and

          micro-circuitry. 2) Figuring out a) if and b) how the brain implements a learning algorithm as powerful and general as (5/14)

          1 reply 0 retweets 4 likes
        3. Adam Marblestone‏ @AdamMarblestone 9 Dec 2018
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          Replying to @AdamMarblestone @tyrell_turing and

          backprop/gradient descent, and if so what cost functions it optimizes, is very important regardless of whether a) turns out in the (6/14)

          1 reply 0 retweets 5 likes
        4. 12 more replies

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