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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. Thomas Miconi‏ @ThomasMiconi 30 Aug 2019
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      Replying to @kendmil @tyrell_turing and

      Original REINFORCE does not require backprop to do RL. The gradient term (which is just "how to make X more likely in the future") can be approximated by a Hebbian-like term, and the reward provides the direction of the update. See p.8 In Williams 1992.

      2 replies 0 retweets 2 likes
    2. Thomas Miconi‏ @ThomasMiconi 30 Aug 2019
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      Replying to @ThomasMiconi @kendmil and

      At the other extreme, we can use backprop to design an overall autonomous learning system, so that reward-modulated Hebbian plasticity is carefully tuned to provide "good" learning. That's the "backpropamine" framework.https://openreview.net/forum?id=r1lrAiA5Ym …

      1 reply 0 retweets 5 likes
    3. Blake Richards‏ @tyrell_turing 30 Aug 2019
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      Replying to @ThomasMiconi @kendmil and

      I know it, and I think it's a cool ide. 🙂 But, I would argue that your Hebbian algorithm is still going to underperform against an algorithm with more explicit guarantees of improvement.

      1 reply 0 retweets 2 likes
    4. Ken Miller‏ @kendmil 30 Aug 2019
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      Replying to @tyrell_turing @ThomasMiconi and

      But isn't the reward modulation a form of credit assignment? Plasticity is being directed to improve task performance as measured by reward? Leaving aside questions of efficiency, seems like this is an example of credit assignment, not an argument against it? But correct me 1/

      3 replies 0 retweets 3 likes
    5. Ken Miller‏ @kendmil 30 Aug 2019
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      Replying to @kendmil @tyrell_turing and

      if I'm misunderstanding.

      1 reply 0 retweets 1 like
    6. Tony Zador‏ @TonyZador 30 Aug 2019
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      Replying to @kendmil @tyrell_turing and

      i'm also somewhat confused by what is meant by "credit assignment." I assume it is something more sophisticated than eg Hebbian rule gated by a global reward signal...something trickier, like propagating the gradient of an error. But comparing those is a bit of a category error.

      1 reply 0 retweets 2 likes
    7. Blake Richards‏ @tyrell_turing 30 Aug 2019
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      Replying to @TonyZador @kendmil and

      No, that is credit assignment! Just inneficient credit assignment. Really, we all have to agree that there is some credit assignment. The disagreement, I think, is about how high D and efficient it needs to be.

      1 reply 0 retweets 9 likes
    8. Tony Zador‏ @TonyZador 30 Aug 2019
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      Replying to @tyrell_turing @kendmil and

      Indeed, the question is how fancy (ie high D) the credit assignment algo is. I dont think it's so controversial to imagine that something like the Rescorla-Wagner rule is implemented...

      2 replies 0 retweets 3 likes
    9. Ken Miller‏ @kendmil 31 Aug 2019
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      Replying to @TonyZador @tyrell_turing and

      It's not crazy at all, but it's also not so crazy to think that at least some forms of sensory cortical plasticity may be independent of reward or other credit assignment signals, eg critical period (perhaps not adult) V1 plasticity or the Li and Dicarlo IT results 1/

      1 reply 0 retweets 3 likes
    10. Ken Miller‏ @kendmil 31 Aug 2019
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      Replying to @kendmil @TonyZador and

      In which case, it would raise the question how that becomes a good (evolutionarily competitive) algorithm in context of brain when it's not competitive in current ML

      2 replies 0 retweets 1 like
      Adam Marblestone‏ @AdamMarblestone 31 Aug 2019
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      Replying to @kendmil @TonyZador and

      Though consistent with local STDP, I wonder if the Li and DiCarlo you mention could also be consistent with multilayer gradient descent on an internal (prediction?) error from a yet-higher area?https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3307055/ …

      4:47 AM - 31 Aug 2019
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      • Pine σ² Blake Richards
      0 replies 0 retweets 2 likes

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