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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. Adam J Calhoun‏Verified account @neuroecology 12 Mar 2018
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      Adam J Calhoun Retweeted {dr. arsonist}, PhD

      Been thinking about this a lot. Possibilities ~ 1. Bio-plausible learning is very architecture-dependent? 2. Similarly, too much is hardwired by evolution? 3. We have the wrong learning rules 4. Missing large chunks of biologically learning algorithm. STDP is tail of elephanthttps://twitter.com/butterflyarson/status/970693942218981376 …

      Adam J Calhoun added,

      {dr. arsonist}, PhD @setofarsonist
      This was the strangest moment of COSYNE. Deepmind rep gets on stage visibly frustrated, says networks with biologically plausible credit assignment (non-backprop) perform terribly. Indeed seems like a challenge issued to the community. https://twitter.com/neurosutras/status/970365749129859077 …
      Show this thread
      8 replies 13 retweets 65 likes
      Show this thread
    2. Alex Williams 🌹‏ @ItsNeuronal 12 Mar 2018
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      Replying to @neuroecology

      I mean... we don't even understand when and why good-old backprop "works". In fact it often doesn't until you add batchnorm and other tricks.

      2 replies 0 retweets 4 likes
    3. Adam J Calhoun‏Verified account @neuroecology 12 Mar 2018
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      Replying to @ItsNeuronal

      But it does work sometimes! I think Tim's point was that their biological rules, uh, basically don't.

      2 replies 0 retweets 2 likes
    4. Blake Richards‏ @tyrell_turing 13 Mar 2018
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      Replying to @neuroecology @ItsNeuronal

      1/ If we're talking about Tim Lillicrap's presentation here, I can clarify: Tim and I (and a few other ppl) got excited recently about Bengio's target propagation proposals, bc they seem to solve some aspects of biological infeasibility from backprop, but allow gradient descent.

      2 replies 0 retweets 3 likes
    5. Blake Richards‏ @tyrell_turing 13 Mar 2018
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      Replying to @tyrell_turing @neuroecology @ItsNeuronal

      2/ Long-story short: targetprop ain't gonna cut it in its current form. It is too restrictive, and doesn't follow true gradients across batch. We need something else... But, it was a reasonable place to explore!

      1 reply 0 retweets 5 likes
      Adam Marblestone‏ @AdamMarblestone 13 Mar 2018
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      Replying to @tyrell_turing @neuroecology @ItsNeuronal

      I hope y'all are trying all the others too... from the recent paper it wasn't clear whether feedback alignment, direct feedback alignment, equilibrium propagation, local representation alignment, synthetic gradients or others would fare equally, better or worse on ImageNet...

      9:21 AM - 13 Mar 2018
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        2. Blake Richards‏ @tyrell_turing 13 Mar 2018
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          Replying to @AdamMarblestone @neuroecology @ItsNeuronal

          Yes, indeed, working on that. Only difference target prop was examined in the work Tim presented at Cosyne. More to come in future.

          1 reply 0 retweets 0 likes
        3. Adam Marblestone‏ @AdamMarblestone 13 Mar 2018
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          Replying to @tyrell_turing @neuroecology @ItsNeuronal

          That's great! In that case, I'm far from saying biologically plausible backprop won't work on hard tasks. And anyway ImageNet is obviously the wrong task although I understand why you chose it. I want to see it on some unsupervised prediction method like https://arxiv.org/abs/1605.08104 

          1 reply 0 retweets 2 likes
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