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avibryant's profile
Avi Bryant
Avi Bryant
Avi Bryant
@avibryant

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Avi Bryant

@avibryant

Happiest when working on Random Forests from random beaches.

Galiano Island
avibryant.com
Joined November 2006

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    1. Avi Bryant‏ @avibryant May 20

      Re last two retweets: IMO the important advance in DL is not any specific architecture or learning method but the use of autodiff to compose many modules and learn them end to end. Which makes arguments like this seem like a red herring.

      2 replies 0 retweets 10 likes
    2. Vitaly Gordon‏ @vitalygordon May 20
      Replying to @avibryant

      Didn’t model composition (or stacking) exist before autodiff?

      1 reply 0 retweets 0 likes
      Avi Bryant‏ @avibryant May 20
      Replying to @vitalygordon

      The hard part without autodiff is jointly learning the stacked models. Not to say you couldn't derive a procedure to do so but it would be extra work each time.

      6:47 PM - 20 May 2018
      • 1 Like
      • Andrew Walkingshaw
      1 reply 0 retweets 1 like
        1. New conversation
        2. Vitaly Gordon‏ @vitalygordon May 20
          Replying to @avibryant

          Jointly as in one pass as opposed to two separate training runs?

          1 reply 0 retweets 0 likes
        3. Byron Ellis‏ @fdaapproved May 20
          Replying to @vitalygordon @avibryant

          Mmm, I think of it more in terms of a compiler optimization like loop unrolling.

          1 reply 0 retweets 0 likes
        4. Avi Bryant‏ @avibryant May 20
          Replying to @fdaapproved @vitalygordon

          Disclaimer, I'm not an expert here. But what I meant was: previously if I stacked two models, I'd train one against some intermediate loss function I selected, then uses its trained outputs as inputs into another (with some other, final loss function).

          1 reply 0 retweets 1 like
        5. Avi Bryant‏ @avibryant May 20
          Replying to @avibryant @fdaapproved @vitalygordon

          What autodiff makes easy is training the stack of model1 -> model2 with respect to the same, final loss function, which will lead to a differently (and better) trained model1 than if you trained them sequentially.

          3 replies 0 retweets 1 like
        6. Avi Bryant‏ @avibryant May 20
          Replying to @avibryant @fdaapproved @vitalygordon

          The benefits of this are probably easiest to see with something like image recognition - "how do we optimize our edge detection? I dunno, however is going to make it easier to tell cats from dogs 7 layers up the stack. Let the gradients sort that out."

          0 replies 0 retweets 1 like
        7. End of conversation

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