[HN Gopher] Imaginary Numbers Protect AI from Real Threats
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       Imaginary Numbers Protect AI from Real Threats
        
       Author : sizzle
       Score  : 14 points
       Date   : 2021-09-08 18:49 UTC (4 hours ago)
        
 (HTM) web link (pratt.duke.edu)
 (TXT) w3m dump (pratt.duke.edu)
        
       | zardo wrote:
       | > By including just two complex-valued layers among hundreds if
       | not thousands of training iterations
       | 
       | Confused by the comparison of layers to training iterations. How
       | many layers are in the model?
        
         | im3w1l wrote:
         | Yeah the whole article seems written by someone with a very
         | poor understanding.
        
           | owlbite wrote:
           | The actual paper is here:
           | http://proceedings.mlr.press/v139/yeats21a/yeats21a.pdf
           | 
           | Summary seems to be that ambiguity attacks can be resisted by
           | regularization such that basin of attraction for each class
           | is larger and it is harder to subtly nudge inference form
           | class A to class B. The complex representation makes the
           | regularization better.
           | 
           | Mechanism for this is essentially that by encoding real to
           | complex as y = { sin(x), cos(x) } and going back again by
           | taking the absolute value, the fallout is that a descent
           | direction constraint manifests such that {the step in the
           | activation descent direction}*2 + {step in regularization
           | space}*2 = 1, so large steps are either modifying the
           | activation or are modifying the regularization, not both, so
           | the result is a more robust training direction.
        
         | Frost1x wrote:
         | Clearly, it's n + 2*i^4 layerations.
        
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       (page generated 2021-09-08 23:01 UTC)