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Reverse-mode automatic differentiation with delimited continuations

License: BSD 3-Clause "New" or "Revised" License

Haskell 100.00%
adjoint automatic-differentiation backpropagation gradient machine-learning optimization

ad-delcont's Introduction

hi !

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ad-delcont's Issues

Radical redesign

Currently, the core abstraction of this library is ContT r (ST s) a. This loosely follows from the design of Wang, ICFP'19 since we need both the continuation monad and mutable references.

However, since the results are numerically incorrect, (see rad (\x -> x + x) 2 for example), a redesign is in order.

  • Formulate a minimal example (a "straight-line" program using ContT combinators directly, e.g. finding the sensitivity of g . f where g and f are fixed), to elucidate the interplay of continuations and mutable references.

Exponential complexity?

Simple example with fibonacci numbers:

n :: Int
n = 50

fibN :: Num a => a -> a
fibN x0 = fibs !! n
  where 
    fibs = 0 : x0 : zipWith (+) fibs (tail fibs)

fibInt :: Integer  
fibInt = fibN 1

fibAD :: (Integer, Integer)
fibAD = rad1g 0 1 fibN 1

fibInt evaluates instantly. fibAD takes a long time and throws stack overflow exception.

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