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UID:pretalx-juliacon2023-RRBDAA@pretalx.com
DTSTART;TZID=EST:20230726T163000
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DESCRIPTION:Automatic differentiation (AD) is great: use gradients to optim
 ize\, sample faster\, or just for fun! But what about coin flips? Agent-ba
 sed models? Nope\, these aren’t differentiable... or are they? Stochasti
 cAD.jl is an open-source research package for AD of stochastic programs\, 
 implementing AD algorithms for handling programs that can contain discrete
  randomness.
DTSTAMP:20260613T000440Z
LOCATION:32-D463 (Star)
SUMMARY:StochasticAD.jl: Differentiating discrete randomness - Frank Schäf
 er\, Gaurav Arya
URL:https://pretalx.com/juliacon2023/talk/RRBDAA/
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