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UID:pretalx-juliacon-local-paris-2025-KTGPQE@pretalx.com
DTSTART;TZID=CET:20251003T113000
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DESCRIPTION:Jacobians and Hessians play vital roles in scientific computing
  and machine learning\, from optimization to probabilistic modeling. While
  these matrices are often considered too computationally expensive to calc
 ulate\, their inherent sparsity can be leveraged to dramatically accelerat
 e Automatic Differentiation (AD). By building on top of DifferentiationInt
 erface.jl\, we are able to bring Automatic Sparse Differentiation to all m
 ajor Julia AD backends\, including ForwardDiff and Enzyme.
DTSTAMP:20260312T142109Z
LOCATION:Robert Faure Amphitheater
SUMMARY:Leveraging Sparsity to Accelerate Automatic Differentiation - Guill
 aume Dalle\, Adrian Hill
URL:https://pretalx.com/juliacon-local-paris-2025/talk/KTGPQE/
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