Guillaume Dalle
Sessions
10-02
08:50
10min
Opening
Guillaume Dalle
-
General
Robert Faure Amphitheater
10-03
11:30
30min
Leveraging Sparsity to Accelerate Automatic Differentiation
Adrian Hill, Guillaume Dalle
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 calculate, their inherent sparsity can be leveraged to dramatically accelerate Automatic Differentiation (AD). By building on top of DifferentiationInterface.jl, we are able to bring Automatic Sparse Differentiation to all major Julia AD backends, including ForwardDiff and Enzyme.
Error, derivatives, stability
Jean-Baptiste Say Amphitheater
10-03
12:40
10min
Closing
Guillaume Dalle
-
General
Robert Faure Amphitheater