JOSS Papers: Automatic Differentiation

All Papers
3125
 Published Papers
2793
 Active Papers
332
published Published 5 months ago

Catalyst: a Python JIT compiler for auto-differentiable hybrid quantum programs

Python
@josh146
10.21105/joss.06720
published Published over 1 year ago

BridgeStan: Efficient in-memory access to the methods of a Stan model

R C Julia
@roualdes
10.21105/joss.05236
published Published over 1 year ago

QAOA.jl: Toolkit for the Quantum and Mean-Field Approximate Optimization Algorithms

Julia
@timbode
10.21105/joss.05364
published Published almost 2 years ago

SICOPOLIS-AD v2: tangent linear and adjoint modeling framework for ice sheet modeling enabled by automatic differentiation tool Tapenade

C Fortran Python
@Shreyas911
10.21105/joss.04679
published Published over 3 years ago

torchquad: Numerical Integration in Arbitrary Dimensions with PyTorch

Python
@gomezzz
10.21105/joss.03439
published Published over 5 years ago

TaylorSeries.jl: Taylor expansions in one and several variables in Julia

Julia Mathematica
@lbenet
10.21105/joss.01043
published Published about 6 years ago

WEdiff: A Python and C++ package for automatic differentiation

Jupyter Notebook Python C++
@ctdegroot
10.21105/joss.00820
published Published over 6 years ago

Flux: Elegant machine learning with Julia

Julia
@MikeInnes
10.21105/joss.00602
Displaying 8 papers

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ISSN 2475-9066