simple autodiff library

Coloring algorithms for sparse Jacobian and Hessian matrices

[Experimental] Graph and Tensor Abstraction for Deep Learning all in Common Lisp

Burn is a new comprehensive dynamic Deep Learning Framework built using Rust with extreme flexibility, compute efficiency and portability as its primary goals.

A JIT compiler for hybrid quantum programs in PennyLane

Fast operator-overloading Jacobian & Hessian sparsity detection.

An interface to various automatic differentiation backends in Julia.

A generic library for linear and non-linear Gaussian smoothing problems. The code leverages JAX and implements several linearization algorithms, both in a sequential and parallel fashion, as well as efficient gradient rules for computing gradients of required quantities (such as the pseudo-loglikelihood of the system).

Lightweight Python package for automatic differentiation

A modular C++17 framework for automatic differentiation

Transparent calculations with uncertainties on the quantities involved (aka "error propagation"); calculation of derivatives.

Repository for automatic differentiation backend types

Automatic differentiation of implicit functions

Solve ODEs fast, with support for PyMC

Mathematics.NET is a C# class library that provides tools for solving mathematical problems.

Benchmarks for decision-focused learning

A Julia package for differentiating through expectations with Monte-Carlo estimates

Differentiable wrapper for FrankWolfe.jl convex optimization routines

Version 2.0 of Kineverse, a framework for modeling kinematics for robotic manipulation and control.