Julia bindings for the Enzyme automatic differentiator
-
Updated
Jun 1, 2024 - Julia
Julia bindings for the Enzyme automatic differentiator
This repo hosts the notes and tutorials related to natural language processing in the format of blogging.
A numerical and automatic mathematical library in C++ for scientific and graphical applications.
An interface to various automatic differentiation backends in Julia.
PennyLane is a cross-platform Python library for quantum computing, quantum machine learning, and quantum chemistry. Train a quantum computer the same way as a neural network.
AD-backend agnostic system defining custom forward and reverse mode rules. This is the light weight core to allow you to define rules for your functions in your packages, without depending on any particular AD system.
Tensor library for machine learning
Taylor-mode automatic differentiation for higher-order derivatives
A fast and flexible implementation of Rigid Body Dynamics algorithms and their analytical derivatives
The Thomson scattering diagnostic offers a method by which to infer plasma parameters such as n_e, T_e. This codebase uses the form factor equations to estimate those plasma parameters by fitting to observed Thomson scattering spectra.
forward and reverse mode automatic differentiation primitives for Julia Base + StdLibs
Introductions to key concepts in quantum programming, as well as tutorials and implementations from cutting-edge quantum computing research.
High-performance automatic differentiation of LLVM and MLIR.
R package for score matching by automatic differentiation
Optimal transport tools implemented with the JAX framework, to get differentiable, parallel and jit-able computations.
A JIT compiler for hybrid quantum programs in PennyLane
The Stan Math Library is a C++ template library for automatic differentiation of any order using forward, reverse, and mixed modes. It includes a range of built-in functions for probabilistic modeling, linear algebra, and equation solving.
A C++ implementation of an OFDFT based molecular force field model.
Tensor network based quantum software framework for the NISQ era
Math on (Hyper-Dual) Tensors with Trailing Axes
Add a description, image, and links to the automatic-differentiation topic page so that developers can more easily learn about it.
To associate your repository with the automatic-differentiation topic, visit your repo's landing page and select "manage topics."