A simple automatic differentiation library in Rust.
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Updated
Mar 3, 2023 - Rust
A simple automatic differentiation library in Rust.
A prototypical, experimental framework to define and execute computational graph to train neural networks.
A simple automatic differentiation library written in Go
A pedagogical implementation of Automatic Differation on multi-dimensional tensors.
AD with Enzyme through Lulesh.
Tiny (header-only) Automatic Differentiation library for C++
A pure-Python, PyTorch-like automatic differentiation library for education.
Like torch, but rather than seeing the light, you get burnt.
Gograd is a small automatic differentiation framework written in Go.
Mercury library for automatic differentiation
A simple forward mode automatic differentiation package
PyTorch Autodiff DFT-D4 Implementation.
A Julia package for differentiating through expectations with Monte-Carlo estimates
Julia interface to the Generalised Truncated Power Series Algebra (GTPSA) library in MAD
python implementation of automatic differentiation for functions written in vanilla python or numpy
This library provides expression trees for representation of geometric expressions and automatic differentiation of these expressions. This enables to write down geometric expressions at the position level, and automatically compute Jacobians and higher order derivatives efficiently and without loss of precision. The library is built upon the KD…
Solving Schrodinger's Equation with a Neural Network using numerical integration and autograd. Check https://arxiv.org/abs/2104.04795
Toy Automatic Differentiation Library
Domain Specific Language to perform Automatic Differentiation on Higher Order functions.
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