Burn is a new comprehensive dynamic Deep Learning Framework built using Rust with extreme flexibility, compute efficiency and portability as its primary goals.
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Updated
Jun 1, 2024 - Rust
Burn is a new comprehensive dynamic Deep Learning Framework built using Rust with extreme flexibility, compute efficiency and portability as its primary goals.
An interface to various automatic differentiation backends in Julia.
A JIT compiler for hybrid quantum programs in PennyLane
Reinforcement Learning for AD algorithm search with AlphaZero
Small autodiff lib and a simple working feedforward neural net in Haskell on top of it, from scratch, zero-deps.
automatic differentiation made easier for C++
Automatic differentiation of implicit functions
An automatic differentiation library written in C++ and CUDA
Repository for automatic differentiation backend types
[Experimental] Graph and Tensor Abstraction for Deep Learning all in Common Lisp
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.
Utilities for testing custom AD primitives.
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