Topic: autodiff Goto Github
Some thing interesting about autodiff
Some thing interesting about autodiff
autodiff,Сustom torch style machine learning framework with automatic differentiation implemented on numpy, allows build GANs, VAEs, etc.
User: akirusprod
autodiff,A .NET library that provides fast, accurate and automatic differentiation (computes derivative / gradient) of mathematical functions.
User: alexshtf
autodiff,automatic differentiation made easier for C++
Organization: autodiff
Home Page: https://autodiff.github.io
autodiff,NotImplementedError: VJP of gammainc wrt argnum 0 not defined
User: camdavidsonpilon
autodiff,An experimental deep learning framework for Nim based on a differentiable array programming language
User: can-lehmann
autodiff,A new lightweight auto-differentation library that directly builds on numpy. Used as a homework for CMU 11785/11685/11485.
Organization: cmu-ideel
autodiff,Deep learning in Rust, with shape checked tensors and neural networks
User: coreylowman
autodiff,DiffSharp: Differentiable Functional Programming
Organization: diffsharp
Home Page: http://diffsharp.github.io
autodiff,Assignment 1: automatic differentiation
Organization: dlsys-course
autodiff,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).
Organization: eea-sensors
autodiff,A C++ implementation of the scalar-valued autograd engine micrograd
User: elinx
autodiff,An interface to various automatic differentiation backends in Julia.
User: gdalle
Home Page: https://gdalle.github.io/DifferentiationInterface.jl/DifferentiationInterface
autodiff,Automatic differentiation of implicit functions
User: gdalle
Home Page: https://gdalle.github.io/ImplicitDifferentiation.jl/
autodiff,Auto differentiation over linear algebras (a Zygote extension)
User: giggleliu
autodiff,Source-to-Source Debuggable Derivatives in Pure Python
Organization: google
autodiff,[Experimental] Graph and Tensor Abstraction for Deep Learning all in Common Lisp
User: hikettei
Home Page: https://hikettei.github.io/cl-waffe2/
autodiff,A operator overloading, tape-based, reverse-mode AD
Organization: invenia
autodiff,FastAD is a C++ implementation of automatic differentiation both forward and reverse mode.
User: jamesyang007
autodiff,JAX-DIPS is a differentiable interfacial PDE solver.
Organization: jax-dips
autodiff,Unitful and differentiable gravitational N-body simulation code in Julia
Organization: juliaastrosim
autodiff,Utilities for testing custom AD primitives.
Organization: juliadiff
autodiff,Library for solving quantum optimal control problems in Julia. Currently offers support for GRAPE and dCRAB algorithms using piecewise constant controls.
Organization: juliaquantumcontrol
autodiff,A lightweight deep learning framework made with ❤️
User: karanchahal
autodiff,A probabilistic programming language that combines automatic differentiation, automatic marginalization, and automatic conditioning within Monte Carlo methods.
User: lawmurray
Home Page: https://birch.sh
autodiff,Betty: an automatic differentiation library for generalized meta-learning and multilevel optimization
Organization: leopard-ai
Home Page: https://leopard-ai.github.io/betty/
autodiff,Transparent calculations with uncertainties on the quantities involved (aka "error propagation"); calculation of derivatives.
Organization: lmfit
Home Page: http://uncertainties-python-package.readthedocs.io/
autodiff,Differentiable optical models as parameterised neural networks in Jax using Zodiax
User: louisdesdoigts
Home Page: https://louisdesdoigts.github.io/dLux/
autodiff,Autodifferentiation package in Rust.
User: maciejkula
autodiff,library of C++ functions that support applications of Stan in Pharmacometrics
Organization: metrumresearchgroup
autodiff,Geometry processing utilities compatible with jax for autodifferentiation.
Organization: ml-for-gp
autodiff,Forward mode automatic differentiation for Fortran
User: nicholaswogan
autodiff,Code for the paper "Algorithmic Differentiation for Automatized Modelling of Machine Learned Force Fields"
User: niklasschmitz
Home Page: https://pubs.acs.org/doi/10.1021/acs.jpclett.2c02632
autodiff,Algorithmic differentiation with hyper-dual numbers in C++ and Python
User: oberbichler
autodiff,A JIT compiler for hybrid quantum programs in PennyLane
Organization: pennylaneai
Home Page: https://docs.pennylane.ai/projects/catalyst
autodiff,Automatic differentiation + optimization
User: pierreablin
autodiff,Solve ODEs fast, with support for PyMC
Organization: pymc-devs
Home Page: https://sunode.readthedocs.io
autodiff,A toy deep learning framework implemented in pure Numpy from scratch. Aka homemade PyTorch lol.
User: renovamen
Home Page: http://flint.vercel.app
autodiff,Drop-in autodiff for NumPy.
User: rsokl
Home Page: https://mygrad.readthedocs.io
autodiff,Scala embedded universal probabilistic programming language
Organization: scala-infer
Home Page: https://scala-infer.github.io
autodiff,Repository for SciML AD backend types
Organization: sciml
Home Page: https://sciml.github.io/ADTypes.jl/
autodiff,Minimal deep learning library written from scratch in Python, using NumPy/CuPy.
User: sradc
autodiff,Tensors and dynamic Neural Networks in Mojo
User: tillife
autodiff,Burn is a new comprehensive dynamic Deep Learning Framework built using Rust with extreme flexibility, compute efficiency and portability as its primary goals.
Organization: tracel-ai
Home Page: https://burn.dev
autodiff,A concise C++17 implementation of automatic differentiation (operator overloading)
User: vincent-picaud
autodiff,Fazang is a Fortran library for reverse-mode automatic differentiation, inspired by Stan/Math library.
User: yizhang-yiz
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