Displaying 1 to 7 from 7 results

tangent - Source-to-Source Debuggable Derivatives in Pure Python

  •    Python

Tangent is a new, free, and open-source Python library for automatic differentiation.Existing libraries implement automatic differentiation by tracing a program's execution (at runtime, like PyTorch) or by staging out a dynamic data-flow graph and then differentiating the graph (ahead-of-time, like TensorFlow). In contrast, Tangent performs ahead-of-time autodiff on the Python source code itself, and produces Python source code as its output. Tangent fills a unique location in the space of machine learning tools.

Theano - Optimizing compiler for evaluating mathematical expressions on CPUs and GPUs.

  •    Python

Theano is a Python library that allows you to define, optimize, and efficiently evaluate mathematical expressions involving multi-dimensional arrays. It is built on top of NumPy. Its features include tight integration with NumPy, transparent use of a GPU, dynamic C code generation and lot more.

assignment1 - Assignment 1: automatic differentiation

  •    Python

In this assignment, we would implement reverse-mode auto-diff. Our code should be able to construct simple expressions, e.g. y=x1*x2+x1, and evaluate their outputs as well as their gradients (or adjoints), e.g. y, dy/dx1 and dy/dx2.

Nabla.jl

  •    Julia

Nabla.jl is a reverse-mode automatic differentiation package targetting machine learning use cases. As such, we have (for example) prioritised support for linear algebra optimisations and higher-order functions over the ability to take higher-order derivatives (Nabla currently only supports first-order derivatives). The latest docs, and code in the examples folder, best indicate how to use the package. Given the early stage of development, we anticipate a number of bugs and performance issues. If you encounter any of these or have any particular feature requests, please raise an issue and let us know.




wyrm - Autodifferentiation package in Rust.

  •    Rust

A reverse mode, define-by-run, low-overhead autodifferentiation library. Performs backpropagation through arbitrary, define-by-run computation graphs, emphasizing low overhead estimation of sparse, small models on the CPU.

autodiff - A

  •    CSharp

A library that provides moderately fast, accurate, and automatic differentiation (computes derivative / gradient) of mathematical functions. AutoDiff provides a simple and intuitive API for computing function gradients/derivatives along with a fast algorithm for performing the computation. Such computations are mainly useful in iterative numerical optimization scenarios.

Torsten - library of C++ functions that support applications of Stan in Pharmacometrics

  •    C++

This library provides Stan language functions that calculate amounts in each compartment, given an event schedule and an ODE system. We are working with Stan development team to create a system to add and share Stan packages. In the mean time, the current repo contains forked version of Stan with Torsten. The latest version of Torsten (v0.87) is compatible with Stan v2.19.1. Torsten is agnostic to which Stan interface you use. Here we provide command line and R interfaces.