filmov
tv
From automatic differentiation to message passing

Показать описание
Automatic differentiation is an elegant technique for converting a computable function expressed as a program into a derivative-computing program with similar time complexity. It does not execute the original program as a black-box, nor does it expand the program into a mathematical formula, both of which would be counter-productive. By generalizing this technique, you can produce efficient algorithms for constraint satisfaction, optimization, and Bayesian inference on models specified as programs. This approach can be broadly described as compiling into a message-passing program.
From automatic differentiation to message passing
From automatic differentiation to message passing
Perturbation Confusion in Forward Automatic Differentiation of Higher-Order Functions
AUTOMATIC DIFFERENTIATION
Automatic Differentiation
Automatic Differentiation in Python and PyTorch (Serverless Machine Learning)
How Autodifferentiation in TensorFlow works
Lecture 5 - Automatic Differentiation Implementation
Automatic Differentiation implementation in C++
Keno Fischer: 'Optics in the wild: reverse mode automatic differentiation in Julia'
Automatic Differentiation.
Use of auto differentiation within the ACTS tookit
PyHEP 2020 Tutorial on Automatic Differentiation
6.1 Optimization Method - Automatic Differentiation
Automatic Differentiation
The Simple Essence of Automatic Differentiation
Alex Wiltschko - Automatic Differentiation, the algorithm behind all deep nets
Automatic differentiation in Ruby
Lecture 13.1: Backpropagation | Automatic Differentiation | ML19
Pytorch tutorial: automatic differentiation
Daniel Brice - Automatic Differentiation in Haskell
Automatic differentiation in scientific programming with jax
Reverse Mode Automatic Differentiation
Perturbation confusion in forward automatic differentiation of higher-order functions (ICFP 2020)
Комментарии