RAFT contains fundamental widely-used algorithms and primitives for machine learning and information retrieval. The algorithms are CUDA-accelerated and form building blocks for more easily writing high performance applications.

Hyperparameter optimization in Julia.

[ICLR 2020] NAS evaluation is frustratingly hard

PROSAC algorithm in python

Fast Pseudo Random Number Generators for R

NAS Benchmark in "Prioritized Architecture Sampling with Monto-Carlo Tree Search", CVPR2021

A collection of algorithms in Java 8 for the problem of random sampling with a reservoir

Optimal approximate sampling from discrete probability distributions

numpy practice exercise with solution

Efficient random sampling via linear interpolation.

Ray Tracer implementation in C++, Random Sample AA, multi-threading, bvh acceleration, temporal denoising, soft shadows, and runtime comparisons on different CPUs

A nodejs module for randomly select elements.

sub-package of spatstat containing core functionality for data analysis and modelling

A Zig Module for Random Number Distributions

Alpha stable and sub-Gaussian distributions in Julia

Reference implementation of the Affirmative Sampling algorithm by Jérémie Lumbroso and Conrado Martínez (2022). 🍀

Output randomly sampled lines from input stream or file

Make Julia code probabilistic-programming-ready by allowing calls to `rand` to be annotated with traced addresses.

A comprehensive tutorial on Monte Carlo Simulation using Python, demonstrating how random sampling and probabilistic models can be used for various real-world applications, including finance, physics, and engineering.