Popular GitHub Repositories for Ken Thompson

an implementation of 3D Ken Burns Effect from a Single Image using PyTorch

? Research Framework for Single and Multi-Players ? Multi-Arms Bandits (MAB) Algorithms, implementing all the state-of-the-art algorithms for single-player (UCB, KL-UCB, Thompson...) and multi-player (MusicalChair, MEGA, rhoRand, MCTop/RandTopM etc).. Available on PyPI: https://pypi.org/project/SMPyBandits/ and documentation on

Online Deep Learning: Learning Deep Neural Networks on the Fly / Non-linear Contextual Bandit Algorithm (ONN_THS)

:bust_in_silhouette: Multi-Armed Bandit Algorithms Library (MAB) :cop:

Learning Multi-Armed Bandits by Examples. Currently covering MAB, UCB, Boltzmann Exploration, Thompson Sampling, Contextual MAB, Deep MAB.

In This repository I made some simple to complex methods in machine learning. Here I try to build template style code.

KEN: Relational Data Embeddings

Code for the NeurIPS 2020 paper: "Federated Bayesian Optimization via Thompson Sampling"

Bayesian Optimization for Categorical and Continuous Inputs

Compile and evaluate regular expressions using Non-deterministic Finite Automata (NFAs).

An improved version of Turbo algorithm for the Black-box optimization competition organized by NeurIPS 2020

A Reinforcement learning based program to dynamic display of ad-banners based on the click event by customers. This uses Thompson sampling technique hence ensuring better click through rate.

Adaptive Advanced Tree Search function designed for OpenWeb-UI

Explore key RL algorithms with detailed explanations and fully commented Python code implementations

Code for the paper "Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning"

A Comparative Evaluation of Active Learning Methods in Deep Recommendation

KEn (Ukrainian, English) - is an NLP-powered network toolkit (Web service with API) for contextual and semantic analysis with document taxonomy building feature | KEn - Мережевий засіб виокремлення термінів з природномовних текстів (Українською та Англійською).

Code for the paper "Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs", AAAI Conference on Artificial Intelligence 2024

An implementation of NEAT based on Ken Stanley's implementation from 2002. This project is designed to teach itself how to play Snake and use Python for data analytics.

Solving n-armed-bandit problems using different policies to find the path with the least regret. The policies used in this project were policy gradient and Thompson sampling. All the environments and agents are implemented with the aid of the Amalearn library. This project was carried out as part of the Reinforcement learning master course offered at the University of Tehran under the supervision of Prof Nili.