This repository provides a hyperparameter optimization implementation for machine learning algorithms, accompanying a paper on hyperparameter optimization theory and practice. It includes sample code for regression and classification problems using the Boston-Housing and MNIST datasets, covering algorithms such as random forest, SVM, KNN and artificial neural networks.
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REMBO is a Matlab package implementing Bayesian optimization in high dimensions via random embedding, accompanying the paper "Bayesian Optimization in a Billion Dimensions via Random Embeddings". Configuration files specify parameter settings, and the package supports continuous, discrete, and categorical parameters as well as log scales.
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A curated GitHub list of federated learning system papers, articles, tutorials, slides and projects. It groups entries such as systems from big tech companies, vertical FL, open-source frameworks, edge and mobile systems, optimization, security and privacy, applications and surveys. Papers reviewed and presented at academic conferences are marked with a graduation cap symbol.
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Awesome AutoDL is a curated list of automated deep learning resources and an in-depth analysis, subtitled that neural architecture search is not the end. It collects papers, libraries and benchmarks covering neural architecture search and hyper-parameter optimization.