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Awesome-GANs is a curated resource list and research repository focused on the development and evaluation of generative adversarial networks. It serves as a structured index for academic literature and open-source implementations dedicated to the creation of synthetic data generators. The project provides a framework for training competing neural networks to produce outputs that mimic the statistical properties of original datasets. It emphasizes the use of configuration-driven pipelines to manage model hyperparameters and dataset paths, facilitating reproducible research workflows and standa
A curated list of gradient boosting research papers with implementations.
A curated list of Monte Carlo tree search papers with implementations.
A curated list of data mining papers about fraud detection.
Personalized federated learning codebase for research
The main features of microsoft/personalizedfl are: Machine Learning Research.
Open-source alternatives to microsoft/personalizedfl include: kozistr/awesome-gans — Awesome-GANs is a curated resource list and research repository focused on the development and evaluation of… benedekrozemberczki/awesome-fraud-detection-papers — A curated list of data mining papers about fraud detection. benedekrozemberczki/awesome-gradient-boosting-papers — A curated list of gradient boosting research papers with implementations. benedekrozemberczki/awesome-monte-carlo-tree-search-papers — A curated list of Monte Carlo tree search papers with implementations. jindongwang/activityrecognition — Resources about activity recognition-行为识别资料. jindongwang/machinelearning — 一些关于机器学习的学习资料与研究介绍.