Multitask-Learning and Awesome-Multi-Task-Learning
These are complementary curated resource lists that serve different organizational purposes—one focuses on comprehensive academic works and papers while the other emphasizes practical implementation resources and code repositories—making them useful to reference together depending on whether you need theoretical foundations or working examples.
About Multitask-Learning
mbs0221/Multitask-Learning
Awesome Multitask Learning Resources
A curated collection of multitask learning research materials spanning foundational papers, scholar homepages, and implementations across methodologies including Bayesian approaches, Gaussian processes, sparse/low-rank methods, and reinforcement learning. Covers specialized toolboxes like MALSAR for structural regularization and implementations in MATLAB, R, and Python, alongside contemporary topics like federated and online multitask learning.
About Awesome-Multi-Task-Learning
WeiHongLee/Awesome-Multi-Task-Learning
An up-to-date list of works on Multi-Task Learning
Related comparisons
Scores updated daily from GitHub, PyPI, and npm data. How scores work