awesome-multi-task-learning
The repository offers a detailed collection of datasets, codes, and scholarly articles related to Multi-Task Learning from a machine learning perspective. It covers extensive topics such as benchmarks in domains like computer vision, NLP, robotics, and recommendation systems. The project also delves into various architectures including hard and soft parameter sharing, and explores optimization techniques essential for advancing MTL. Open to contributions, this platform aims to support collaboration and improve multi-tasking methodologies, facilitating researchers and industry experts in addressing complex challenges effectively.
