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2023 up-to-date list of DATASETS, CODEBASES and PAPERS on Multi-Task Learning (MTL), from Machine Learning perspective.

Home Page: https://github.com/Manchery/awesome-multi-task-learning

multi-task-learning machine-learning deep-learning computer-vision loss-strategy multi-task-optimization multi-task-architecture deep-neural-networks transfer-learning multi-domain-learning

awesome-multi-task-learning's Introduction

Hi 👋, I'm Jialong Wu (Manchery)

An active stargazer from China

🔭 I'm currently a PhD student (from 2022 Fall) in School of Software, Tsinghua University, advised by Prof. Mingsheng Long.
📘 I've received my Bachelor's degree in Software Engineering and a second Bachelor's degree in Pure and Applied Mathematics, from Tsinghua University.
✨ I'm interested in deep learning, machine learning and mathematics.
💻 I used to enjoy competitive programming and compete in Olympiad in Informatics (OI).
📝 My favorite motto is Sidere mens eadem mutato (Latin, 繁星纵变，智慧永恒 in Chinese).
😄 I'm an idealist and a perfectionist. I'm also an Apple fan and a Pokémon fan.
❤️ I'm lucky to have my love LYW in my life.

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New MTL papers from our group

Hi there,

We're a MLSys group working on multi-task learning at the University of Massachusetts Amherst. Recently we have some new works completed. Would you mind taking a look at these papers and adding them to your repo list? Thank you!

Rethinking hard-parameter sharing in multi-domain learning, accepted by ICME 2022 (acceptance rate 29%).
AutoMTL: A Programming Framework for Automated Multi-Task Learning. A NAS-based MTL infrastructure.
A Tree-Structured Multi-Task Model Recommender, accepted by AutoML-Conf 2022 (acceptance rate 19.2%).

Best,
Lijun Zhang

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