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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
A PyTorch Library for Multi-Task Learning
| Date | Stars |
|---|---|
| 2026-07-24 | 2574 |
| 2026-07-25 | 2574 |
| 2026-07-28 | 2574 |
| 2026-07-30 | 2574 |
| 2026-08-06 | 2574 |
Today
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Momentum
0.0
growth rate 0.00%/day
# LibMTL [](https://libmtl.readthedocs.io/en/latest/?badge=latest) [](https://github.com/median-research-group/LibMTL/blob/main/LICENSE) [](https://badge.fury.io/py/LibMTL) [](https://github.com/median-research-group/LibMTL) [](https://www.codefactor.io/repository/github/median-research-group/libmtl/overview/main) [](https://www.jmlr.org/papers/v24/22-0347.html) [](https://github.com/median-research-group/LibMTL) [](https://hits.seeyoufarm.com) [](https://github.com/median-research-group/LibMTL) ``LibMTL`` is an open-source library built on [PyTorch](https://pytorch.org/) for Multi-Task Learning (MTL). See the [latest documentation](https://libmtl.readthedocs.io/en/latest/) for detailed introductions and API instructions. :star: Star us on GitHub — it motivates us a lot! :bangbang: A comprehensive survey on **Gradient-based Multi-Objective Deep Learning** is now available on [arXiv](https://arxiv.org/abs/2501.10945), along with an [awesome list](https://github.com/Baijiong-Lin/Awesome-Multi-Objective-Deep-Learning). Check it out! ## News - **[Apr 21 2025]** Added support for [UPGrad](https://arxiv.org/pdf/2406.16232). - **[Feb 18 2025]** Added support for a bilevel method [Auto-Lambda](https://openreview.net/forum?id=KKeCMim5VN) (TMLR 2022). - **[Feb 17 2025]** Added support for [FAMO](https://openreview.net/forum?id=zMeemcUeXL) (NeurIPS 2023), [SDMGrad](https://openreview.net/forum?id=4Ks8RPcXd9) (NeurIPS 2023), and [MoDo](https://openreview.net/forum?id=yPkbdJxQ0o) (NeurIPS 2023; JMLR 2024). - **[Feb 06 2025]** Added support for two bilevel methods: [MOML](https://proceedings.neurips.cc/paper/2021/hash/b23975176653284f1f7356ba5539cfcb-Abstract.html) (NeurIPS 2021; AIJ 2024), [FORUM](https://ebooks.iospress.nl/doi/10.3233/FAIA240793) (ECAI 2024). - **[Sep 19 2024]** Added support for [FairGrad](https://openreview.net/forum?id=KLmWRMg6nL) (ICML 2024). - **[Aug 31 2024]** Added support for [ExcessMTL](https://openreview.net/forum?id=JzWFmMySpn) (ICML 2024). - **[Jul 24 2024]** Added support for [STCH](https://openreview.net/forum?id=m4dO5L6eCp) (ICML 2024). - **[Feb 08 2024]** Added support for [DB-MTL](https://arxiv.org/abs/2308.12029). - **[Aug 16 2023]**: Added support for [MoCo](https://openreview.net/forum?id=dLAYGdKTi2) (ICLR 2023). Many thanks to the author's help [@heshandevaka](https://github.com/heshandevaka). - **[Jul 11 2023]** Paper got accepted to [JMLR](https://jmlr.org/papers/v24/22-0347.html). - **[Jun 19 2023]** Added support for [Aligned-MTL](https://openaccess.thecvf.com/content/CVPR2023/html/Senushkin_Independent_Component_Alignment_for_Multi-Task_Learning_CVPR_2023_paper.html) (CVPR 2023). - **[Mar 10 2023]**: Added [QM9](https://github.com/median-research-group/LibMTL/tree/main/examples/qm9) and [PAWS-X](https://github.com/median-research-group/LibMTL/tree/main/examples/xtreme) examples. - **[Jul 22 2022]**: Added support for [Nash-MTL](https://proceedings.mlr.press/v162/navon22a/navon22a.pdf) (ICML 2022). - **[Jul 21 2022]**: Added support for [Learning to Branch](http://proceedings.mlr.press/v119/guo20e/guo20e.pdf) (ICML 2020). Many thanks to [@yuezhixiong](https://github.com/yuezhixiong) ([#14](https://git
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:fd3e12e2db66f1bc, topic:deep-learning, topic:pytorch