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https://github.com/daochenzha/daochenzha

Last synced: 7 days ago
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Host: GitHub
URL: https://github.com/daochenzha/daochenzha
Owner: daochenzha
Created: 2022-05-12T15:31:48.000Z (over 2 years ago)
Default Branch: main
Last Pushed: 2024-06-26T23:00:20.000Z (6 months ago)
Last Synced: 2024-06-27T02:44:58.761Z (6 months ago)
Size: 49.8 KB
Stars: 0
Watchers: 3
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md

Awesome Lists containing this project

README

        ### Hi there, I'm [Daochen Zha (查道琛 in Chinese)!](https://dczha.com/) 👋👋👋

🔭 I’m working on machine learning and data mining, particularly on Reinforcement Learning (RL) and Data-centric AI

😄 I love developing open-source projects. I’m looking forward to collaborate on any fun projects.

🤔 I have made a YouTube video to share [my journey of open-source projects](https://youtu.be/UBn9ks8fj80).

:loudspeaker: News: Please check out our open-sourced [Large Time Series Model (LTSM)](https://github.com/daochenzha/ltsm)!

:loudspeaker: News: Please check out our [data-centric AI survey](https://arxiv.org/abs/2303.10158) and [awesome data-centric AI resources](https://github.com/daochenzha/data-centric-AI)!



### Popular Projects

| Project  | Resources |

|---|---|

| [Understanding Different Design Choices in Training Large Time Series Models](https://github.com/daochenzha/ltsm) | [Paper](https://arxiv.org/abs/2406.14045) 

| [DiscoverPath: A Knowledge Refinement and Retrieval System for Interdisciplinarity on Biomedical Research](https://github.com/ynchuang/DiscoverPath) | [Paper](https://arxiv.org/abs/2309.01808) \| [Demo](http://www.discoverpath.top/) \| [Video](https://youtu.be/xcDzBl7jp-s) |

| [FinGPT: Democratizing Internet-scale Data for Financial Large Language Models](https://github.com/AI4Finance-Foundation/FinGPT) | [Paper](https://arxiv.org/abs/2307.10485) \| [Website](https://ai4finance-foundation.github.io/FinNLP/) |

| [OpenGSL: A Comprehensive Benchmark for Graph Structure Learning](https://github.com/OpenGSL/OpenGSL) | [Paper](https://arxiv.org/abs/2306.10280) \| [知乎](https://zhuanlan.zhihu.com/p/642738341) |

| [Awesome Data-centric AI Resources](https://github.com/daochenzha/data-centric-AI) | [Survey Paper](https://arxiv.org/abs/2303.10158) \| [Perspective Paper](https://arxiv.org/abs/2301.04819) \| [知乎](https://zhuanlan.zhihu.com/p/617057227) \| [Blog](https://medium.com/towards-data-science/what-are-the-data-centric-ai-concepts-behind-gpt-models-a590071bb727) |

| [NeuroShard: Pre-train and Search: Efficient Embedding Table Sharding with Pre-trained Neural Cost Models](https://github.com/daochenzha/neuroshard) | [MLSys'23 Paper](https://arxiv.org/abs/2305.01868) |

| [DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learning](https://github.com/kwai/DouZero)  | [ICML'21 Paper](https://arxiv.org/abs/2106.06135) \| [Demo](https://www.douzero.org/) \| [知乎](https://zhuanlan.zhihu.com/p/526723604) \| [Video](https://youtu.be/inHIi8sej7Y) \| [Blog](https://medium.com/towards-data-science/douzero-mastering-doudizhu-with-reinforcement-learning-864363549c6a) |

| [RLCard: A Toolkit for Reinforcement Learning in Card Games](https://github.com/datamllab/rlcard) | [IJCAI'20 Paper](https://www.ijcai.org/proceedings/2020/0764.pdf) \| [Demo](https://www.douzero.org/) \| [知乎](https://zhuanlan.zhihu.com/p/526723604) \| [Video](https://youtu.be/krK2jmSdKZc) \| [Blog](https://medium.com/towards-data-science/rlcard-building-your-own-poker-ai-in-3-steps-398aa864a0db) |

| [RLCard-Showdown: Frontend for DouZero and RLCard](https://github.com/datamllab/rlcard-showdown) | [Demo](https://www.douzero.org/) |

| [Awesome Game AI Resources on Multi-agent Learning](https://github.com/datamllab/awesome-game-ai)  |   |

| [TODS: Automated Time-series Outlier Detection System](https://github.com/datamllab/tods) | [AAAI'21 Paper](https://arxiv.org/abs/2009.09822) \| [Blog](https://medium.com/towards-data-science/tods-detecting-outliers-from-time-series-data-2d4bd2e91381) \| [Video](https://youtu.be/H0bBXuDUe7s) |

| [AutoVideo: An Automated Video Action Recognition System](https://github.com/datamllab/autovideo)  | [IJCAI'22 Paper](https://arxiv.org/abs/2108.04212) \| [Blog](https://towardsdatascience.com/autovideo-an-automated-video-action-recognition-system-43198beff99d) \| [Video](https://youtu.be/BEInjBjeIuo) |

| [BED: A Real-Time Object Detection System for Edge Devices](https://github.com/datamllab/BED_main) | [CIKM'22 Best Demo Paper](https://arxiv.org/abs/2202.07503) \| [Video](https://youtu.be/0tY31_cECCA) |

| [PyODDS: An End-to-end Outlier Detection System](https://github.com/datamllab/pyodds) | [WWW'20 Paper](https://arxiv.org/abs/2003.05602) |

| [DreamShard: Generalizable Embedding Table Placement for Recommender Systems](https://github.com/daochenzha/dreamshard) | [NeurIPS'22 Paper](https://arxiv.org/abs/2210.02023) |

| [AutoShard: Automated Embedding Table Sharding for Recommender Systems](https://github.com/daochenzha/autoshard) | [KDD'22 Paper](https://arxiv.org/abs/2208.06399) |

| [Towards Automated Over-Sampling for Imbalanced Classification](https://github.com/daochenzha/autosmote) | [CIKM'22 Paper](https://arxiv.org/abs/2208.12433) |

| [Towards Similarity-Aware Time-Series Classification](https://github.com/daochenzha/SimTSC) | [SDM'22 Paper](https://arxiv.org/abs/2201.01413) |

| [Rank the Episodes: A Simple Approach for Exploration in Procedurally-Generated Environments](https://github.com/daochenzha/rapid) | [ICLR'21 Paper](https://arxiv.org/abs/2101.08152) |

| [Meta-AAD: Active Anomaly Detection with Deep Reinforcement Learning](https://github.com/daochenzha/Meta-AAD) | [ICDM'20 Paper](https://arxiv.org/abs/2009.07415) |