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Our transdim is still under development. More machine learning models and technical features are going to be added and we always welcome contributions to help make transdim better. In this document, we would like to express our sincere gratitude to researchers for improving transdim project:

  • 2020

    • Antony Masso Lussier (HEC Montreal): For improving the readability of TRMF code.
    • Yingjie Li (China University of Mining and Technology): For improving the readability of BTMF code.

  • 2021

    • Tinghan Chen (University of Florida): For feedback about the training process (difference between dense_mat and sparse_mat) of BTMF code.
    • Gang Wang (Stony Brook University): For correcting the typo of Kronecker product example and cp_combine example.

If you have any suggestion about this project, please feel free to contact Xinyu Chen (email: [email protected]) and send your suggestion. We would like to thank everyone who has helped this project in any way.