Skip to content
@MaterialsInformaticsDemo

Materials Informatics

the code demo for book : An Introduction to Materials Informatics, managed by @Bin-Cao

Hello everyone 👋, here is an open-source organization that supports the book "An Introduction to Materials Informatics. Prof. Zhang Tong-yi". Our goal is to facilitate teaching and understanding in the field. We welcome your contributions and feedback, especially if you spot any mistakes that need to be corrected.

If you have any suggestions, comments, or corrections regarding the content of the book, please feel free to share them with us. We appreciate your engagement and help in improving the quality and accuracy of the materials.

Together, we can create a valuable resource for the community and ensure that the book provides accurate and up-to-date information in the field of Materials Informatics. Thank you for your support and participation!

book
Materials Informatics

Click to buy the book : An Introduction to Materials Informatics(I)

Star History

Star History Chart


Transfer learning links

1 : Instance-based transfer learning

  • Instance selection (marginal distributions are same while conditional distributions are different) :

    TrAdaboost

  • Instance re-weighting (conditional distributions are same while marginal distributions are different) :

    KMM

2 : Feature-based transfer learning

  • Explicit distance:

    • case 1 : marginal distributions are same while conditional distributions are different:

      TCA(MMD based) ; DAN(MK-MMD based)

    • case 1 : conditional distributions are same while marginal distributions are different

      JDA

    • case 3 : Both marginal distributions and conditional distributions are different

      DDA

  • Implicit distance :

    DANN

3 : Parameter-based transfer learning

  • Pretraining + fine tune

Pinned Loading

  1. DAN Public

    Learning Transferable Features with Deep Adaptation Networks

    Python 9 1

  2. TCA Public

    Domain Adaptation via Transfer Component Analysis

    Jupyter Notebook 12 1

  3. MK-MMD Public

    multi-kernel maximum mean discrepancy

    Jupyter Notebook 6 1

  4. FrustratinglyEasyDomainAdaptation Public

    Frustratingly Easy Domain Adaptation by Hal Daume ́ III

    Jupyter Notebook 2

Repositories

Showing 6 of 6 repositories
  • .github Public
    0 0 0 0 Updated Sep 9, 2024
  • PCA Public

    example

    Jupyter Notebook 1 0 0 0 Updated Sep 9, 2024
  • TCA Public

    Domain Adaptation via Transfer Component Analysis

    Jupyter Notebook 12 1 0 0 Updated Dec 11, 2023
  • FrustratinglyEasyDomainAdaptation Public

    Frustratingly Easy Domain Adaptation by Hal Daume ́ III

    Jupyter Notebook 2 0 0 0 Updated Aug 31, 2023
  • MK-MMD Public

    multi-kernel maximum mean discrepancy

    Jupyter Notebook 6 1 0 0 Updated Jul 29, 2023
  • DAN Public

    Learning Transferable Features with Deep Adaptation Networks

    Python 9 1 1 0 Updated Jul 18, 2023