Temporal Graph Networks (TGN) | GNN Paper Explained

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  • Опубликовано: 8 ноя 2024

Комментарии • 10

  • @DiogoSanti
    @DiogoSanti 3 года назад +1

    Need some background on NLP, but definitely coming back here in the near future...

    • @TheAIEpiphany
      @TheAIEpiphany  3 года назад +1

      Yep unfortunately it's a universal problem, knowledge dependencies, in maths/engineering or teaching in general.

    • @DiogoSanti
      @DiogoSanti 3 года назад

      ​@@TheAIEpiphany Sure was too focused on ComputerVision, Yolo's and tracking stuff but theres a whole world out there... Started seeing some lessons from Stanford Cs224n, and reading this book so far: www.amazon.com/Transformers-Natural-Language-Processing-architectures-ebook/dp/B08S977X8K
      Will definitely see your videos too to consolidate the subject, already subscribed here and followed you on Medium as you bring a lot of the cool stuff... Thanks for blessing community with your knowledge!

  • @TheAIEpiphany
    @TheAIEpiphany  3 года назад +3

    TGN makes use of node memory information, node features, edge features, temporal information and last (but definitely not least) topological information.
    Will you folks stop, please? Hahah

  • @amirhosseindaraie5622
    @amirhosseindaraie5622 2 года назад +3

    Can you implement TGN?

  • @НиколайНовичков-е1э

    Thank you!

  • @alessandrobitetto2361
    @alessandrobitetto2361 3 года назад

    Really nice video! What's the interpretation of inductive and transductive in the benchmark part?

    • @TheAIEpiphany
      @TheAIEpiphany  3 года назад +1

      Check out my GAT jupyter notebook I've explained the difference there: github.com/gordicaleksa/pytorch-GAT/blob/main/The%20Annotated%20GAT%20(Cora).ipynb

  • @billykotsos4642
    @billykotsos4642 3 года назад +1

    So many NN architectures to get to grips with ... !!!

    • @TheAIEpiphany
      @TheAIEpiphany  3 года назад

      That's true! Haha. It can get overwhelming. I am not there as well, but step by step. 😅