Deep learning on graphs: successes, challenges | Graph Neural Networks | Michael Bronstein

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  • Опубликовано: 24 дек 2024

Комментарии • 22

  • @andreasholzinger7056
    @andreasholzinger7056 2 года назад +4

    An excellent talk on the emerging topic of geometrical deep learning - this could bring topological data analysis which we did for decades to new importance!

  • @IproCoGo
    @IproCoGo 3 года назад

    This talk provides a helpful understanding of the intuition behind the speaker's work on geometric deep learning. Great talk! Thank you.

  • @yiyinghu2304
    @yiyinghu2304 3 года назад +4

    great talk, such a generous share

  • @KwstaSRr
    @KwstaSRr 3 года назад

    Man, this is a Masterpiece.
    Thank you for sharing.

  • @romigoldner4040
    @romigoldner4040 3 года назад +1

    Fantastic talk! Thanks for sharing!

  • @Ali-xo9ht
    @Ali-xo9ht 3 года назад +2

    Amazing talk! I learned a lot and got some ideas for my research. Cheers!

  • @torstenschindler1965
    @torstenschindler1965 4 года назад +6

    Nice lecture!
    “Attention is all you need.” - Is that also true for GNNs?
    Can dynamic graph networks be used to predict chemical reaction outcomes or yields or retrosynthetic pathways?
    The secret sauce of autogluon tabular is bagging, stacking and destillation. How to apply destillation on graph neural networks?

    • @chrisoman87
      @chrisoman87 2 года назад +2

      Well regarding attention I would look up GAT (Graphical Attention Networks)

  • @nerdinvestdor
    @nerdinvestdor 4 года назад +1

    Awesome lecture thanks!!

    • @nerdinvestdor
      @nerdinvestdor 4 года назад

      One thing that’s always confuse me is that seems (or in any example I found) with node/link prediction the whole dataset is a single graph. Is it possible to train/predict with different graphs?

  • @Yutaro-Yoshii
    @Yutaro-Yoshii 2 года назад

    41:45 It was a bunny mesh oirc

    • @Yutaro-Yoshii
      @Yutaro-Yoshii 2 года назад

      *iirc

    • @Yutaro-Yoshii
      @Yutaro-Yoshii 2 года назад

      I love how carefully picking samples expedites the training time! Great idea that may be applicable in other situations!

  • @PlexusTen
    @PlexusTen 3 года назад

    Great talk!

  • @francescos7361
    @francescos7361 2 года назад

    Thanks super powerful.

  • @gems34
    @gems34 2 года назад

    Yup, food is medicine, many of us overlook this fundamental property. Eat right and the likelihood of illness diminishes exponentially

  • @kellybrower301
    @kellybrower301 3 года назад +1

    Gold

  • @gumbo64
    @gumbo64 3 года назад

    oh I was wondering how all those 3d AI things worked

  • @peabrane8067
    @peabrane8067 3 года назад

    Hi

  • @siquod
    @siquod 3 года назад

    I didn't even need to see the bunny to be pretty sure it must be a bunny. Most animal meshes in academia are bunnies.

    • @afbf6522
      @afbf6522 3 года назад

      Applying Bayes' naive rule, eh?😜

  • @mikefat6189
    @mikefat6189 4 года назад +1

    This is who you want to be getting your geometry from hahahahaha