Self-/Unsupervised GNN Training

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  • Опубліковано 13 чер 2024
  • ▬▬ Papers/Sources ▬▬▬▬▬▬▬
    - Molecular Pre-Training Evaluation: arxiv.org/pdf/2207.06010.pdf
    - Latent Space Image: arxiv.org/pdf/2206.08005.pdf
    - Survey Xie et al.: arxiv.org/pdf/2102.10757.pdf
    - Survey Liu et al.: arxiv.org/pdf/2103.00111.pdf
    - Graph Autoencoder, Kipf/Welling: arxiv.org/pdf/1611.07308.pdf
    - GraphCL: arxiv.org/pdf/2010.13902.pdf
    - Deep Graph Infomax: arxiv.org/pdf/1809.10341.pdf
    - InfoGraph: openreview.net/pdf?id=r1lfF2NYvH
    ▬▬ Used Icons ▬▬▬▬▬▬▬▬▬▬
    All Icons are from flaticon: www.flaticon.com/authors/freepik
    ▬▬ Used Music ▬▬▬▬▬▬▬▬▬▬▬
    Music from Uppbeat (free for Creators!):
    uppbeat.io/t/mountaineer/autu...
    License code: EHK2BNGUHRZX1CXK
    ▬▬ Timestamps ▬▬▬▬▬▬▬▬▬▬▬
    00:00 Introduction
    00:20 Applications
    01:48 Unsupervised Learning on Graphs
    02:28 Self-Supervised Learning
    03:19 Overview SSL on Graphs
    04:44 Graph/Node-level
    05:16 Reconstruction Approaches
    07:10 Predictive / Task Generation Approaches
    08:12 Contrastive Approaches
    10:51 Comments on Similarity
    11:30 Final Remarks
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КОМЕНТАРІ • 23

  • @jihochoi_cs
    @jihochoi_cs Рік тому +4

    Amazing video! Thanks for the great work.

  • @radhen171992
    @radhen171992 6 місяців тому +1

    This video is exactly what I was looking for! Great summary.

  • @solo_driven
    @solo_driven Рік тому +1

    Great channel! Keep up the good work!

  • @NeotenicApe
    @NeotenicApe Рік тому

    You are incredible

  •  Рік тому

    Great as always :D

  • @MaryamKamali-ln4lz
    @MaryamKamali-ln4lz Рік тому

    Thanks for really great videos! Could you please create a video on temporal variational graph autoencoders?

  • @sakib.9419
    @sakib.9419 Рік тому +1

    bro your channel is insane!

    • @DeepFindr
      @DeepFindr  Рік тому

      Haha thanks! Glad you like it

    • @sakib.9419
      @sakib.9419 Рік тому

      @@DeepFindr I do a lot of machine learning for my work and I’ve recently thought about modelling train networks as a graph to apply algorithms. d’you have a discord or something, would love to chat a bit?

    • @DeepFindr
      @DeepFindr  Рік тому +1

      Hi! I'd love to but unfortunately I don't find time to maintain a discord or anything like that. Maybe in the future :)

  • @RishabhSingh-hc4nz
    @RishabhSingh-hc4nz Рік тому +1

    Ur work is appreciable sir pls upload next video and also make the movie recommender system code explanation

  • @trez6465
    @trez6465 Рік тому

    Amazing video. What tool do you use to make the slides and the beautiful graphics?

    • @DeepFindr
      @DeepFindr  Рік тому

      Thanks :) DaVinci resolve and PowerPoint :P

  • @OGIMxGaMeR
    @OGIMxGaMeR 10 місяців тому

    Do you know any libraries implementing self supervised learning on graphs?

  • @mrinmoybanik5598
    @mrinmoybanik5598 5 місяців тому

    Hmm can make something on solving NP hard combinatorial graph problems using GNNs?

  • @comp-it1913
    @comp-it1913 Рік тому +5

    I am looking forward to present a lecture based in dynamic graphs variational autoencoder where temporal dependencies occured in dataset such as autonomous driving for point clouds change at each time instant,also to get latent space Z. Please read a paper joint dynamic variational graph autoencoder and make a video on its implementation step by step. I will be very thankful

    • @user-lb9fv3kd8v
      @user-lb9fv3kd8v 5 місяців тому

      You can search
      VGRNN(Variational Graph Recurrent Neural Network) which is handling dynamic graph by snapshot. It means that sequences graph (=dynamic graph) can see multi-static graph. This method called discrete method.

  • @dzxxbj
    @dzxxbj Рік тому

    Can show some demos on google colab, are there any more applications ?

    • @DeepFindr
      @DeepFindr  Рік тому

      Will do a contrastive tutorial with code soon :) what kind of applications do you mean?

  • @ghadaalmousa9248
    @ghadaalmousa9248 Рік тому

    Hello,
    Do you think Generative Adversarial Networks can improve the performance of GCN . if that so how it work?/