Next
Livestream will start soon!
Livestream has already ended.
Presentation has not been recorded yet!
  • title: On the Universality of Graph Neural Networks on Large Random Graphs
      0:00 / 0:00
      • Report Issue
      • Settings
      • Playlists
      • Bookmarks
      • Subtitles Off
      • Playback rate
      • Quality
      • Settings
      • Debug information
      • Server sl-yoda-v3-stream-014-alpha.b-cdn.net
      • Subtitles size Medium
      • Bookmarks
      • Server
      • sl-yoda-v3-stream-014-alpha.b-cdn.net
      • sl-yoda-v3-stream-014-beta.b-cdn.net
      • 1978117156.rsc.cdn77.org
      • 1243944885.rsc.cdn77.org
      • Subtitles
      • Off
      • English
      • Playback rate
      • Quality
      • Subtitles size
      • Large
      • Medium
      • Small
      • Mode
      • Video Slideshow
      • Audio Slideshow
      • Slideshow
      • Video
      My playlists
        Bookmarks
          00:00:00
            On the Universality of Graph Neural Networks on Large Random Graphs
            • Settings
            • Sync diff
            • Quality
            • Settings
            • Server
            • Quality
            • Server

            On the Universality of Graph Neural Networks on Large Random Graphs

            Dec 6, 2021

            Speakers

            NK

            Nicolas Keriven

            Speaker · 0 followers

            AB

            Alberto Bietti

            Speaker · 0 followers

            SV

            Samuel Vaiter

            Speaker · 0 followers

            About

            We study the approximation power of Graph Neural Networks (GNNs) on latent position random graphs. In the large graph limit, GNNs are known to converge to certain “continuous” models known as c-GNNs, which directly enables a study of their approximation power on random graph models. In the absence of input node features however, just as GNNs are limited by the Weisfeiler-Lehman isomorphism test, c-GNNs will be severely limited on simple random graph models. For instance, they will fail to distin…

            Organizer

            N2
            N2

            NeurIPS 2021

            Account · 1.9k followers

            About NeurIPS 2021

            Neural Information Processing Systems (NeurIPS) is a multi-track machine learning and computational neuroscience conference that includes invited talks, demonstrations, symposia and oral and poster presentations of refereed papers. Following the conference, there are workshops which provide a less formal setting.

            Like the format? Trust SlidesLive to capture your next event!

            Professional recording and live streaming, delivered globally.

            Sharing

            Recommended Videos

            Presentations on similar topic, category or speaker

            Making a (Counterfactual) Difference one Rationale at a Time
            13:57

            Making a (Counterfactual) Difference one Rationale at a Time

            Mitchell Plyler, …

            N2
            N2
            NeurIPS 2021 3 years ago

            Total of 0 viewers voted for saving the presentation to eternal vault which is 0.0%

            Self-Adaptable Point Processes with Nonparametric Time Decays
            10:01

            Self-Adaptable Point Processes with Nonparametric Time Decays

            Zhimeng Pan, …

            N2
            N2
            NeurIPS 2021 3 years ago

            Total of 0 viewers voted for saving the presentation to eternal vault which is 0.0%

            Symmetries and Self-Supervision in Particle Physics
            04:46

            Symmetries and Self-Supervision in Particle Physics

            Barry Dillon, …

            N2
            N2
            NeurIPS 2021 3 years ago

            Total of 0 viewers voted for saving the presentation to eternal vault which is 0.0%

            Open Catalyst Project
            10:04

            Open Catalyst Project

            Muhammed Shuaibi, …

            N2
            N2
            NeurIPS 2021 3 years ago

            Total of 0 viewers voted for saving the presentation to eternal vault which is 0.0%

            NeurInt-Learning Interpolation by Neural ODEs
            15:13

            NeurInt-Learning Interpolation by Neural ODEs

            Avinandan Bose, …

            N2
            N2
            NeurIPS 2021 3 years ago

            Total of 0 viewers voted for saving the presentation to eternal vault which is 0.0%

            Exploring through Random Curiosity with General Value Functions
            04:56

            Exploring through Random Curiosity with General Value Functions

            Aditya Ramesh, …

            N2
            N2
            NeurIPS 2021 3 years ago

            Total of 0 viewers voted for saving the presentation to eternal vault which is 0.0%

            Interested in talks like this? Follow NeurIPS 2021