Next
Livestream will start soon!
Livestream has already ended.
Presentation has not been recorded yet!
  • title: Systematic Generalization with Edge Transformers
      0:00 / 0:00
      • Report Issue
      • Settings
      • Playlists
      • Bookmarks
      • Subtitles Off
      • Playback rate
      • Quality
      • Settings
      • Debug information
      • Server sl-yoda-v3-stream-013-alpha.b-cdn.net
      • Subtitles size Medium
      • Bookmarks
      • Server
      • sl-yoda-v3-stream-013-alpha.b-cdn.net
      • sl-yoda-v3-stream-013-beta.b-cdn.net
      • 1668715672.rsc.cdn77.org
      • 1420896597.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
            Systematic Generalization with Edge Transformers
            • Settings
            • Sync diff
            • Quality
            • Settings
            • Server
            • Quality
            • Server

            Systematic Generalization with Edge Transformers

            Dec 6, 2021

            Speakers

            LB

            Leon Bergen

            Speaker · 1 follower

            TO

            Timothy O'Donnell

            Speaker · 0 followers

            DB

            Dima Bahdanau

            Speaker · 0 followers

            About

            Recent research suggests that systematic generalization in language understanding remains a challenge for state-of-the-art neural models such as Transformers and Graph Neural Networks. To tackle this challenge, we propose the Edge Transformer, a new model that combines inspiration from Transformers and rule-based symbolic AI. The first key idea in Edge Transformers is to associate vector states with every edge, that is, with every pair of input nodes as opposed to just every node as is done in t…

            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

            Versatile Inverse Reinforcement Learning via Cumulative Rewards
            03:01

            Versatile Inverse Reinforcement Learning via Cumulative Rewards

            Niklas Freymuth, …

            N2
            N2
            NeurIPS 2021 3 years ago

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

            Task-Adaptive Neural Network Search with Meta-Contrastive Learning
            12:19

            Task-Adaptive Neural Network Search with Meta-Contrastive Learning

            Wonyong Jeong, …

            N2
            N2
            NeurIPS 2021 3 years ago

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

            Unsupervised Machine Learning Framework for Sensor Placement Optimization: Analyzing Methane Leaks
            04:50

            Unsupervised Machine Learning Framework for Sensor Placement Optimization: Analyzing Methane Leaks

            Shirui Wang, …

            N2
            N2
            NeurIPS 2021 3 years ago

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

            Teaching via Best-Case Counterexamples in the Learning-with-Equivalence-Queries Paradigm
            02:30

            Teaching via Best-Case Counterexamples in the Learning-with-Equivalence-Queries Paradigm

            Akash Kumar, …

            N2
            N2
            NeurIPS 2021 3 years ago

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

            Functional Regularization for Reinforcement Learning via Learned Fourier Features
            14:35

            Functional Regularization for Reinforcement Learning via Learned Fourier Features

            Alexander C. Li, …

            N2
            N2
            NeurIPS 2021 3 years ago

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

            Q&A for Contributed talks 1,2,3
            10:20

            Q&A for Contributed talks 1,2,3

            Agnieszka Słowik, …

            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