Next
Livestream will start soon!
Livestream has already ended.
Presentation has not been recorded yet!
  • title: Let the Flows Tell: Solving Graph Combinatorial Problems with GFlowNets
      0:00 / 0:00
      • Report Issue
      • Settings
      • Playlists
      • Bookmarks
      • Subtitles Off
      • Playback rate
      • Quality
      • Settings
      • Debug information
      • Server sl-yoda-v2-stream-005-alpha.b-cdn.net
      • Subtitles size Medium
      • Bookmarks
      • Server
      • sl-yoda-v2-stream-005-alpha.b-cdn.net
      • sl-yoda-v2-stream-005-beta.b-cdn.net
      • 1034628162.rsc.cdn77.org
      • 1409346856.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
            Let the Flows Tell: Solving Graph Combinatorial Problems with GFlowNets
            • Settings
            • Sync diff
            • Quality
            • Settings
            • Server
            • Quality
            • Server

            Let the Flows Tell: Solving Graph Combinatorial Problems with GFlowNets

            Dec 10, 2023

            Speakers

            DZ

            Dinghuai Zhang

            Speaker · 2 followers

            HD

            Hanjun Dai

            Speaker · 1 follower

            NM

            Nikolay Malkin

            Speaker · 1 follower

            About

            Combinatorial optimization (CO) problems are often NP-hard and thus out of reach for exact algorithms, making them a tempting domain to apply machine learning methods. The highly structured constraints in these problems can hinder either optimization or sampling directly in the solution space.On the other hand, GFlowNets have recently emerged as a powerful machinery to efficiently sample from composite unnormalized densities sequentially and have the potential to amortize such solution-searching…

            Organizer

            N2
            N2

            NeurIPS 2023

            Account · 617 followers

            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

            GIMLET: A Unified Graph-Text Model for Instruction-Based Molecule Zero-Shot Learning
            04:47

            GIMLET: A Unified Graph-Text Model for Instruction-Based Molecule Zero-Shot Learning

            Haiteng Zhao, …

            N2
            N2
            NeurIPS 2023 15 months ago

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

            Comparing Data-Driven and Mechanistic Models for Predicting Phenology in Deciduous Broadleaf Forests
            05:00

            Comparing Data-Driven and Mechanistic Models for Predicting Phenology in Deciduous Broadleaf Forests

            Christian Reimers, …

            N2
            N2
            NeurIPS 2023 15 months ago

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

            Fused Gromov-Wasserstein Graph Mixup for Graph-level Classifications
            05:25

            Fused Gromov-Wasserstein Graph Mixup for Graph-level Classifications

            Xinyu Ma, …

            N2
            N2
            NeurIPS 2023 15 months ago

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

            Subclass-Dominant Label Noise: A Counterexample for the Success of Early Stopping
            04:48

            Subclass-Dominant Label Noise: A Counterexample for the Success of Early Stopping

            Yingbin Bai, …

            N2
            N2
            NeurIPS 2023 15 months ago

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

            Text-to-Image Diffusion Models are Zero Shot Classifiers
            05:00

            Text-to-Image Diffusion Models are Zero Shot Classifiers

            Kevin Clark, …

            N2
            N2
            NeurIPS 2023 15 months ago

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

            Implicit Bias of (Stochastic) Gradient Descent for Rank-1 Linear Neural Network
            04:47

            Implicit Bias of (Stochastic) Gradient Descent for Rank-1 Linear Neural Network

            Bochen Lyu, …

            N2
            N2
            NeurIPS 2023 15 months ago

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

            Interested in talks like this? Follow NeurIPS 2023