Next
Livestream will start soon!
Livestream has already ended.
Presentation has not been recorded yet!
  • title: Poisson Learning: Graph Based Semi-Supervised Learning At Very Low Label Rates
      0:00 / 0:00
      • Report Issue
      • Settings
      • Playlists
      • Bookmarks
      • Subtitles Off
      • Playback rate
      • Quality
      • Settings
      • Debug information
      • Server sl-yoda-v3-stream-012-alpha.b-cdn.net
      • Subtitles size Medium
      • Bookmarks
      • Server
      • sl-yoda-v3-stream-012-alpha.b-cdn.net
      • sl-yoda-v3-stream-012-beta.b-cdn.net
      • 1338956956.rsc.cdn77.org
      • 1656830687.rsc.cdn77.org
      • Subtitles
      • Off
      • en
      • Playback rate
      • Quality
      • Subtitles size
      • Large
      • Medium
      • Small
      • Mode
      • Video Slideshow
      • Audio Slideshow
      • Slideshow
      • Video
      My playlists
        Bookmarks
          00:00:00
            Poisson Learning: Graph Based Semi-Supervised Learning At Very Low Label Rates
            • Settings
            • Sync diff
            • Quality
            • Settings
            • Server
            • Quality
            • Server

            Poisson Learning: Graph Based Semi-Supervised Learning At Very Low Label Rates

            Jul 12, 2020

            Sprecher:innen

            JC

            Jeff Calder

            Řečník · 0 sledujících

            BC

            Brendan Cook

            Řečník · 0 sledujících

            MT

            Matthew Thorpe

            Řečník · 0 sledujících

            Über

            We propose a new framework, called Poisson learning, for graph based semi-supervised learning at very low label rates. Poisson learning is motivated by the need to address the degeneracy of Laplacian semi-supervised learning at very low label rates. The method replaces the assignment of label values at training points with the placement of sources and sinks, and solves the resulting Poisson equation on the graph. The outcomes are provably more stable and informative than those of Laplacian learn…

            Organisator

            I2
            I2

            ICML 2020

            Účet · 2,7k sledujících

            Kategorien

            Matematika

            Kategorie · 2,4k prezentací

            Über ICML 2020

            The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.

            Gefällt euch das Format? Vertraut auf SlidesLive, um euer nächstes Event festzuhalten!

            Professionelle Aufzeichnung und Livestreaming – weltweit.

            Freigeben

            Empfohlene Videos

            Präsentationen, deren Thema, Kategorie oder Sprecher:in ähnlich sind

            Laguerre-Gauss Preprocessing: Line Profiles as Image Features
            05:07

            Laguerre-Gauss Preprocessing: Line Profiles as Image Features

            Alejandro Murillo-Gonzalez, …

            I2
            I2
            ICML 2020 5 years ago

            Pro uložení prezentace do věčného trezoru hlasovalo 0 diváků, což je 0.0 %

            Interference and Generalization in Temporal Difference Learning
            10:58

            Interference and Generalization in Temporal Difference Learning

            Emmanuel Bengio, …

            I2
            I2
            ICML 2020 5 years ago

            Pro uložení prezentace do věčného trezoru hlasovalo 0 diváků, což je 0.0 %

            PAC Bounds for Imitation and Model-based Batch Learning of Contextual Markov Decision Processes
            05:16

            PAC Bounds for Imitation and Model-based Batch Learning of Contextual Markov Decision Processes

            Yash Nair, …

            I2
            I2
            ICML 2020 5 years ago

            Pro uložení prezentace do věčného trezoru hlasovalo 0 diváků, což je 0.0 %

            GradientDICE: Rethinking Generalized Offline Estimation of Stationary Values
            12:27

            GradientDICE: Rethinking Generalized Offline Estimation of Stationary Values

            Shangtong Zhang, …

            I2
            I2
            ICML 2020 5 years ago

            Pro uložení prezentace do věčného trezoru hlasovalo 0 diváků, což je 0.0 %

            Interpretable End-to-end Autonomous Driving with Reinforcement Learning
            02:57

            Interpretable End-to-end Autonomous Driving with Reinforcement Learning

            Jianyu Chen, …

            I2
            I2
            ICML 2020 5 years ago

            Pro uložení prezentace do věčného trezoru hlasovalo 0 diváků, což je 0.0 %

            Transformer Hawkes Process
            14:13

            Transformer Hawkes Process

            Simiao Zuo, …

            I2
            I2
            ICML 2020 5 years ago

            Pro uložení prezentace do věčného trezoru hlasovalo 1 diváků, což je 0.1 %

            Interessiert an Vorträgen wie diesem? ICML 2020 folgen