Next
Livestream will start soon!
Livestream has already ended.
Presentation has not been recorded yet!
  • title: Accelerating Large-Scale Inference with Anisotropic Vector Quantization
      0:00 / 0:00
      • Report Issue
      • Settings
      • Playlists
      • Bookmarks
      • Subtitles Off
      • Playback rate
      • Quality
      • Settings
      • Debug information
      • Server sl-yoda-v2-stream-006-alpha.b-cdn.net
      • Subtitles size Medium
      • Bookmarks
      • Server
      • sl-yoda-v2-stream-006-alpha.b-cdn.net
      • sl-yoda-v2-stream-006-beta.b-cdn.net
      • 1549480416.rsc.cdn77.org
      • 1102696603.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
            Accelerating Large-Scale Inference with Anisotropic Vector Quantization
            • Settings
            • Sync diff
            • Quality
            • Settings
            • Server
            • Quality
            • Server

            Accelerating Large-Scale Inference with Anisotropic Vector Quantization

            Jul 12, 2020

            Speakers

            RG

            Ruiqi Guo

            Řečník · 1 sledující

            PS

            Philip Sun

            Řečník · 2 sledující

            EL

            Erik Lindgren

            Řečník · 0 sledujících

            About

            Quantization based techniques are the current state-of-the-art for scaling maximum inner product search to massive databases. Traditional approaches to quantization aim to minimize the reconstruction error of the database points. Based on the observation that for a given query, the database points that have the largest inner products are more relevant, we develop a family of anisotropic quantization loss functions. Under natural statistical assumptions, we show that quantization with these loss…

            Organizer

            I2
            I2

            ICML 2020

            Účet · 2,7k sledujících

            Categories

            Vývoj webu a UX/UI

            Kategorie · 1,2k prezentací

            Marketing a obchodní strategie

            Kategorie · 1,4k prezentací

            Umělá inteligence a data science

            Kategorie · 10,8k prezentací

            About 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.

            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

            Towards Algorithm-Agnostic Uncertainty Estimation: Predicting Classification Error in an Automated Machine Learning Setting
            01:06

            Towards Algorithm-Agnostic Uncertainty Estimation: Predicting Classification Error in an Automated Machine Learning Setting

            Matthias König, …

            I2
            I2
            ICML 2020 5 years ago

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

            Acceleration for Compressed Gradient Descent in Distributed Optimization
            14:06

            Acceleration for Compressed Gradient Descent in Distributed Optimization

            Zhize Li, …

            I2
            I2
            ICML 2020 5 years ago

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

            Learning Factorized Weight Matrix for Joint Image Filtering
            14:08

            Learning Factorized Weight Matrix for Joint Image Filtering

            Xiangyu Xu, …

            I2
            I2
            ICML 2020 5 years ago

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

            Evolutionary Reinforcement Learning for Sample-Efficient Multiagent Cooperation
            15:53

            Evolutionary Reinforcement Learning for Sample-Efficient Multiagent Cooperation

            Somdeb Majumdar, …

            I2
            I2
            ICML 2020 5 years ago

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

            Graph Neural Networks for Self-Driving
            38:02

            Graph Neural Networks for Self-Driving

            Raquel Urtasun

            I2
            I2
            ICML 2020 5 years ago

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

            Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models
            10:28

            Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models

            Lasse F. Wolff Anthony, …

            I2
            I2
            ICML 2020 5 years ago

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

            Interested in talks like this? Follow ICML 2020