Next
Livestream will start soon!
Livestream has already ended.
Presentation has not been recorded yet!
  • title: Privacy-Preserving Machine Learning for Collaborative Data Sharing via Auto-encoder Latent Space Embeddings
      0:00 / 0:00
      • Report Issue
      • Settings
      • Playlists
      • Bookmarks
      • Subtitles Off
      • Playback rate
      • Quality
      • Settings
      • Debug information
      • Server sl-yoda-v2-stream-003-alpha.b-cdn.net
      • Subtitles size Medium
      • Bookmarks
      • Server
      • sl-yoda-v2-stream-003-alpha.b-cdn.net
      • sl-yoda-v2-stream-003-beta.b-cdn.net
      • 1544410162.rsc.cdn77.org
      • 1005514182.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
            Privacy-Preserving Machine Learning for Collaborative Data Sharing via Auto-encoder Latent Space Embeddings
            • Settings
            • Sync diff
            • Quality
            • Settings
            • Server
            • Quality
            • Server

            Privacy-Preserving Machine Learning for Collaborative Data Sharing via Auto-encoder Latent Space Embeddings

            Dec 2, 2022

            Speakers

            AMQ

            Ana María Quintero-Ossa

            Speaker · 0 followers

            JS

            Jesus Solano

            Speaker · 0 followers

            HJ

            Hermán Jarcia

            Speaker · 0 followers

            About

            Privacy-preserving machine learning in data-sharing processes is an ever-critical task that enables collaborative training of Machine Learning (ML) models without the need to share the original data sources. It is especially relevant when an organization must assure that sensitive data remains private throughout the whole ML pipeline, i.e., training and inference phases. This paper presents an innovative framework that uses Representation Learning via autoencoders to generate privacy-preserving…

            Organizer

            N2
            N2

            NeurIPS 2022

            Account · 952 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

            The Hessian Screening Rule
            05:02

            The Hessian Screening Rule

            Johan Larsson, …

            N2
            N2
            NeurIPS 2022 2 years ago

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

            A Ranking Game for Imitation Learning
            05:12

            A Ranking Game for Imitation Learning

            Harshit Sikchi, …

            N2
            N2
            NeurIPS 2022 2 years ago

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

            Towards Diverse and Faithful One-shot Adaption of Generative Adversarial Networks
            04:13

            Towards Diverse and Faithful One-shot Adaption of Generative Adversarial Networks

            Yabo Zhang, …

            N2
            N2
            NeurIPS 2022 2 years ago

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

            Learning Causual Structures and Causual Representations from Data
            28:36

            Learning Causual Structures and Causual Representations from Data

            Peter Spirtes

            N2
            N2
            NeurIPS 2022 2 years ago

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

            LASSIE: Learning Articulated Shapes from Sparse Image Ensemble via 3D Part Discovery
            05:01

            LASSIE: Learning Articulated Shapes from Sparse Image Ensemble via 3D Part Discovery

            Chun-Han Yao, …

            N2
            N2
            NeurIPS 2022 2 years ago

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

            An Embarrassingly Simple Approach to Semi-Supervised Few-Shot Learning
            01:00

            An Embarrassingly Simple Approach to Semi-Supervised Few-Shot Learning

            Xiu-Shen Wei, …

            N2
            N2
            NeurIPS 2022 2 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 2022