ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2005.01889
  4. Cited By
Interpreting Rate-Distortion of Variational Autoencoder and Using Model
  Uncertainty for Anomaly Detection

Interpreting Rate-Distortion of Variational Autoencoder and Using Model Uncertainty for Anomaly Detection

5 May 2020
Seonho Park
George Adosoglou
P. Pardalos
    DRL
    UQCV
ArXivPDFHTML

Papers citing "Interpreting Rate-Distortion of Variational Autoencoder and Using Model Uncertainty for Anomaly Detection"

1 / 1 papers shown
Title
Dropout as a Bayesian Approximation: Representing Model Uncertainty in
  Deep Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCV
BDL
247
9,042
0
06 Jun 2015
1