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. 2105.11828
  4. Cited By
Bridging the Gap Between Explainable AI and Uncertainty Quantification
  to Enhance Trustability

Bridging the Gap Between Explainable AI and Uncertainty Quantification to Enhance Trustability

25 May 2021
Dominik Seuss
ArXivPDFHTML

Papers citing "Bridging the Gap Between Explainable AI and Uncertainty Quantification to Enhance Trustability"

4 / 4 papers shown
Title
A Detailed Study of Interpretability of Deep Neural Network based Top
  Taggers
A Detailed Study of Interpretability of Deep Neural Network based Top Taggers
Ayush Khot
Mark S. Neubauer
Avik Roy
AAML
33
16
0
09 Oct 2022
Explainable AI for High Energy Physics
Explainable AI for High Energy Physics
Mark S. Neubauer
Avik Roy
32
10
0
14 Jun 2022
Simple and Scalable Predictive Uncertainty Estimation using Deep
  Ensembles
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Balaji Lakshminarayanan
Alexander Pritzel
Charles Blundell
UQCV
BDL
276
5,675
0
05 Dec 2016
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
285
9,145
0
06 Jun 2015
1