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. 2310.03393
  4. Cited By
Uncertainty quantification for deep learning-based schemes for solving
  high-dimensional backward stochastic differential equations

Uncertainty quantification for deep learning-based schemes for solving high-dimensional backward stochastic differential equations

5 October 2023
Lorenc Kapllani
Long Teng
Matthias Rottmann
ArXivPDFHTML

Papers citing "Uncertainty quantification for deep learning-based schemes for solving high-dimensional backward stochastic differential equations"

4 / 4 papers shown
Title
A backward differential deep learning-based algorithm for solving
  high-dimensional nonlinear backward stochastic differential equations
A backward differential deep learning-based algorithm for solving high-dimensional nonlinear backward stochastic differential equations
Lorenc Kapllani
Long Teng
31
2
0
12 Apr 2024
UQGAN: A Unified Model for Uncertainty Quantification of Deep
  Classifiers trained via Conditional GANs
UQGAN: A Unified Model for Uncertainty Quantification of Deep Classifiers trained via Conditional GANs
Philipp Oberdiek
G. Fink
Matthias Rottmann
OODD
40
14
0
31 Jan 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