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Uncertainty Toolbox: an Open-Source Library for Assessing, Visualizing,
  and Improving Uncertainty Quantification

Uncertainty Toolbox: an Open-Source Library for Assessing, Visualizing, and Improving Uncertainty Quantification

21 September 2021
Youngseog Chung
I. Char
Han Guo
J. Schneider
W. Neiswanger
ArXivPDFHTML

Papers citing "Uncertainty Toolbox: an Open-Source Library for Assessing, Visualizing, and Improving Uncertainty Quantification"

13 / 13 papers shown
Title
Lightning UQ Box: A Comprehensive Framework for Uncertainty
  Quantification in Deep Learning
Lightning UQ Box: A Comprehensive Framework for Uncertainty Quantification in Deep Learning
Nils Lehmann
Jakob Gawlikowski
Adam J. Stewart
Vytautas Jancauskas
Stefan Depeweg
Eric T. Nalisnick
N. Gottschling
32
0
0
04 Oct 2024
A novel fusion of Sentinel-1 and Sentinel-2 with climate data for crop phenology estimation using Machine Learning
A novel fusion of Sentinel-1 and Sentinel-2 with climate data for crop phenology estimation using Machine Learning
Shahab Aldin Shojaeezadeh
Abdelrazek Elnashar
Tobias Karl David Weber
29
0
0
16 Aug 2024
Full Shot Predictions for the DIII-D Tokamak via Deep Recurrent Networks
Full Shot Predictions for the DIII-D Tokamak via Deep Recurrent Networks
I. Char
Youngseog Chung
J. Abbate
E. Kolemen
Jeff Schneider
20
4
0
18 Apr 2024
Uncertainty Quantification for Image-based Traffic Prediction across
  Cities
Uncertainty Quantification for Image-based Traffic Prediction across Cities
Alexander Timans
Nina Wiedemann
Nishant Kumar
Ye Hong
Martin Raubal
11
1
0
11 Aug 2023
Distance Preserving Machine Learning for Uncertainty Aware Accelerator
  Capacitance Predictions
Distance Preserving Machine Learning for Uncertainty Aware Accelerator Capacitance Predictions
S. Goldenberg
M. Schram
Kishansingh Rajput
T. Britton
C. Pappas
Dawei Lu
Jared Walden
M. Radaideh
Sarah Cousineau
S. Harave
19
1
0
05 Jul 2023
Bayesian Optimization of Catalysts With In-context Learning
Bayesian Optimization of Catalysts With In-context Learning
M. C. Ramos
Shane S. Michtavy
Marc D. Porosoff
Andrew D. White
BDL
18
30
0
11 Apr 2023
On the role of Model Uncertainties in Bayesian Optimization
On the role of Model Uncertainties in Bayesian Optimization
Jonathan Foldager
Mikkel Jordahn
Lars Kai Hansen
Michael Riis Andersen
11
4
0
14 Jan 2023
NeuralUQ: A comprehensive library for uncertainty quantification in
  neural differential equations and operators
NeuralUQ: A comprehensive library for uncertainty quantification in neural differential equations and operators
Zongren Zou
Xuhui Meng
Apostolos F. Psaros
George Karniadakis
AI4CE
17
36
0
25 Aug 2022
Transformer Neural Processes: Uncertainty-Aware Meta Learning Via
  Sequence Modeling
Transformer Neural Processes: Uncertainty-Aware Meta Learning Via Sequence Modeling
Tung Nguyen
Aditya Grover
BDL
UQCV
8
99
0
09 Jul 2022
Uncertainty Quantification for Deep Unrolling-Based Computational
  Imaging
Uncertainty Quantification for Deep Unrolling-Based Computational Imaging
Canberk Ekmekci
Müjdat Çetin
UQCV
11
10
0
02 Jul 2022
Modular Conformal Calibration
Modular Conformal Calibration
Charles Marx
Shengjia Zhao
W. Neiswanger
Stefano Ermon
24
15
0
23 Jun 2022
A framework for benchmarking uncertainty in deep regression
A framework for benchmarking uncertainty in deep regression
F. Schmähling
Jörg Martin
Clemens Elster
UQCV
23
8
0
10 Sep 2021
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
268
5,635
0
05 Dec 2016
1