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1906.00588
Cited By
Quantifying Point-Prediction Uncertainty in Neural Networks via Residual Estimation with an I/O Kernel
3 June 2019
Xin Qiu
Elliot Meyerson
Risto Miikkulainen
UQCV
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Papers citing
"Quantifying Point-Prediction Uncertainty in Neural Networks via Residual Estimation with an I/O Kernel"
9 / 9 papers shown
Title
Uncertainty Quantification Metrics for Deep Regression
Simon Kristoffersson Lind
Ziliang Xiong
Per-Erik Forssén
Volker Kruger
UQCV
34
3
0
07 May 2024
Scheduling Planting Time Through Developing an Optimization Model and Analysis of Time Series Growing Degree Units
Javad Ansarifar
Faezeh Akhavizadegan
Lizhi Wang
14
1
0
02 Jul 2022
Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imaging
Anastasios Nikolas Angelopoulos
Amit Kohli
Stephen Bates
Michael I. Jordan
Jitendra Malik
T. Alshaabi
S. Upadhyayula
Yaniv Romano
UQCV
OOD
14
94
0
10 Feb 2022
Quantifying Uncertainty in Deep Spatiotemporal Forecasting
Dongxian Wu
Liyao (Mars) Gao
X. Xiong
Matteo Chinazzi
Alessandro Vespignani
Yi Ma
Rose Yu
AI4TS
13
68
0
25 May 2021
DeepGLEAM: A hybrid mechanistic and deep learning model for COVID-19 forecasting
Dongxian Wu
Liyao (Mars) Gao
X. Xiong
Matteo Chinazzi
Alessandro Vespignani
Yi Ma
Rose Yu
FedML
30
27
0
12 Feb 2021
Why have a Unified Predictive Uncertainty? Disentangling it using Deep Split Ensembles
U. Sarawgi
W. Zulfikar
Rishab Khincha
Pattie Maes
PER
UQCV
BDL
UD
21
7
0
25 Sep 2020
Effective Reinforcement Learning through Evolutionary Surrogate-Assisted Prescription
Olivier Francon
Santiago Gonzalez
B. Hodjat
Elliot Meyerson
Risto Miikkulainen
Xin Qiu
H. Shahrzad
26
16
0
13 Feb 2020
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Balaji Lakshminarayanan
Alexander Pritzel
Charles Blundell
UQCV
BDL
276
5,683
0
05 Dec 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCV
BDL
287
9,156
0
06 Jun 2015
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