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Can You Trust Your Model's Uncertainty? Evaluating Predictive
  Uncertainty Under Dataset Shift
v1v2 (latest)

Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift

6 June 2019
Yaniv Ovadia
Emily Fertig
Jie Jessie Ren
Zachary Nado
D. Sculley
Sebastian Nowozin
Joshua V. Dillon
Balaji Lakshminarayanan
Jasper Snoek
    UQCV
ArXiv (abs)PDFHTML

Papers citing "Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift"

12 / 1,062 papers shown
Title
We Know Where We Don't Know: 3D Bayesian CNNs for Credible Geometric
  Uncertainty
We Know Where We Don't Know: 3D Bayesian CNNs for Credible Geometric Uncertainty
T. LaBonte
Carianne Martinez
S. Roberts
UQCV3DV
53
31
0
23 Oct 2019
Detecting Underspecification with Local Ensembles
Detecting Underspecification with Local Ensembles
David Madras
James Atwood
Alexander DÁmour
64
4
0
21 Oct 2019
NGBoost: Natural Gradient Boosting for Probabilistic Prediction
NGBoost: Natural Gradient Boosting for Probabilistic Prediction
Tony Duan
Anand Avati
D. Ding
Khanh K. Thai
S. Basu
A. Ng
Alejandro Schuler
BDL
67
306
0
08 Oct 2019
Uncertainty Quantification with Statistical Guarantees in End-to-End
  Autonomous Driving Control
Uncertainty Quantification with Statistical Guarantees in End-to-End Autonomous Driving Control
Rhiannon Michelmore
Matthew Wicker
Luca Laurenti
L. Cardelli
Y. Gal
Marta Z. Kwiatkowska
BDL
133
107
0
21 Sep 2019
Open Set Recognition Through Deep Neural Network Uncertainty: Does
  Out-of-Distribution Detection Require Generative Classifiers?
Open Set Recognition Through Deep Neural Network Uncertainty: Does Out-of-Distribution Detection Require Generative Classifiers?
Martin Mundt
Iuliia Pliushch
Sagnik Majumder
Visvanathan Ramesh
UQCVEDL
65
42
0
26 Aug 2019
Vector Quantized Bayesian Neural Network Inference for Data Streams
Vector Quantized Bayesian Neural Network Inference for Data Streams
Namuk Park
Taekyu Lee
Songkuk Kim
MQ
59
10
0
12 Jul 2019
Likelihood Ratios for Out-of-Distribution Detection
Likelihood Ratios for Out-of-Distribution Detection
Jie Jessie Ren
Peter J. Liu
Emily Fertig
Jasper Snoek
Ryan Poplin
M. DePristo
Joshua V. Dillon
Balaji Lakshminarayanan
OODD
246
728
0
07 Jun 2019
Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer
  Vision
Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Vision
Fredrik K. Gustafsson
Martin Danelljan
Thomas B. Schon
OODUQCVBDL
82
301
0
04 Jun 2019
Unified Probabilistic Deep Continual Learning through Generative Replay
  and Open Set Recognition
Unified Probabilistic Deep Continual Learning through Generative Replay and Open Set Recognition
Martin Mundt
Iuliia Pliushch
Sagnik Majumder
Yongwon Hong
Visvanathan Ramesh
UQCVBDL
104
41
0
28 May 2019
Field-aware Calibration: A Simple and Empirically Strong Method for
  Reliable Probabilistic Predictions
Field-aware Calibration: A Simple and Empirically Strong Method for Reliable Probabilistic Predictions
Feiyang Pan
Xiang Ao
Pingzhong Tang
Min Lu
Dapeng Liu
Lei Xiao
Qing He
72
22
0
26 May 2019
Ensemble Distribution Distillation
Ensemble Distribution Distillation
A. Malinin
Bruno Mlodozeniec
Mark Gales
UQCV
99
237
0
30 Apr 2019
A Roadmap for Robust End-to-End Alignment
A Roadmap for Robust End-to-End Alignment
L. Hoang
62
1
0
04 Sep 2018
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