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Simple and Scalable Predictive Uncertainty Estimation using Deep
  Ensembles
v1v2v3 (latest)

Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles

5 December 2016
Balaji Lakshminarayanan
Alexander Pritzel
Charles Blundell
    UQCVBDL
ArXiv (abs)PDFHTML

Papers citing "Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles"

50 / 3,224 papers shown
Title
Robustness via Cross-Domain Ensembles
Robustness via Cross-Domain Ensembles
Teresa Yeo
Oğuzhan Fatih Kar
Alexander Sax
Amir Zamir
UQCVOOD
57
25
0
19 Mar 2021
CoordiNet: uncertainty-aware pose regressor for reliable vehicle
  localization
CoordiNet: uncertainty-aware pose regressor for reliable vehicle localization
Arthur Moreau
Nathan Piasco
D. Tsishkou
B. Stanciulescu
A. de La Fortelle
81
38
0
19 Mar 2021
Efficient Deep Reinforcement Learning with Imitative Expert Priors for
  Autonomous Driving
Efficient Deep Reinforcement Learning with Imitative Expert Priors for Autonomous Driving
Zhiyu Huang
Jingda Wu
Chen Lv
80
142
0
19 Mar 2021
Decision Theoretic Bootstrapping
Decision Theoretic Bootstrapping
P. Tavallali
Hamed Hamze Bajgiran
Danial Esaid
H. Owhadi
47
0
0
18 Mar 2021
CheXbreak: Misclassification Identification for Deep Learning Models
  Interpreting Chest X-rays
CheXbreak: Misclassification Identification for Deep Learning Models Interpreting Chest X-rays
E. Chen
Andy Kim
R. Krishnan
J. Long
A. Ng
Pranav Rajpurkar
51
2
0
18 Mar 2021
Repurposing Pretrained Models for Robust Out-of-domain Few-Shot Learning
Repurposing Pretrained Models for Robust Out-of-domain Few-Shot Learning
Namyeong Kwon
Hwidong Na
Gabriel Huang
Simon Lacoste-Julien
55
7
0
16 Mar 2021
Generating Interpretable Counterfactual Explanations By Implicit
  Minimisation of Epistemic and Aleatoric Uncertainties
Generating Interpretable Counterfactual Explanations By Implicit Minimisation of Epistemic and Aleatoric Uncertainties
Lisa Schut
Oscar Key
R. McGrath
Luca Costabello
Bogdan Sacaleanu
Medb Corcoran
Y. Gal
CML
110
48
0
16 Mar 2021
Sampling-free Variational Inference for Neural Networks with
  Multiplicative Activation Noise
Sampling-free Variational Inference for Neural Networks with Multiplicative Activation Noise
Jannik Schmitt
Stefan Roth
UQCV
55
6
0
15 Mar 2021
MixMo: Mixing Multiple Inputs for Multiple Outputs via Deep Subnetworks
MixMo: Mixing Multiple Inputs for Multiple Outputs via Deep Subnetworks
Alexandre Ramé
Rémy Sun
Matthieu Cord
UQCV
108
60
0
10 Mar 2021
On complementing end-to-end human behavior predictors with planning
On complementing end-to-end human behavior predictors with planning
Liting Sun
Xiaogang Jia
Anca Dragan
77
17
0
09 Mar 2021
Pixel-wise Anomaly Detection in Complex Driving Scenes
Pixel-wise Anomaly Detection in Complex Driving Scenes
Giancarlo Di Biase
Hermann Blum
Roland Siegwart
Cesar Cadena
UQCV
80
156
0
09 Mar 2021
Active Testing: Sample-Efficient Model Evaluation
Active Testing: Sample-Efficient Model Evaluation
Jannik Kossen
Sebastian Farquhar
Y. Gal
Tom Rainforth
VLM
89
53
0
09 Mar 2021
Quantifying Ignorance in Individual-Level Causal-Effect Estimates under
  Hidden Confounding
Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding
Andrew Jesson
Sören Mindermann
Y. Gal
Uri Shalit
CML
90
56
0
08 Mar 2021
Adaptive-Control-Oriented Meta-Learning for Nonlinear Systems
Adaptive-Control-Oriented Meta-Learning for Nonlinear Systems
Spencer M. Richards
Navid Azizan
Jean-Jacques E. Slotine
Marco Pavone
86
73
0
07 Mar 2021
Contextual Dropout: An Efficient Sample-Dependent Dropout Module
Contextual Dropout: An Efficient Sample-Dependent Dropout Module
Xinjie Fan
Shujian Zhang
Korawat Tanwisuth
Xiaoning Qian
Mingyuan Zhou
OODBDLUQCV
89
31
0
06 Mar 2021
Global canopy height regression and uncertainty estimation from GEDI
  LIDAR waveforms with deep ensembles
Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles
Nico Lang
Nikolai Kalischek
J. Armston
Konrad Schindler
R. Dubayah
Jan Dirk Wegner
59
168
0
05 Mar 2021
SCRIB: Set-classifier with Class-specific Risk Bounds for Blackbox
  Models
SCRIB: Set-classifier with Class-specific Risk Bounds for Blackbox Models
Zhen Lin
Cao Xiao
Lucas Glass
M. P. M. Brandon Westover
Jimeng Sun
BDL
63
11
0
05 Mar 2021
An Application-Driven Conceptualization of Corner Cases for Perception
  in Highly Automated Driving
An Application-Driven Conceptualization of Corner Cases for Perception in Highly Automated Driving
Florian Heidecker
Jasmin Breitenstein
Kevin Rösch
Jonas Löhdefink
Maarten Bieshaar
Christoph Stiller
Tim Fingscheidt
Bernhard Sick
80
42
0
05 Mar 2021
Limits of Probabilistic Safety Guarantees when Considering Human
  Uncertainty
Limits of Probabilistic Safety Guarantees when Considering Human Uncertainty
Richard Cheng
R. Murray
J. W. Burdick
101
8
0
05 Mar 2021
A Kernel Framework to Quantify a Model's Local Predictive Uncertainty
  under Data Distributional Shifts
A Kernel Framework to Quantify a Model's Local Predictive Uncertainty under Data Distributional Shifts
Rishabh Singh
José C. Príncipe
UQCV
34
0
0
02 Mar 2021
Generative Particle Variational Inference via Estimation of Functional
  Gradients
Generative Particle Variational Inference via Estimation of Functional Gradients
Neale Ratzlaff
Qinxun Bai
Fuxin Li
Wenyuan Xu
BDLDRL
114
0
0
01 Mar 2021
Uncertainty Quantification by Ensemble Learning for Computational
  Optical Form Measurements
Uncertainty Quantification by Ensemble Learning for Computational Optical Form Measurements
L. Hoffmann
I. Fortmeier
Clemens Elster
UQCV
68
28
0
01 Mar 2021
Posterior Meta-Replay for Continual Learning
Posterior Meta-Replay for Continual Learning
Christian Henning
Maria R. Cervera
Francesco DÁngelo
J. Oswald
Regina Traber
Benjamin Ehret
Seijin Kobayashi
Benjamin Grewe
João Sacramento
CLLBDL
113
60
0
01 Mar 2021
Fool Me Once: Robust Selective Segmentation via Out-of-Distribution
  Detection with Contrastive Learning
Fool Me Once: Robust Selective Segmentation via Out-of-Distribution Detection with Contrastive Learning
David S. W. Williams
Matthew Gadd
D. Martini
Paul Newman
OODD
57
13
0
01 Mar 2021
Medical Image Segmentation with Limited Supervision: A Review of Deep
  Network Models
Medical Image Segmentation with Limited Supervision: A Review of Deep Network Models
Jialin Peng
Ye Wang
VLM
110
60
0
28 Feb 2021
Flexible Model Aggregation for Quantile Regression
Flexible Model Aggregation for Quantile Regression
Rasool Fakoor
Tae-Soo Kim
Jonas W. Mueller
Alexander J. Smola
Robert Tibshirani
92
21
0
26 Feb 2021
NOMU: Neural Optimization-based Model Uncertainty
NOMU: Neural Optimization-based Model Uncertainty
Jakob Heiss
Jakob Weissteiner
Hanna Wutte
Sven Seuken
Josef Teichmann
BDL
92
20
0
26 Feb 2021
Learning Prediction Intervals for Regression: Generalization and
  Calibration
Learning Prediction Intervals for Regression: Generalization and Calibration
Haoxian Chen
Ziyi Huang
Henry Lam
Huajie Qian
Haofeng Zhang
UQCV
67
20
0
26 Feb 2021
Loss Surface Simplexes for Mode Connecting Volumes and Fast Ensembling
Loss Surface Simplexes for Mode Connecting Volumes and Fast Ensembling
Gregory W. Benton
Wesley J. Maddox
Sanae Lotfi
A. Wilson
UQCV
126
70
0
25 Feb 2021
Simultaneously Reconciled Quantile Forecasting of Hierarchically Related
  Time Series
Simultaneously Reconciled Quantile Forecasting of Hierarchically Related Time Series
Xing Han
S. Dasgupta
Joydeep Ghosh
AI4TS
79
34
0
25 Feb 2021
Sketching Curvature for Efficient Out-of-Distribution Detection for Deep
  Neural Networks
Sketching Curvature for Efficient Out-of-Distribution Detection for Deep Neural Networks
Apoorva Sharma
Navid Azizan
Marco Pavone
UQCV
91
47
0
24 Feb 2021
Bayesian OOD detection with aleatoric uncertainty and outlier exposure
Bayesian OOD detection with aleatoric uncertainty and outlier exposure
Xi Wang
Laurence Aitchison
UD
87
15
0
24 Feb 2021
Parameterized Temperature Scaling for Boosting the Expressive Power in
  Post-Hoc Uncertainty Calibration
Parameterized Temperature Scaling for Boosting the Expressive Power in Post-Hoc Uncertainty Calibration
Christian Tomani
Daniel Cremers
Florian Buettner
UQCV
54
36
0
24 Feb 2021
Uncertainty-aware Generalized Adaptive CycleGAN
Uncertainty-aware Generalized Adaptive CycleGAN
Uddeshya Upadhyay
Yanbei Chen
Zeynep Akata
33
6
0
23 Feb 2021
Deep Deterministic Uncertainty: A Simple Baseline
Deep Deterministic Uncertainty: A Simple Baseline
Jishnu Mukhoti
Andreas Kirsch
Joost R. van Amersfoort
Philip Torr
Y. Gal
UDUQCVPERBDL
129
156
0
23 Feb 2021
Assigning Confidence to Molecular Property Prediction
Assigning Confidence to Molecular Property Prediction
AkshatKumar Nigam
R. Pollice
Matthew F. D. Hurley
Riley J. Hickman
Matteo Aldeghi
Naruki Yoshikawa
Seyone Chithrananda
Vincent A. Voelz
Alán Aspuru-Guzik
AI4CE
141
47
0
23 Feb 2021
Optimal Prediction Intervals for Macroeconomic Time Series Using Chaos
  and NSGA II
Optimal Prediction Intervals for Macroeconomic Time Series Using Chaos and NSGA II
Sarveswararao Vangala
V. Ravi
S. T. Huq
AI4TS
28
0
0
23 Feb 2021
On Feature Collapse and Deep Kernel Learning for Single Forward Pass
  Uncertainty
On Feature Collapse and Deep Kernel Learning for Single Forward Pass Uncertainty
Joost R. van Amersfoort
Lewis Smith
Andrew Jesson
Oscar Key
Y. Gal
UQCV
81
105
0
22 Feb 2021
On the Effects of Quantisation on Model Uncertainty in Bayesian Neural
  Networks
On the Effects of Quantisation on Model Uncertainty in Bayesian Neural Networks
Martin Ferianc
Partha P. Maji
Matthew Mattina
Miguel R. D. Rodrigues
UQCVBDL
72
10
0
22 Feb 2021
Improving Uncertainty Calibration via Prior Augmented Data
Improving Uncertainty Calibration via Prior Augmented Data
Jeffrey Willette
Juho Lee
Sung Ju Hwang
UQCV
36
0
0
22 Feb 2021
Synthesizing Irreproducibility in Deep Networks
Synthesizing Irreproducibility in Deep Networks
R. Snapp
G. Shamir
OOD
62
10
0
21 Feb 2021
Constrained Optimization to Train Neural Networks on Critical and
  Under-Represented Classes
Constrained Optimization to Train Neural Networks on Critical and Under-Represented Classes
Sara Sangalli
Ertunc Erdil
A. Hoetker
O. Donati
E. Konukoglu
AI4CE
74
28
0
21 Feb 2021
Bayesian Deep Learning for Segmentation for Autonomous Safe Planetary
  Landing
Bayesian Deep Learning for Segmentation for Autonomous Safe Planetary Landing
Kento Tomita
Katherine A. Skinner
K. Ho
UQCV
48
13
0
21 Feb 2021
Stronger NAS with Weaker Predictors
Stronger NAS with Weaker Predictors
Junru Wu
Xiyang Dai
Dongdong Chen
Yinpeng Chen
Mengchen Liu
Ye Yu
Zhangyang Wang
Zicheng Liu
Mei Chen
Lu Yuan
OOD
127
44
0
21 Feb 2021
Learning Neural Network Subspaces
Learning Neural Network Subspaces
Mitchell Wortsman
Maxwell Horton
Carlos Guestrin
Ali Farhadi
Mohammad Rastegari
UQCV
105
88
0
20 Feb 2021
Random Projections for Improved Adversarial Robustness
Random Projections for Improved Adversarial Robustness
Ginevra Carbone
G. Sanguinetti
Luca Bortolussi
AAML
63
2
0
18 Feb 2021
BORE: Bayesian Optimization by Density-Ratio Estimation
BORE: Bayesian Optimization by Density-Ratio Estimation
Louis C. Tiao
Aaron Klein
Matthias Seeger
Edwin V. Bonilla
Cédric Archambeau
F. Ramos
91
29
0
17 Feb 2021
Few-shot Conformal Prediction with Auxiliary Tasks
Few-shot Conformal Prediction with Auxiliary Tasks
Adam Fisch
Tal Schuster
Tommi Jaakkola
Regina Barzilay
375
56
0
17 Feb 2021
DEUP: Direct Epistemic Uncertainty Prediction
DEUP: Direct Epistemic Uncertainty Prediction
Salem Lahlou
Moksh Jain
Hadi Nekoei
V. Butoi
Paul Bertin
Jarrid Rector-Brooks
Maksym Korablyov
Yoshua Bengio
PERUQLMUQCVUD
321
94
0
16 Feb 2021
Hierarchical VAEs Know What They Don't Know
Hierarchical VAEs Know What They Don't Know
Jakob Drachmann Havtorn
J. Frellsen
Søren Hauberg
Lars Maaløe
DRL
116
74
0
16 Feb 2021
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