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Evidential Deep Learning to Quantify Classification Uncertainty
v1v2v3 (latest)

Evidential Deep Learning to Quantify Classification Uncertainty

5 June 2018
Murat Sensoy
Lance M. Kaplan
M. Kandemir
    OODUQCVEDLBDL
ArXiv (abs)PDFHTML

Papers citing "Evidential Deep Learning to Quantify Classification Uncertainty"

50 / 572 papers shown
Title
Deep Learning meets Liveness Detection: Recent Advancements and
  Challenges
Deep Learning meets Liveness Detection: Recent Advancements and Challenges
Arian Sabaghi
Marzieh Oghbaie
Kooshan Hashemifard
Mohammad Akbari
AAML
174
10
0
29 Dec 2021
Improving evidential deep learning via multi-task learning
Improving evidential deep learning via multi-task learning
Dongpin Oh
Bonggun Shin
EDLUQCV
176
31
0
17 Dec 2021
Automatic quality control framework for more reliable integration of
  machine learning-based image segmentation into medical workflows
Automatic quality control framework for more reliable integration of machine learning-based image segmentation into medical workflows
Elena Williams
Sebastian Niehaus
J. Reinelt
A. Merola
P. Mihai
...
Evelyn Medawar
Daniel Lichterfeld
Ingo Roeder
N. Scherf
Maria del C. Valdés Hernández
220
4
0
06 Dec 2021
Towards Interactive Reinforcement Learning with Intrinsic Feedback
Towards Interactive Reinforcement Learning with Intrinsic Feedback
Ben Poole
Minwoo Lee
OffRL
245
2
0
02 Dec 2021
A Unified Benchmark for the Unknown Detection Capability of Deep Neural
  Networks
A Unified Benchmark for the Unknown Detection Capability of Deep Neural Networks
Jihyo Kim
Jiin Koo
Sangheum Hwang
UQCV
291
23
0
01 Dec 2021
FROB: Few-shot ROBust Model for Classification and Out-of-Distribution
  Detection
FROB: Few-shot ROBust Model for Classification and Out-of-Distribution Detection
Nikolaos Dionelis
Mehrdad Yaghoobi
Sotirios A. Tsaftaris
OODD
202
4
0
30 Nov 2021
Trustworthy Long-Tailed Classification
Trustworthy Long-Tailed Classification
Bolian Li
Zongbo Han
Haining Li
Huazhu Fu
Changqing Zhang
EDL
148
86
0
17 Nov 2021
Trustworthy Multimodal Regression with Mixture of Normal-inverse Gamma
  Distributions
Trustworthy Multimodal Regression with Mixture of Normal-inverse Gamma DistributionsNeural Information Processing Systems (NeurIPS), 2021
Huan Ma
Zongbo Han
Changqing Zhang
Huazhu Fu
Qiufeng Wang
Q. Hu
EDLUQCV
232
55
0
11 Nov 2021
Gated Linear Model induced U-net for surrogate modeling and uncertainty
  quantification
Gated Linear Model induced U-net for surrogate modeling and uncertainty quantification
Sai Krishna Mendu
S. Chakraborty
BDLAI4CE
153
2
0
08 Nov 2021
Graph Posterior Network: Bayesian Predictive Uncertainty for Node
  Classification
Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification
Maximilian Stadler
Bertrand Charpentier
Simon Geisler
Daniel Zügner
Stephan Günnemann
UQCVBDL
292
103
0
26 Oct 2021
Addressing out-of-distribution label noise in webly-labelled data
Addressing out-of-distribution label noise in webly-labelled data
Paul Albert
Diego Ortego
Eric Arazo
Noel E. O'Connor
Kevin McGuinness
NoLa
180
22
0
26 Oct 2021
Robust Monocular Localization in Sparse HD Maps Leveraging Multi-Task
  Uncertainty Estimation
Robust Monocular Localization in Sparse HD Maps Leveraging Multi-Task Uncertainty EstimationIEEE International Conference on Robotics and Automation (ICRA), 2021
Kürsat Petek
Kshitij Sirohi
Daniel Buscher
Wolfram Burgard
UQCV
236
25
0
20 Oct 2021
Utilizing Active Machine Learning for Quality Assurance: A Case Study of
  Virtual Car Renderings in the Automotive Industry
Utilizing Active Machine Learning for Quality Assurance: A Case Study of Virtual Car Renderings in the Automotive Industry
Patrick Hemmer
Niklas Kühl
Jakob Schöffer
96
5
0
18 Oct 2021
Identifying Incorrect Classifications with Balanced Uncertainty
Identifying Incorrect Classifications with Balanced Uncertainty
Bolian Li
Zige Zheng
Changqing Zhang
UQCV
146
3
0
15 Oct 2021
MetaCOG: A Hierarchical Probabilistic Model for Learning Meta-Cognitive
  Visual Representations
MetaCOG: A Hierarchical Probabilistic Model for Learning Meta-Cognitive Visual Representations
Marlene D. Berke
Zhangir Azerbayev
M. Belledonne
Zenna Tavares
J. Jara-Ettinger
196
1
0
06 Oct 2021
Prior and Posterior Networks: A Survey on Evidential Deep Learning
  Methods For Uncertainty Estimation
Prior and Posterior Networks: A Survey on Evidential Deep Learning Methods For Uncertainty Estimation
Dennis Ulmer
Christian Hardmeier
J. Frellsen
BDLUQCVUDEDLPER
312
76
0
06 Oct 2021
Deep Classifiers with Label Noise Modeling and Distance Awareness
Deep Classifiers with Label Noise Modeling and Distance Awareness
Vincent Fortuin
Mark Collier
F. Wenzel
J. Allingham
J. Liu
Dustin Tran
Balaji Lakshminarayanan
Jesse Berent
Rodolphe Jenatton
E. Kokiopoulou
UQCV
238
12
0
06 Oct 2021
CertainNet: Sampling-free Uncertainty Estimation for Object Detection
CertainNet: Sampling-free Uncertainty Estimation for Object Detection
Stefano Gasperini
Johannes Haug
M. N. Mahani
Alvaro Marcos-Ramiro
Nassir Navab
Benjamin Busam
F. Tombari
UQCV
114
27
0
04 Oct 2021
Trust Your Robots! Predictive Uncertainty Estimation of Neural Networks
  with Sparse Gaussian Processes
Trust Your Robots! Predictive Uncertainty Estimation of Neural Networks with Sparse Gaussian Processes
Jongseo Lee
Jianxiang Feng
Matthias Humt
M. Müller
Rudolph Triebel
UQCV
259
24
0
20 Sep 2021
Reliable Neural Networks for Regression Uncertainty Estimation
Reliable Neural Networks for Regression Uncertainty Estimation
Tony Tohme
Kevin Vanslette
K. Youcef-Toumi
UQCVBDL
191
15
0
16 Sep 2021
Uncertainty-Aware Machine Translation Evaluation
Uncertainty-Aware Machine Translation Evaluation
T. Glushkova
Chrysoula Zerva
Ricardo Rei
André F.T. Martins
UQLM
309
49
0
13 Sep 2021
Boosting Cross-Lingual Transfer via Self-Learning with Uncertainty
  Estimation
Boosting Cross-Lingual Transfer via Self-Learning with Uncertainty EstimationConference on Empirical Methods in Natural Language Processing (EMNLP), 2021
Liyan Xu
Xuchao Zhang
Xujiang Zhao
Haifeng Chen
F. Chen
Jinho Choi
121
15
0
01 Sep 2021
Sparse Communication via Mixed Distributions
Sparse Communication via Mixed DistributionsInternational Conference on Learning Representations (ICLR), 2021
António Farinhas
Wilker Aziz
Vlad Niculae
André F. T. Martins
163
3
0
05 Aug 2021
Triggering Failures: Out-Of-Distribution detection by learning from
  local adversarial attacks in Semantic Segmentation
Triggering Failures: Out-Of-Distribution detection by learning from local adversarial attacks in Semantic Segmentation
Victor Besnier
Andrei Bursuc
David Picard
Alexandre Briot
UQCV
217
53
0
03 Aug 2021
Robust Semantic Segmentation with Superpixel-Mix
Robust Semantic Segmentation with Superpixel-Mix
Gianni Franchi
Nacim Belkhir
Mai Lan Ha
Yufei Hu
Andrei Bursuc
V. Blanz
Angela Yao
UQCV
181
24
0
02 Aug 2021
Evidential Deep Learning for Open Set Action Recognition
Evidential Deep Learning for Open Set Action RecognitionIEEE International Conference on Computer Vision (ICCV), 2021
Wentao Bao
Qi Yu
Yu Kong
CMLEDL
326
179
0
21 Jul 2021
Vision-Based Autonomous Car Racing Using Deep Imitative Reinforcement
  Learning
Vision-Based Autonomous Car Racing Using Deep Imitative Reinforcement LearningIEEE Robotics and Automation Letters (RA-L), 2021
Peide Cai
Hengli Wang
Huaiyang Huang
Yuxuan Liu
Ming-Yuan Liu
120
73
0
18 Jul 2021
Uncertainty-Aware Reliable Text Classification
Uncertainty-Aware Reliable Text ClassificationKnowledge Discovery and Data Mining (KDD), 2021
Yibo Hu
Latifur Khan
EDLUQCV
147
36
0
15 Jul 2021
A Survey of Uncertainty in Deep Neural Networks
A Survey of Uncertainty in Deep Neural Networks
J. Gawlikowski
Cedrique Rovile Njieutcheu Tassi
Mohsin Ali
Jongseo Lee
Matthias Humt
...
R. Roscher
Muhammad Shahzad
Wen Yang
R. Bamler
Xiaoxiang Zhu
BDLUQCVOOD
527
1,457
0
07 Jul 2021
Logit-based Uncertainty Measure in Classification
Logit-based Uncertainty Measure in Classification
Huiyue Wu
Diego Klabjan
EDLBDLUQCV
94
7
0
06 Jul 2021
Detecting Concept Drift With Neural Network Model Uncertainty
Detecting Concept Drift With Neural Network Model Uncertainty
Lucas Baier
Tim Schlör
Jakob Schöffer
Niklas Kühl
172
34
0
05 Jul 2021
Leveraging Graph and Deep Learning Uncertainties to Detect Anomalous
  Trajectories
Leveraging Graph and Deep Learning Uncertainties to Detect Anomalous Trajectories
S. Singh
Jaya Shradha Fowdur
J. Gawlikowski
Daniel Medina
166
2
0
04 Jul 2021
Scene Uncertainty and the Wellington Posterior of Deterministic Image
  Classifiers
Scene Uncertainty and the Wellington Posterior of Deterministic Image Classifiers
Stephanie Tsuei
Aditya Golatkar
Stefano Soatto
UQCV
137
0
0
25 Jun 2021
FF-NSL: Feed-Forward Neural-Symbolic Learner
FF-NSL: Feed-Forward Neural-Symbolic Learner
Daniel Cunnington
Mark Law
A. Russo
Jorge Lobo
NAI
223
17
0
24 Jun 2021
Graceful Degradation and Related Fields
Graceful Degradation and Related Fields
J. Dymond
180
5
0
21 Jun 2021
Trust It or Not: Confidence-Guided Automatic Radiology Report Generation
Trust It or Not: Confidence-Guided Automatic Radiology Report Generation
Yixin Wang
Zihao Lin
Zhe Xu
Haoyu Dong
Jiang Tian
Jie Luo
Peng Wang
Yang Zhang
Jianping Fan
Zhiqiang He
UQCVMedIm
245
15
0
21 Jun 2021
Being a Bit Frequentist Improves Bayesian Neural Networks
Being a Bit Frequentist Improves Bayesian Neural NetworksInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2021
Agustinus Kristiadi
Matthias Hein
Philipp Hennig
BDLUQCV
184
17
0
18 Jun 2021
Provably Robust Detection of Out-of-distribution Data (almost) for free
Provably Robust Detection of Out-of-distribution Data (almost) for freeNeural Information Processing Systems (NeurIPS), 2021
Alexander Meinke
Julian Bitterwolf
Matthias Hein
OODD
170
21
0
08 Jun 2021
Can a single neuron learn predictive uncertainty?
Can a single neuron learn predictive uncertainty?
Edgardo Solano-Carrillo
UQCV
229
1
0
07 Jun 2021
Evidential Turing Processes
Evidential Turing ProcessesInternational Conference on Learning Representations (ICLR), 2021
M. Kandemir
Abdullah Akgul
Manuel Haussmann
Gözde B. Ünal
EDLUQCVBDL
159
10
0
02 Jun 2021
Classification and Uncertainty Quantification of Corrupted Data using
  Semi-Supervised Autoencoders
Classification and Uncertainty Quantification of Corrupted Data using Semi-Supervised Autoencoders
Philipp Joppich
S. Dorn
Oliver De Candido
Wolfgang Utschick
Jakob Knollmuller
132
2
0
27 May 2021
Efficient and Robust LiDAR-Based End-to-End Navigation
Efficient and Robust LiDAR-Based End-to-End NavigationIEEE International Conference on Robotics and Automation (ICRA), 2021
Zhijian Liu
Alexander Amini
Sibo Zhu
S. Karaman
Song Han
Daniela Rus
419
56
0
20 May 2021
APPL: Adaptive Planner Parameter Learning
APPL: Adaptive Planner Parameter Learning
Xuesu Xiao
Zizhao Wang
Zifan Xu
Bo Liu
Garrett A. Warnell
Gauraang Dhamankar
Anirudh Nair
Peter Stone
303
61
0
17 May 2021
Probabilistic Rainfall Estimation from Automotive Lidar
Probabilistic Rainfall Estimation from Automotive Lidar
Robin Karlsson
D. Wong
Kazunari Kawabata
S. Thompson
N. Sakai
195
12
0
23 Apr 2021
Uncertainty Surrogates for Deep Learning
Uncertainty Surrogates for Deep Learning
R. Achanta
Natasa Tagasovska
OODUQCV
96
0
0
16 Apr 2021
Multivariate Deep Evidential Regression
Multivariate Deep Evidential Regression
N. Meinert
Alexander Lavin
BDLPEREDLUQCV
370
28
0
13 Apr 2021
Robust Vision-Based Cheat Detection in Competitive Gaming
Robust Vision-Based Cheat Detection in Competitive GamingProceedings of the ACM on Computer Graphics and Interactive Techniques (PACMCGIT), 2021
Aditya Jonnalagadda
I. Frosio
Seth Schneider
M. McGuire
Joohwan Kim
AAML
159
22
0
18 Mar 2021
Medical Imaging and Machine Learning
Medical Imaging and Machine LearningNature Machine Intelligence (Nat. Mach. Intell.), 2021
R. Shad
John P. Cunningham
Euan A. Ashley
C. Langlotz
W. Hiesinger
175
46
0
02 Mar 2021
A Simulation-based End-to-End Learning Framework for Evidential
  Occupancy Grid Mapping
A Simulation-based End-to-End Learning Framework for Evidential Occupancy Grid Mapping
Raphael van Kempen
Bastian Lampe
Timo Woopen
L. Eckstein
185
13
0
25 Feb 2021
Handling Epistemic and Aleatory Uncertainties in Probabilistic Circuits
Handling Epistemic and Aleatory Uncertainties in Probabilistic CircuitsMachine-mediated learning (ML), 2021
Federico Cerutti
Lance M. Kaplan
Angelika Kimmig
Murat Sensoy
TPM
121
16
0
22 Feb 2021
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