Fetching the paper…
Reading the bibliography…
Accurately estimating uncertainties in neural network predictions is of great importance in building trusted DNNs-based models, and there is an increasing interest in providing accurate uncertainty estimation on many tasks, such as security cameras and autonomous driving vehicles.
The relationship between precision-recall and roc curves
Jesse Davis and Mark Goadrich · 2006
Earlier work this paper cites.
Increasing the reliability of reliability diagrams
Jochen Bröcker and Leonard A Smith · 2007
Earlier work this paper cites.
Strictly proper scoring rules, prediction, and estimation
Tilmann Gneiting and Adrian E Raftery · 2007
Earlier work this paper cites.
Verification of the weather channel probability of precipitation forecasts
J Eric Bickel and Seong Dae Kim · 2008
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
Earlier work this paper cites.
Evaluating probability forecasts
Tze Leung Lai, Shulamith T Gross, David Bo Shen, et al · 2011
Earlier work this paper cites.
Forecast verification: a practitioner’s guide in atmospheric science
Ian T Jolliffe and David B Stephenson · 2012
Earlier work this paper cites.
Choosing a strictly proper scoring rule
Edgar C Merkle and Mark Steyvers · 2013
Earlier work this paper cites.
Obtaining well calibrated probabilities using bayesian binning
Mahdi Pakdaman Naeini, Gregory Cooper, and Milos Hauskrecht · 2015
Earlier work this paper cites.
U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
Earlier work this paper cites.
Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani · 2016
Earlier work this paper cites.
A baseline for detecting misclassified and out-of-distribution examples in neural networks
Dan Hendrycks and Kevin Gimpel · 2016
Earlier work this paper cites.
Sergey Zagoruyko and Nikos Komodakis · 2016
Earlier work this paper cites.
Selective classification for deep neural networks
Yonatan Geifman and Ran El-Yaniv · 2017
Earlier work this paper cites.
On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger · 2017
Earlier work this paper cites.
What do we need to build explainable ai systems for the medical domain?
Andreas Holzinger, Chris Biemann, Constantinos S Pattichis, and Douglas B Kell · 2017
Cited alongside, same era.
Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
Cited alongside, same era.
What uncertainties do we need in bayesian deep learning for computer vision?
Alex Kendall and Yarin Gal · 2017
Cited alongside, same era.
Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
Cited alongside, same era.
Confident multiple choice learning
Kimin Lee, Changho Hwang, Kyoung Soo Park, and Jinwoo Shin · 2017
Cited alongside, same era.
Training confidence-calibrated classifiers for detecting out-of-distribution samples
Trainable calibration measures for neural networks from kernel mean embeddings
Aviral Kumar, Sunita Sarawagi, and Ujjwal Jain · 2018
Later among the works it cites.
Predictive uncertainty estimation via prior networks
Andrey Malinin and Mark Gales · 2018
Later among the works it cites.
Exploring uncertainty measures in deep networks for multiple sclerosis lesion detection and segmentation
Tanya Nair, Doina Precup, Douglas L Arnold, and Tal Arbel · 2018
Later among the works it cites.
Confidence calibration in deep neural networks through stochastic inferences
Seonguk Seo, Paul Hongsuck Seo, and Bohyung Han · 2018
Later among the works it cites.
Estimation of the continuous ranked probability score with limited information and applications to ensemble weather forecasts
Michaël Zamo and Philippe Naveau · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Kimin Lee, Honglak Lee, Kibok Lee, and Jinwoo Shin · 2017
Cited alongside, same era.
Enhancing the reliability of out-of-distribution image detection in neural networks
Shiyu Liang, Yixuan Li, and R Srikant · 2017
Cited alongside, same era.
Distance-based confidence score for neural network classifiers
Amit Mandelbaum and Daphna Weinshall · 2017
Cited alongside, same era.
Confidence scoring using whitebox meta-models with linear classifier probes
Tongfei Chen, Jiří Navrátil, Vijay Iyengar, and Karthikeyan Shanmugam · 2018
Cited alongside, same era.
Learning confidence for out-of-distribution detection in neural networks
Terrance DeVries and Graham W Taylor · 2018
Cited alongside, same era.
Leveraging uncertainty estimates for predicting segmentation quality
Terrance DeVries and Graham W Taylor · 2018
Cited alongside, same era.
Reducing network agnostophobia
Akshay Raj Dhamija, Manuel Günther, and Terrance Boult · 2018
Cited alongside, same era.
Yonatan Geifman and Ran El-Yaniv · 2019
Closest in time.
Deep gamblers: Learning to abstain with portfolio theory
Ziyin Liu, Zhikang Wang, Paul Pu Liang, Russ R Salakhutdinov, Louis-Philippe Morency, and Masahito Ueda · 2019
Closest in time.
Measuring calibration in deep learning
Jeremy Nixon, Mike Dusenberry, Linchuan Zhang, Ghassen Jerfel, and Dustin Tran · 2019
Closest in time.
Bayesian quicknat: model uncertainty in deep whole-brain segmentation for structure-wise quality control
Abhijit Guha Roy, Sailesh Conjeti, Nassir Navab, Christian Wachinger, Alzheimer’s Disease Neuroimaging Initiative, et al · 2019
Closest in time.
Christos Sakaridis, Dengxin Dai, and Luc Van Gool · 2019
Closest in time.
Towards increased trustworthiness of deep learning segmentation methods on cardiac mri
Jörg Sander, Bob D de Vos, Jelmer M Wolterink, and Ivana Išgum · 2019
Closest in time.
Avanti Shrikumar and Anshul Kundaje · 2019
Closest in time.
Can you trust your model’s uncertainty? evaluating predictive uncertainty under dataset shift
Jasper Snoek, Yaniv Ovadia, Emily Fertig, Balaji Lakshminarayanan, Sebastian Nowozin, D Sculley, Joshua Dillon, Jie Ren, and Zachary Nado · 2019
Closest in time.
Evaluating model calibration in classification
Juozas Vaicenavicius, David Widmann, Carl Andersson, Fredrik Lindsten, Jacob Roll, and Thomas B Schön · 2019
Closest in time.
Evaluation of algorithms for multi-modality whole heart segmentation: An open-access grand challenge
Xiahai Zhuang, Lei Li, Christian Payer, Darko Štern, Martin Urschler, Mattias P Heinrich, Julien Oster, Chunliang Wang, Örjan Smedby, Cheng Bian, et al · 2019
Closest in time.