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We propose a novel confidence scoring mechanism for deep neural networks based on a two-model paradigm involving a base model and a meta-model.
Greedy function approximation: a gradient boosting machine
Jerome H Friedman. 2001 · 2001
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Obtaining calibrated probability estimates from decision trees and naive bayesian classifiers
Bianca Zadrozny and Charles Elkan. 2001 · 2001
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DETAC - a discriminative criterion for speaker verification
J. Navrátil and G.N. Ramaswamy. 2002 · 2002
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Transforming classifier scores into accurate multiclass probability estimates
Bianca Zadrozny and Charles Elkan. 2002 · 2002
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Third Workshop on ROC Analysis in ML , ICML Workshop
ICML Workshop. 2006 · 2006
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An experimental comparison of performance measures for classification
C. Ferri, J. Hernández-Orallo, and R. Modroiu. 2009 · 2009
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Calibrating predictive model estimates to support personalized medicine
Xiaoqian Jiang, Melanie Osl, Jihoon Kim, and Lucila Ohno-Machado. 2011 · 2011
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Statistical evaluation of diagnostic performance: topics in ROC analysis
Kelly H Zou, Aiyi Liu, Andriy I Bandos, Lucila Ohno-Machado, and Howard E Rockette. 2011 · 2011
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Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission
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Tensorflow: A system for large-scale machine learning
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Understanding intermediate layers using linear classifier probes
Guillaume Alain and Yoshua Bengio. 2016 · 2016
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End to end learning for self-driving cars
Mariusz Bojarski, Davide Del Testa, Daniel Dworakowski, Bernhard Firner, Beat Flepp, Prasoon Goyal, Lawrence D Jackel, Mathew Monfort, Urs Muller, Jiakai Zhang, et al. 2016 · 2016
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani. 2016 · 2016
Sergey Zagoruyko and Nikos Komodakis. 2016 · 2016
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Yarin Gal, Jiri Hron, and Alex Kendall. 2017 · 2017
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Selective classification for deep neural networks
Yonatan Geifman and Ran El-Yaniv. 2017 · 2017
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On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q. Weinberger. 2017 · 2017
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Uncertainty-aware reinforcement learning for collision avoidance
Gregory Kahn, Adam Villaflor, Vitchyr Pong, Pieter Abbeel, and Sergey Levine. 2017 · 2017
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Semantic segmentation of small objects and modeling of uncertainty in urban remote sensing images using deep convolutional neural networks
Michael Kampffmeyer, Arnt-Borre Salberg, and Robert Jenssen. 2016 · 2016
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Modeling uncertainty in deep learning for camera relocalization
Alex Kendall and Roberto Cipolla. 2016 · 2016
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Auc-maximized deep convolutional neural fields for protein sequence labeling
Sheng Wang, Siqi Sun, and Jinbo Xu. 2016 · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016a
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Identity mappings in deep residual networks
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016b
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What uncertainties do we need in bayesian deep learning for computer vision?
Alex Kendall and Yarin Gal. 2017 · 2017
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Leveraging uncertainty information from deep neural networks for disease detection
Christian Leibig, Vaneeda Vaneeda Allken, Murat Seckin Ayhan, Philipp Berens, and Siegfried Wahl Wahl. 2017 · 2017
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PE 16-007
National Highway Traffic Safety Administration. 2017 · 2017
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