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When deployed for risk-sensitive tasks, deep neural networks must include an uncertainty estimation mechanism.
Selectivenet: A deep neural network with an integrated reject option
Yonatan Geifman and Ran El-Yaniv · 1901
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Yukun Ding, Jinglan Liu, Jinjun Xiong, and Yiyu Shi · 1903
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Andrew Howard, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang, Yukun Zhu, Ruoming Pang, Vijay Vasudevan, Quoc V. Le, and Hartwig Adam · 1905
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Verification of Forecasts Expressed in Terms of Probability
Glenn W. Brier · 1950
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Measures of association for cross classifications
Leo A. Goodman and William H. Kruskal · 1954
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An optimum character recognition system using decision functions
C. K. Chow · 1957
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A comparison of current measures of the accuracy of feeling-of-knowing predictions
Thomas O. Nelson · 1984
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A method for improving classification reliability of multilayer perceptrons
L. P. Cordella, C. De Stefano, F. Tortorella, and M. Vento · 1995
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To reject or not to reject: that is the question-an answer in case of neural classifiers
C. De Stefano, C. Sansone, and M. Vento · 2000
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Knapsack pruning with inner distillation
Yonathan Aflalo, Asaf Noy, Ming Lin, Itamar Friedman, and Lihi Zelnik-Manor · 2002
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An introduction to roc analysis
Tom Fawcett · 2005
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On the foundations of noise-free selective classification
Ran El-Yaniv and Yair Wiener · 2010
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Metacognition and system usability: Incorporating metacognitive research paradigm into usability testing
Rakefet Ackerman, Avi Parush, Fareda Nassar, and Avraham Shtub · 2015
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Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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Obtaining well calibrated probabilities using bayesian binning
Mahdi Pakdaman Naeini, Gregory F. Cooper, and Milos Hauskrecht · 2015
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Selective classification for deep neural networks
Yonatan Geifman and Ran El-Yaniv · 2017
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On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q. Weinberger · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
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Using metacognitive methods to examine emotion recognition in children with ADHD
Alexandra Basile, Maggie E. Toplak, and Brendan F. Andrade · 2018
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The lottery ticket hypothesis: Training pruned neural networks
Jonathan Frankle and Michael Carbin · 2018
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Bias-reduced uncertainty estimation for deep neural classifiers
Yonatan Geifman, Guy Uziel, and Ran El-Yaniv · 2018
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New improved gamma: Enhancing the accuracy of goodman–kruskal’s gamma using ROC curves
Philip A. Higham and D. Paul Higham · 2018
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Neural architecture design for gpu-efficient networks, 2020
Ming Lin, Hesen Chen, Xiuyu Sun, Qi Qian, Hao Li, and Rong Jin · 2020
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Confidence-aware learning for deep neural networks
Jooyoung Moon, Jihyo Kim, Younghak Shin, and Sangheum Hwang · 2020
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Adversarial examples improve image recognition
Cihang Xie, Mingxing Tan, Boqing Gong, Jiang Wang, Alan L. Yuille, and Quoc V. Le · 2020
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Self-training with noisy student improves imagenet classification
Qizhe Xie, Minh-Thang Luong, Eduard H. Hovy, and Quoc V. Le · 2020
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Individual calibration with randomized forecasting
Shengjia Zhao, Tengyu Ma, and Stefano Ermon · 2020
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Xcit: Cross-covariance image transformers
Alaaeldin Ali, Hugo Touvron, Mathilde Caron, Piotr Bojanowski, Matthijs Douze, Armand Joulin, Ivan Laptev, Natalia Neverova, Gabriel Synnaeve, Jakob Verbeek, and Hervé Jégou · 2021
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Dhruv Mahajan, Ross B. Girshick, Vignesh Ramanathan, Kaiming He, Manohar Paluri, Yixuan Li, Ashwin Bharambe, and Laurens van der Maaten · 2018
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Ensemble adversarial training: Attacks and defenses
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! metacognition : Monitoring and controlling one ’ s own knowledge , reasoning and decisions
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An image is worth 16x16 words: Transformers for image recognition at scale
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Hardcore-nas: Hard constrained differentiable neural architecture search
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Learning transferable visual models from natural language supervision
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When vision transformers outperform resnets without pre-training or strong data augmentations
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Revisiting weakly supervised pre-training of visual perception models
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How to train your vit? data, augmentation, and regularization in vision transformers
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Plex: Towards reliability using pretrained large model extensions
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A framework for benchmarking class-out-of-distribution detection and its application to imagenet
Ido Galil, Mohammed Dabbah, and Ran El-Yaniv · 2023
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