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Many important computer vision applications are naturally formulated as regression problems.
Regression quantiles
Roger Koenker and Gilbert Bassett Jr · 1978
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Bayesian learning for neural networks
Radford M Neal · 1995
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Inductive confidence machines for regression
Harris Papadopoulos, Kostas Proedrou, Volodya Vovk, and Alex Gammerman · 2002
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Classification with reject option
Radu Herbei and Marten H Wegkamp · 2006
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Probabilistic forecasts, calibration and sharpness
Tilmann Gneiting, Fadoua Balabdaoui, and Adrian E Raftery · 2007
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Inductive conformal prediction: Theory and application to neural networks
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Dataset shift in machine learning, 2009
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Adam: A method for stochastic optimization
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Weight uncertainty in neural network
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A complete recipe for stochastic gradient MCMC
Yi-An Ma, Tianqi Chen, and Emily Fox · 2015
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Uncertainty in Deep Learning
Yarin Gal · 2016
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Deep residual learning for image recognition
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Deep expectation of real and apparent age from a single image without facial landmarks
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Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger · 2017
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Alex Kendall and Yarin Gal · 2017
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A dataset and a technique for generalized nuclear segmentation for computational pathology
Neeraj Kumar, Ruchika Verma, Sanuj Sharma, Surabhi Bhargava, Abhishek Vahadane, and Amit Sethi · 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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Can semantic labeling methods generalize to any city? the Inria aerial image labeling benchmark
Emmanuel Maggiori, Yuliya Tarabalka, Guillaume Charpiat, and Pierre Alliez · 2017
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Direct multitype cardiac indices estimation via joint representation and regression learning
Wufeng Xue, Ali Islam, Mousumi Bhaduri, and Shuo Li · 2017
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Spyridon Bakas, Mauricio Reyes, Andras Jakab, Stefan Bauer, Markus Rempfler, Alessandro Crimi, Russell Takeshi Shinohara, Christoph Berger, Sung Min Ha, Martin Rozycki, et al · 2018
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Deep reinforcement learning in a handful of trials using probabilistic dynamics models
Kurtland Chua, Roberto Calandra, Rowan McAllister, and Sergey Levine · 2018
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Eddy Ilg, Ozgun Cicek, Silvio Galesso, Aaron Klein, Osama Makansi, Frank Hutter, and Thomas Bro · 2018
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Accurate uncertainties for deep learning using calibrated regression
Volodymyr Kuleshov, Nathan Fenner, and Stefano Ermon · 2018
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Hei Law and Jia Deng · 2018
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Input complexity and out-of-distribution detection with likelihood-based generative models
Joan Serrà, David Álvarez, Vicenç Gómez, Olga Slizovskaia, José F. Núñez, and Jordi Luque · 2020
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Wen Shi, Guohui Yan, Yamin Li, Haotian Li, Tingting Liu, Cong Sun, Guangbin Wang, Yi Zhang, Yu Zou, and Dan Wu · 2020
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Using publicly available satellite imagery and deep learning to understand economic well-being in Africa
Christopher Yeh, Anthony Perez, Anne Driscoll, George Azzari, Zhongyi Tang, David Lobell, Stefano Ermon, and Marshall Burke · 2020
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Beyond pinball loss: Quantile methods for calibrated uncertainty quantification
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Spectral normalization for generative adversarial networks
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Peter Naylor, Marick Laé, Fabien Reyal, and Thomas Walter · 2018
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Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning
Ryan Poplin, Avinash V Varadarajan, Katy Blumer, Yun Liu, Michael V McConnell, Greg S Corrado, Lily Peng, and Dale R Webster · 2018
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The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions
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Uncertainty-aware body composition analysis with deep regression ensembles on uk biobank mri
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The medical segmentation decathlon
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Calibrated selective classification
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Pixmix: Dreamlike pictures comprehensively improve safety measures
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Ensemble conformalized quantile regression for probabilistic time series forecasting
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Out-of-distribution detection with deep nearest neighbors
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Assaying out-of-distribution generalization in transfer learning
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