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Prior Networks are a recently developed class of models which yield interpretable measures of uncertainty and have been shown to outperform state-of-the-art ensemble approaches on a range of tasks.
Pattern recognition and machine learning
Christopher M Bishop · 2006
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Elements of information theory
Thomas M Cover and Joy A Thomas · 2006
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Parametric bayesian estimation of differential entropy and relative entropy
Maya Gupta and Santosh Srivastava · 2010
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Recurrent Neural Network Based Language Model
Tomas Mikolov, Martin Karafiát, Lukás Burget, Jan Cernocký, and Sanjeev Khudanpur · 2010
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Deep neural networks for acoustic modeling in speech recognition
Geoffrey Hinton, Li Deng, Dong Yu, George Dahl, Abdel rahman Mohamed, Navdeep Jaitly, Andrew Senior, Vincent Vanhoucke, Patrick Nguyen, Tara Sainath, and Brian Kingsbury · 2012
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Machine Learning
Kevin P. Murphy · 2012
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Indoor segmentation and support inference from rgbd images
Pushmeet Kohli Nathan Silberman, Derek Hoiem and Rob Fergus · 2012
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Shannon entropy and mutual information for multivariate skew-elliptical distributions
REINALDO B ARELLANO-VALLE, JAVIER E CONTRERAS-REYES, and Marc G Genton · 2013
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Vision meets robotics: The kitti dataset
Andreas Geiger, Philip Lenz, Christoph Stiller, and Raquel Urtasun · 2013
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Depth map prediction from a single image using a multi-scale deep network
David Eigen, Christian Puhrsch, and Rob Fergus · 2014
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Deep speech: Scaling up end-to-end speech recognition, 2014
Awni Y. Hannun, Carl Case, Jared Casper, Bryan Catanzaro, Greg Diamos, Erich Elsen, Ryan Prenger, Sanjeev Satheesh, Shubho Sengupta, Adam Coates, and Andrew Y. Ng · 2014
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Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning
Babak Alipanahi, Andrew Delong, Matthew T. Weirauch, and Brendan J. Frey · 2015
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Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission
Rich Caruana, Yin Lou, Johannes Gehrke, Paul Koch, Marc Sturm, and Noemie Elhadad · 2015
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Fast R-CNN
Ross Girshick · 2015
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeffrey Dean · 2015
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Object scene flow for autonomous vehicles
Moritz Menze and Andreas Geiger · 2015
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Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan and Andrew Zisserman · 2015
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LSUN: construction of a large-scale image dataset using deep learning with humans in the loop, 2015
Fisher Yu, Yinda Zhang, Shuran Song, Ari Seff, and Jianxiong Xiao · 2015
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Concrete problems in AI safety
Dario Amodei, Chris Olah, Jacob Steinhardt, Paul F. Christiano, John Schulman, and Dan Mané · 2016
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Understanding Measures of Uncertainty for Adversarial Example Detection
L. Smith and Y. Gal · 2018
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Deep ensembles: A loss landscape perspective
Stanislav Fort, Huiyi Hu, and Balaji Lakshminarayanan · 2019
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Batchbald: Efficient and diverse batch acquisition for deep bayesian active learning, 2019
Andreas Kirsch, Joost van Amersfoort, and Yarin Gal · 2019
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A simple baseline for bayesian uncertainty in deep learning
Wesley Maddox, Timur Garipov, Pavel Izmailov, Dmitry Vetrov, and Andrew Gordon Wilson · 2019
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Uncertainty Estimation in Deep Learning with application to Spoken Language Assessment
Andrey Malinin · 2019
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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 learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
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Adversarial examples are not easily detected: Bypassing ten detection methods
Nicholas Carlini and David A. Wagner · 2017
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Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
B. Lakshminarayanan, A. Pritzel, and C. Blundell · 2017
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Learning to Generate Long-term Future via Hierarchical Prediction
Ruben Villegas, Jimei Yang, Yuliang Zou, Sungryull Sohn, Xunyu Lin, and Honglak Lee · 2017
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High quality monocular depth estimation via transfer learning
Ibraheem Alhashim and Peter Wonka · 2018
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Deep ordinal regression network for monocular depth estimation
Huan Fu, Mingming Gong, Chaohui Wang, Kayhan Batmanghelich, and Dacheng Tao · 2018
Cited alongside, same era.
Reverse kl-divergence training of prior networks: Improved uncertainty and adversarial robustness
Andrey Malinin and Mark JF Gales · 2019
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Can you trust your model’s uncertainty? evaluating predictive uncertainty under dataset shift
Yaniv Ovadia, Emily Fertig, Jie Ren, Zachary Nado, D Sculley, Sebastian Nowozin, Joshua V Dillon, Balaji Lakshminarayanan, and Jasper Snoek · 2019
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Deep evidential regression
Alexander Amini, Wilko Schwarting, Ava Soleimany, and Daniela Rus · 2020
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Pitfalls of in-domain uncertainty estimation and ensembling in deep learning
Arsenii Ashukha, Alexander Lyzhov, Dmitry Molchanov, and Dmitry Vetrov · 2020
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Ensemble distribution distillation
Andrey Malinin, Bruno Mlodozeniec, and Mark JF Gales · 2020
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Hydra: Preserving ensemble diversity for model distillation, 2020
Linh Tran, Bastiaan S. Veeling, Kevin Roth, Jakub Świątkowski, Joshua V. Dillon, Jasper Snoek, Stephan Mandt, Tim Salimans, Sebastian Nowozin, and Rodolphe Jenatton · 2020
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Batchensemble: an alternative approach to efficient ensemble and lifelong learning
Yeming Wen, Dustin Tran, and Jimmy Ba · 2020
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Ensemble approaches for uncertainty in spoken language assessment
Xixin Wu, Kate M Knill, Mark JF Gales, and Andrey Malinin · 2020
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