Concrete Dropout

22 May 2017

Alex Kendall

Papers citing "Concrete Dropout"

47 / 147 papers shown

Title
Estimating Uncertainty Intervals from Collaborating Networks Tianhui Zhou Yitong Li Yuan Wu David Carlson UQCV 30 16 0 12 Feb 2020
Uncertainty-Based Out-of-Distribution Classification in Deep Reinforcement Learning Andreas Sedlmeier Thomas Gabor Thomy Phan Lenz Belzner Claudia Linnhoff-Popien 21 25 0 31 Dec 2019
TRADI: Tracking deep neural network weight distributions for uncertainty estimation Gianni Franchi Andrei Bursuc Emanuel Aldea Séverine Dubuisson Isabelle Bloch UQCV 26 51 0 24 Dec 2019
Angular Visual Hardness Beidi Chen Weiyang Liu Zhiding Yu Jan Kautz Anshumali Shrivastava Animesh Garg Anima Anandkumar AAML 43 51 0 04 Dec 2019
Learning with Multiplicative Perturbations Xiulong Yang Shihao Ji AAML 30 4 0 04 Dec 2019
Optimizing Millions of Hyperparameters by Implicit Differentiation Jonathan Lorraine Paul Vicol David Duvenaud DD 36 404 0 06 Nov 2019
On the Regularization Properties of Structured Dropout Ambar Pal Connor Lane René Vidal B. Haeffele 18 13 0 30 Oct 2019
Uncertainty Quantification with Statistical Guarantees in End-to-End Autonomous Driving Control Rhiannon Michelmore Matthew Wicker Luca Laurenti L. Cardelli Y. Gal Marta Z. Kwiatkowska BDL 18 105 0 21 Sep 2019
Building Calibrated Deep Models via Uncertainty Matching with Auxiliary Interval Predictors Jayaraman J. Thiagarajan Bindya Venkatesh P. Sattigeri P. Bremer UQCV 25 31 0 09 Sep 2019
Stochastic Filter Groups for Multi-Task CNNs: Learning Specialist and Generalist Convolution Kernels Felix J. S. Bragman Ryutaro Tanno Sebastien Ourselin Daniel C. Alexander M. Jorge Cardoso 24 86 0 26 Aug 2019
A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation Amir Atapour-Abarghouei Stephen Bonner A. Mcgough 29 7 0 19 Aug 2019
Adaptative Inference Cost With Convolutional Neural Mixture Models Adria Ruiz Jakob Verbeek VLM 30 22 0 19 Aug 2019
Improved Adversarial Robustness by Reducing Open Space Risk via Tent Activations Andras Rozsa Terrance E. Boult AAML 30 18 0 07 Aug 2019
A General Framework for Uncertainty Estimation in Deep Learning Antonio Loquercio Mattia Segu Davide Scaramuzza UQCV BDL OOD 31 289 0 16 Jul 2019
Variational Inference MPC for Bayesian Model-based Reinforcement Learning Masashi Okada T. Taniguchi 38 73 0 08 Jul 2019
Heterogeneous Robot Teams for Informative Sampling Travis Manderson Sandeep Manjanna Gregory Dudek 20 5 0 17 Jun 2019
MonoLoco: Monocular 3D Pedestrian Localization and Uncertainty Estimation Lorenzo Bertoni S. Kreiss Alexandre Alahi UQCV 33 117 0 14 Jun 2019
DropConnect Is Effective in Modeling Uncertainty of Bayesian Deep Networks Aryan Mobiny H. Nguyen S. Moulik Naveen Garg Carol C. Wu UQCV BDL 20 155 0 07 Jun 2019
Perceptual Attention-based Predictive Control Keuntaek Lee G. N. An Viacheslav Zakharov Evangelos A. Theodorou 15 19 0 26 Apr 2019
Survey of Dropout Methods for Deep Neural Networks Alex Labach Hojjat Salehinejad S. Valaee 27 149 0 25 Apr 2019
Centerline Depth World Reinforcement Learning-based Left Atrial Appendage Orifice Localization Walid Abdullah Al I. Yun E. Chun 21 1 0 02 Apr 2019
Functional Variational Bayesian Neural Networks Shengyang Sun Guodong Zhang Jiaxin Shi Roger C. Grosse BDL 22 235 0 14 Mar 2019
The MBPEP: a deep ensemble pruning algorithm providing high quality uncertainty prediction Ruihan Hu Qijun Huang Sheng Chang Hao Wang Jin He UQCV 14 32 0 25 Feb 2019
Multi-modal Ensemble Classification for Generalized Zero Shot Learning Rafael Felix Michele Sasdelli Ian Reid G. Carneiro VLM 24 20 0 15 Jan 2019
Uncertainty-Based Out-of-Distribution Detection in Deep Reinforcement Learning Andreas Sedlmeier Thomas Gabor Thomy Phan Lenz Belzner Claudia Linnhoff-Popien UQCV 24 24 0 08 Jan 2019
Guided Dropout Rohit Keshari Richa Singh Mayank Vatsa BDL 26 37 0 10 Dec 2018
Knowing what you know in brain segmentation using Bayesian deep neural networks Patrick McClure Nao Rho J. Lee Jakub R. Kaczmarzyk C. Zheng Satrajit S. Ghosh D. Nielson Adam G. Thomas P. Bandettini Francisco Pereira UQCV 3DV BDL 24 52 0 03 Dec 2018
Evaluating Bayesian Deep Learning Methods for Semantic Segmentation Jishnu Mukhoti Y. Gal UQCV BDL 33 219 0 30 Nov 2018
Scalable agent alignment via reward modeling: a research direction Jan Leike David M. Krueger Tom Everitt Miljan Martic Vishal Maini Shane Legg 34 397 0 19 Nov 2018
Good Initializations of Variational Bayes for Deep Models Simone Rossi Pietro Michiardi Maurizio Filippone BDL 17 21 0 18 Oct 2018
Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems Dongkun Zhang Lu Lu Ling Guo George Karniadakis UQCV 27 399 0 21 Sep 2018
Accurate Uncertainties for Deep Learning Using Calibrated Regression Volodymyr Kuleshov Nathan Fenner Stefano Ermon BDL UQCV 41 622 0 01 Jul 2018
Randomized Prior Functions for Deep Reinforcement Learning Ian Osband John Aslanides Albin Cassirer UQCV BDL 21 372 0 08 Jun 2018
Uncertainty-Aware Attention for Reliable Interpretation and Prediction Jay Heo Haebeom Lee Saehoon Kim Juho Lee Kwang Joon Kim Eunho Yang Sung Ju Hwang OOD 17 87 0 24 May 2018
Excitation Dropout: Encouraging Plasticity in Deep Neural Networks Andrea Zunino Sarah Adel Bargal Pietro Morerio Jianming Zhang Stan Sclaroff Vittorio Murino 21 23 0 23 May 2018
Confidence Scoring Using Whitebox Meta-models with Linear Classifier Probes Tongfei Chen Jirí Navrátil Vijay Iyengar Karthikeyan Shanmugam 10 42 0 14 May 2018
Towards Safe Autonomous Driving: Capture Uncertainty in the Deep Neural Network For Lidar 3D Vehicle Detection Di Feng Lars Rosenbaum Klaus C. J. Dietmayer 3DPC UQCV 36 243 0 13 Apr 2018
Safe end-to-end imitation learning for model predictive control Keuntaek Lee Kamil Saigol Evangelos A. Theodorou BDL 24 24 0 27 Mar 2018
Understanding Measures of Uncertainty for Adversarial Example Detection Lewis Smith Y. Gal UQCV 57 358 0 22 Mar 2018
Synthesizing Neural Network Controllers with Probabilistic Model based Reinforcement Learning J. A. G. Higuera David Meger Gregory Dudek BDL 22 39 0 06 Mar 2018
Quantifying Uncertainty in Discrete-Continuous and Skewed Data with Bayesian Deep Learning T. Vandal E. Kodra Jennifer Dy S. Ganguly R. Nemani A. Ganguly UQCV BDL 32 52 0 13 Feb 2018
Understanding the Disharmony between Dropout and Batch Normalization by Variance Shift Xiang Li Shuo Chen Xiaolin Hu Jian Yang 39 309 0 16 Jan 2018
Uncertainty Estimates for Efficient Neural Network-based Dialogue Policy Optimisation Christopher Tegho Paweł Budzianowski Milica Gasic 29 8 0 30 Nov 2017
DropoutDAgger: A Bayesian Approach to Safe Imitation Learning Kunal Menda Katherine Driggs-Campbell Mykel J. Kochenderfer 29 28 0 18 Sep 2017
Deep and Confident Prediction for Time Series at Uber Lingxue Zhu N. Laptev BDL AI4TS 35 343 0 06 Sep 2017
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 287 9,156 0 06 Jun 2015
Improving neural networks by preventing co-adaptation of feature detectors Geoffrey E. Hinton Nitish Srivastava A. Krizhevsky Ilya Sutskever Ruslan Salakhutdinov VLM 266 7,639 0 03 Jul 2012