Towards neural networks that provably know when they don't know

26 September 2019

Matthias Hein

Papers citing "Towards neural networks that provably know when they don't know"

41 / 91 papers shown

Title
Uncertainty-Aware Reliable Text Classification Yibo Hu Latifur Khan EDL UQCV 31 33 0 15 Jul 2021
Deep Ensembling with No Overhead for either Training or Testing: The All-Round Blessings of Dynamic Sparsity Shiwei Liu Tianlong Chen Zahra Atashgahi Xiaohan Chen Ghada Sokar Elena Mocanu Mykola Pechenizkiy Zhangyang Wang D. Mocanu OOD 25 49 0 28 Jun 2021
Explanatory Pluralism in Explainable AI Yiheng Yao XAI 14 4 0 26 Jun 2021
Task-Driven Detection of Distribution Shifts with Statistical Guarantees for Robot Learning Alec Farid Sushant Veer Divya Pachisia Anirudha Majumdar OODD 20 1 0 25 Jun 2021
Being a Bit Frequentist Improves Bayesian Neural Networks Agustinus Kristiadi Matthias Hein Philipp Hennig BDL UQCV 15 15 0 18 Jun 2021
Taxonomy of Machine Learning Safety: A Survey and Primer Sina Mohseni Haotao Wang Zhiding Yu Chaowei Xiao Zhangyang Wang J. Yadawa 19 31 0 09 Jun 2021
Provably Robust Detection of Out-of-distribution Data (almost) for free Alexander Meinke Julian Bitterwolf Matthias Hein OODD 25 22 0 08 Jun 2021
Natural Posterior Network: Deep Bayesian Uncertainty for Exponential Family Distributions Bertrand Charpentier Oliver Borchert Daniel Zügner Simon Geisler Stephan Günnemann UQCV BDL 22 17 0 10 May 2021
SegmentMeIfYouCan: A Benchmark for Anomaly Segmentation Robin Shing Moon Chan Krzysztof Lis Svenja Uhlemeyer Hermann Blum S. Honari Roland Siegwart Pascal Fua Mathieu Salzmann Matthias Rottmann UQCV 19 136 0 30 Apr 2021
Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety Sebastian Houben Stephanie Abrecht Maram Akila Andreas Bär Felix Brockherde ... Serin Varghese Michael Weber Sebastian J. Wirkert Tim Wirtz Matthias Woehrle AAML 13 58 0 29 Apr 2021
Multidimensional Uncertainty-Aware Evidential Neural Networks Yibo Hu Yuzhe Ou Xujiang Zhao Jin-Hee Cho Feng Chen EDL UQCV AAML 20 23 0 26 Dec 2020
Learning Energy-Based Models With Adversarial Training Xuwang Yin Shiying Li Gustavo K. Rohde AAML DiffM 22 9 0 11 Dec 2020
Entropy Maximization and Meta Classification for Out-Of-Distribution Detection in Semantic Segmentation Robin Shing Moon Chan Matthias Rottmann Hanno Gottschalk OODD 25 149 0 09 Dec 2020
Unsupervised Anomaly Detection From Semantic Similarity Scores Nima Rafiee Rahil Gholamipoor M. Kollmann OODD 6 2 0 01 Dec 2020
Feature Space Singularity for Out-of-Distribution Detection Haiwen Huang Zhihan Li Lulu Wang Sishuo Chen Bin Dong Xinyu Zhou OODD 20 65 0 30 Nov 2020
Probing Predictions on OOD Images via Nearest Categories Yao-Yuan Yang Cyrus Rashtchian Ruslan Salakhutdinov Kamalika Chaudhuri AAML 14 0 0 17 Nov 2020
Evaluating Robustness of Predictive Uncertainty Estimation: Are Dirichlet-based Models Reliable? Anna-Kathrin Kopetzki Bertrand Charpentier Daniel Zügner Sandhya Giri Stephan Günnemann 23 45 0 28 Oct 2020
Towards Maximizing the Representation Gap between In-Domain & Out-of-Distribution Examples Jay Nandy W. Hsu M. Lee UQCV 8 60 0 20 Oct 2020
Learnable Uncertainty under Laplace Approximations Agustinus Kristiadi Matthias Hein Philipp Hennig UQCV BDL 6 30 0 06 Oct 2020
An Infinite-Feature Extension for Bayesian ReLU Nets That Fixes Their Asymptotic Overconfidence Agustinus Kristiadi Matthias Hein Philipp Hennig BDL 13 9 0 06 Oct 2020
A Unifying Review of Deep and Shallow Anomaly Detection Lukas Ruff Jacob R. Kauffmann Robert A. Vandermeulen G. Montavon Wojciech Samek Marius Kloft Thomas G. Dietterich Klaus-Robert Muller UQCV 18 779 0 24 Sep 2020
Regularizing Attention Networks for Anomaly Detection in Visual Question Answering Doyup Lee Yeongjae Cheon Wook-Shin Han AAML OOD 6 16 0 21 Sep 2020
Certifiably Adversarially Robust Detection of Out-of-Distribution Data Julian Bitterwolf Alexander Meinke Matthias Hein 6 9 0 16 Jul 2020
ATOM: Robustifying Out-of-distribution Detection Using Outlier Mining Jiefeng Chen Yixuan Li Xi Wu Yingyu Liang S. Jha OODD 8 135 0 26 Jun 2020
Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness Jeremiah Zhe Liu Zi Lin Shreyas Padhy Dustin Tran Tania Bedrax-Weiss Balaji Lakshminarayanan UQCV BDL 14 436 0 17 Jun 2020
Density of States Estimation for Out-of-Distribution Detection Warren Morningstar Cusuh Ham Andrew Gallagher Balaji Lakshminarayanan Alexander A. Alemi Joshua V. Dillon OODD 6 83 0 16 Jun 2020
Depth Uncertainty in Neural Networks Javier Antorán J. Allingham José Miguel Hernández-Lobato UQCV OOD BDL 28 100 0 15 Jun 2020
Calibrated neighborhood aware confidence measure for deep metric learning Maryna Karpusha Sunghee Yun István Fehérvári UQCV FedML 13 2 0 08 Jun 2020
A Smooth Representation of Belief over SO(3) for Deep Rotation Learning with Uncertainty Valentin Peretroukhin Matthew Giamou David M. Rosen W. N. Greene Nicholas Roy Jonathan Kelly 6 42 0 01 Jun 2020
Machine Learning Systems for Intelligent Services in the IoT: A Survey Wiebke Toussaint Aaron Yi Ding LRM 22 0 0 29 May 2020
Self-supervised Robust Object Detectors from Partially Labelled Datasets Mahdieh Abbasi D. Laurendeau Christian Gagné ObjD 11 3 0 23 May 2020
Robust Out-of-distribution Detection for Neural Networks Jiefeng Chen Yixuan Li Xi Wu Yingyu Liang S. Jha OODD 153 84 0 21 Mar 2020
Adversarial Robustness on In- and Out-Distribution Improves Explainability Maximilian Augustin Alexander Meinke Matthias Hein OOD 62 98 0 20 Mar 2020
No Surprises: Training Robust Lung Nodule Detection for Low-Dose CT Scans by Augmenting with Adversarial Attacks Siqi Liu A. Setio Florin-Cristian Ghesu Eli Gibson Sasa Grbic Bogdan Georgescu D. Comaniciu AAML OOD 15 40 0 08 Mar 2020
Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks Agustinus Kristiadi Matthias Hein Philipp Hennig BDL UQCV 25 277 0 24 Feb 2020
On-manifold Adversarial Data Augmentation Improves Uncertainty Calibration Kanil Patel William H. Beluch Dan Zhang Michael Pfeiffer Bin Yang UQCV 13 30 0 16 Dec 2019
Detection of False Positive and False Negative Samples in Semantic Segmentation Matthias Rottmann Kira Maag Robin Shing Moon Chan Fabian Hüger Peter Schlicht Hanno Gottschalk UQCV 12 23 0 08 Dec 2019
Toward Metrics for Differentiating Out-of-Distribution Sets Mahdieh Abbasi Changjian Shui Arezoo Rajabi Christian Gagné R. Bobba OODD 12 4 0 18 Oct 2019
Natural Adversarial Examples Dan Hendrycks Kevin Zhao Steven Basart Jacob Steinhardt D. Song OODD 47 1,417 0 16 Jul 2019
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 270 5,660 0 05 Dec 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 279 9,136 0 06 Jun 2015