To Trust Or Not To Trust A Classifier

To Trust Or Not To Trust A Classifier

30 May 2018

Papers citing "To Trust Or Not To Trust A Classifier"

14 / 64 papers shown

Title
The Right Tool for the Job: Matching Model and Instance Complexities Roy Schwartz Gabriel Stanovsky Swabha Swayamdipta Jesse Dodge Noah A. Smith 33 167 0 16 Apr 2020
Mix-n-Match: Ensemble and Compositional Methods for Uncertainty Calibration in Deep Learning Jize Zhang B. Kailkhura T. Y. Han UQCV 19 220 0 16 Mar 2020
Anomalous Example Detection in Deep Learning: A Survey Saikiran Bulusu B. Kailkhura Bo-wen Li P. Varshney D. Song AAML 28 47 0 16 Mar 2020
Real-time Out-of-distribution Detection in Learning-Enabled Cyber-Physical Systems Feiyang Cai X. Koutsoukos OODD 121 73 0 28 Jan 2020
Distance-Based Learning from Errors for Confidence Calibration Chen Xing Sercan Ö. Arik Zizhao Zhang Tomas Pfister FedML 18 39 0 03 Dec 2019
Addressing Failure Prediction by Learning Model Confidence Charles Corbière Nicolas Thome Avner Bar-Hen Matthieu Cord P. Pérez 19 281 0 01 Oct 2019
Density estimation in representation space to predict model uncertainty Tiago Ramalho M. Corbalan UQCV BDL 8 37 0 20 Aug 2019
Interpretable Counterfactual Explanations Guided by Prototypes A. V. Looveren Janis Klaise FAtt 11 378 0 03 Jul 2019
Detecting Adversarial Examples and Other Misclassifications in Neural Networks by Introspection Jonathan Aigrain Marcin Detyniecki AAML 14 30 0 22 May 2019
Tutorial: Safe and Reliable Machine Learning S. Saria Adarsh Subbaswamy FaML 25 82 0 15 Apr 2019
Visual Entailment: A Novel Task for Fine-Grained Image Understanding Ning Xie Farley Lai Derek Doran Asim Kadav CoGe 31 321 0 20 Jan 2019
HashTran-DNN: A Framework for Enhancing Robustness of Deep Neural Networks against Adversarial Malware Samples Deqiang Li Ramesh Baral Tao Li Han Wang Qianmu Li Shouhuai Xu AAML 17 21 0 18 Sep 2018
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 276 5,660 0 05 Dec 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 285 9,136 0 06 Jun 2015