Deep Ensembles Work, But Are They Necessary?

Deep Ensembles Work, But Are They Necessary?

14 February 2022

E. Kelly Buchanan

John P. Cunningham

Papers citing "Deep Ensembles Work, But Are They Necessary?"

15 / 15 papers shown

Title
CreINNs: Credal-Set Interval Neural Networks for Uncertainty Estimation in Classification Tasks Kaizheng Wang Keivan K1 Shariatmadar Shireen Kudukkil Manchingal Fabio Cuzzolin David Moens Hans Hallez UQCV BDL 92 12 0 28 Jan 2025
Sampling from Bayesian Neural Network Posteriors with Symmetric Minibatch Splitting Langevin Dynamics Daniel Paulin P. Whalley Neil K. Chada B. Leimkuhler BDL 49 4 0 14 Oct 2024
Relaxed Quantile Regression: Prediction Intervals for Asymmetric Noise T. Pouplin Alan Jeffares Nabeel Seedat Mihaela van der Schaar 55 3 0 05 Jun 2024
Credal Wrapper of Model Averaging for Uncertainty Estimation in Classification Kaizheng Wang Fabio Cuzzolin Keivan K1 Shariatmadar David Moens Hans Hallez UQCV BDL 83 0 0 23 May 2024
Uncertainty Quantification for Image-based Traffic Prediction across Cities Alexander Timans Nina Wiedemann Nishant Kumar Ye Hong Martin Raubal 18 1 0 11 Aug 2023
When are ensembles really effective? Ryan Theisen Hyunsuk Kim Yaoqing Yang Liam Hodgkinson Michael W. Mahoney FedML UQCV 35 15 0 21 May 2023
Diversifying Deep Ensembles: A Saliency Map Approach for Enhanced OOD Detection, Calibration, and Accuracy Stanislav Dereka I. Karpukhin Maksim Zhdanov Sergey Kolesnikov 36 0 0 19 May 2023
Pathologies of Predictive Diversity in Deep Ensembles Taiga Abe E. Kelly Buchanan Geoff Pleiss John P. Cunningham UQCV 38 13 0 01 Feb 2023
Joint Training of Deep Ensembles Fails Due to Learner Collusion Alan Jeffares Tennison Liu Jonathan Crabbé M. Schaar FedML 53 15 0 26 Jan 2023
Learning New Tasks from a Few Examples with Soft-Label Prototypes Avyav Kumar Singh Ekaterina Shutova H. Yannakoudakis VLM 25 0 0 31 Oct 2022
Quantifying Aleatoric and Epistemic Uncertainty in Machine Learning: Are Conditional Entropy and Mutual Information Appropriate Measures? Lisa Wimmer Yusuf Sale Paul Hofman Bern Bischl Eyke Hüllermeier PER UD 34 64 0 07 Sep 2022
Success of Uncertainty-Aware Deep Models Depends on Data Manifold Geometry M. Penrod Harrison Termotto Varshini Reddy Jiayu Yao Finale Doshi-Velez Weiwei Pan AAML OOD 37 1 0 02 Aug 2022
Deep interpretable ensembles Lucas Kook Andrea Götschi Philipp F. M. Baumann Torsten Hothorn Beate Sick UQCV 22 8 0 25 May 2022
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 276 5,661 0 05 Dec 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 285 9,138 0 06 Jun 2015