Diversity Matters When Learning From Ensembles

27 October 2021

Papers citing "Diversity Matters When Learning From Ensembles"

21 / 21 papers shown

Title
Fairness of Deep Ensembles: On the interplay between per-group task difficulty and under-representation Estanislao Claucich Sara Hooker Diego H. Milone Enzo Ferrante Rodrigo Echeveste FedML 47 0 0 24 Jan 2025
Deep Nets with Subsampling Layers Unwittingly Discard Useful Activations at Test-Time Chiao-An Yang Ziwei Liu Raymond A. Yeh 23 1 0 01 Oct 2024
Towards a Probabilistic Fusion Approach for Robust Battery Prognostics Jokin Alcibar J. Aizpurua E. Zugasti 33 1 0 24 May 2024
Fast Ensembling with Diffusion Schrödinger Bridge Hyunsu Kim Jongmin Yoon Juho Lee FedML UQCV 16 1 0 24 Apr 2024
Efficient Multi-Model Fusion with Adversarial Complementary Representation Learning Zuheng Kang Yayun He Jianzong Wang Junqing Peng Jing Xiao 21 0 0 24 Apr 2024
Medical Image Debiasing by Learning Adaptive Agreement from a Biased Council Luyang Luo Xin Huang Minghao Wang Zhuoyue Wan Hao Chen 21 2 0 22 Jan 2024
AST: Effective Dataset Distillation through Alignment with Smooth and High-Quality Expert Trajectories Jiyuan Shen Wenzhuo Yang Kwok-Yan Lam DD 25 1 0 16 Oct 2023
Distilling Influences to Mitigate Prediction Churn in Graph Neural Networks Andreas Roth Thomas Liebig 50 0 0 02 Oct 2023
Exploring Resiliency to Natural Image Corruptions in Deep Learning using Design Diversity Rafael Rosales Pablo Munoz Michael Paulitsch 25 2 0 15 Mar 2023
Memory-adaptive Depth-wise Heterogenous Federated Learning Kai Zhang Yutong Dai Hongyi Wang Eric P. Xing Xun Chen Lichao Sun FedML 18 7 0 08 Mar 2023
Generalized Uncertainty of Deep Neural Networks: Taxonomy and Applications Chengyu Dong OOD UQCV BDL AI4CE 18 0 0 02 Feb 2023
Bayesian posterior approximation with stochastic ensembles Oleksandr Balabanov Bernhard Mehlig H. Linander BDL UQCV 27 5 0 15 Dec 2022
Accelerating Dataset Distillation via Model Augmentation Lei Zhang Jie M. Zhang Bowen Lei Subhabrata Mukherjee Xiang Pan Bo-Lu Zhao Caiwen Ding Y. Li Dongkuan Xu DD 26 62 0 12 Dec 2022
Unsupervised Machine Learning for Explainable Health Care Fraud Detection Shubhranshu Shekhar Jetson Leder-Luis L. Akoglu OOD 14 5 0 05 Nov 2022
Improving Ensemble Distillation With Weight Averaging and Diversifying Perturbation G. Nam Hyungi Lee Byeongho Heo Juho Lee UQCV FedML 18 7 0 30 Jun 2022
Deep Isolation Forest for Anomaly Detection Hongzuo Xu Guansong Pang Yijie Wang Yongjun Wang 21 182 0 14 Jun 2022
Ensembles for Uncertainty Estimation: Benefits of Prior Functions and Bootstrapping Vikranth Dwaracherla Zheng Wen Ian Osband Xiuyuan Lu S. Asghari Benjamin Van Roy UQCV 16 16 0 08 Jun 2022
Functional Ensemble Distillation Coby Penso Idan Achituve Ethan Fetaya FedML 23 2 0 05 Jun 2022
Verification-Aided Deep Ensemble Selection Guy Amir Tom Zelazny Guy Katz Michael Schapira AAML 30 18 0 08 Feb 2022
Sparse MoEs meet Efficient Ensembles J. Allingham F. Wenzel Zelda E. Mariet Basil Mustafa J. Puigcerver ... Balaji Lakshminarayanan Jasper Snoek Dustin Tran Carlos Riquelme Ruiz Rodolphe Jenatton MoE 44 21 0 07 Oct 2021
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 270 5,660 0 05 Dec 2016