Learn to Accumulate Evidence from All Training Samples: Theory and Practice

19 June 2023

Papers citing "Learn to Accumulate Evidence from All Training Samples: Theory and Practice"

10 / 10 papers shown

Title
Label Calibration in Source Free Domain Adaptation Shivangi Rai Rini Smita Thakur Kunal Jangid Vinod K. Kurmi UQCV 48 1 0 13 Jan 2025
Conformalized Credal Regions for Classification with Ambiguous Ground Truth Michele Caprio David Stutz Shuo Li Arnaud Doucet UQCV 70 4 0 07 Nov 2024
Weakly-Supervised Residual Evidential Learning for Multi-Instance Uncertainty Estimation Pei Liu Luping Ji EDL 34 4 0 07 May 2024
Think Twice Before Selection: Federated Evidential Active Learning for Medical Image Analysis with Domain Shifts Jiayi Chen Benteng Ma Hengfei Cui Yong-quan Xia OOD FedML 29 12 0 05 Dec 2023
Vision Transformers in 2022: An Update on Tiny ImageNet Ethan Huynh ViT 31 11 0 21 May 2022
Uncertainty Aware Semi-Supervised Learning on Graph Data Xujiang Zhao Feng Chen Shu Hu Jin-Hee Cho UQCV EDL BDL 121 131 0 24 Oct 2020
Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot Learning Yinbo Chen Zhuang Liu Huijuan Xu Trevor Darrell Xiaolong Wang 175 342 0 09 Mar 2020
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Chelsea Finn Pieter Abbeel Sergey Levine OOD 428 11,715 0 09 Mar 2017
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 276 5,683 0 05 Dec 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 287 9,156 0 06 Jun 2015