BayesAdapter: Being Bayesian, Inexpensively and Reliably, via Bayesian
Fine-tuning

BayesAdapter: Being Bayesian, Inexpensively and Reliably, via Bayesian Fine-tuning

5 October 2020

Jun Zhu

Papers citing "BayesAdapter: Being Bayesian, Inexpensively and Reliably, via Bayesian Fine-tuning"

6 / 6 papers shown

Title
Dynamic ensemble selection based on Deep Neural Network Uncertainty Estimation for Adversarial Robustness Ruoxi Qin Linyuan Wang Xuehui Du Xing-yuan Chen Binghai Yan AAML 26 0 0 01 Aug 2023
Efficient Detection of LLM-generated Texts with a Bayesian Surrogate Model Yibo Miao Hongcheng Gao Hao Zhang Zhijie Deng DeLMO 30 20 0 26 May 2023
Accelerated Linearized Laplace Approximation for Bayesian Deep Learning Zhijie Deng Feng Zhou Jun Zhu BDL 47 19 0 23 Oct 2022
Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam Mohammad Emtiyaz Khan Didrik Nielsen Voot Tangkaratt Wu Lin Y. Gal Akash Srivastava ODL 74 266 0 13 Jun 2018
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