Frequentist Uncertainty in Recurrent Neural Networks via Blockwise Influence Functions

20 June 2020

Papers citing "Frequentist Uncertainty in Recurrent Neural Networks via Blockwise Influence Functions"

9 / 9 papers shown

Title
Copula Conformal Prediction for Multi-step Time Series Forecasting S. Sun Rose Yu AI4TS 37 22 0 06 Dec 2022
What's Behind the Mask: Estimating Uncertainty in Image-to-Image Problems Gilad Kutiel Regev Cohen Michael Elad Daniel Freedman UQCV 34 5 0 28 Nov 2022
Uncertainty Quantification with Pre-trained Language Models: A Large-Scale Empirical Analysis Yuxin Xiao Paul Pu Liang Umang Bhatt Willie Neiswanger Ruslan Salakhutdinov Louis-Philippe Morency 175 86 0 10 Oct 2022
Multi-robot Mission Planning in Dynamic Semantic Environments Samarth Kalluraya George J. Pappas Y. Kantaros 42 21 0 13 Sep 2022
Conformal Methods for Quantifying Uncertainty in Spatiotemporal Data: A Survey S. Sun AI4CE 38 10 0 08 Sep 2022
Quantifying Epistemic Uncertainty in Deep Learning Ziyi Huang H. Lam Haofeng Zhang UQCV BDL UD PER 24 12 0 23 Oct 2021
Quantifying Uncertainty in Deep Spatiotemporal Forecasting Dongxian Wu Liyao (Mars) Gao X. Xiong Matteo Chinazzi Alessandro Vespignani Yi Ma Rose Yu AI4TS 13 68 0 25 May 2021
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 276 5,675 0 05 Dec 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 285 9,145 0 06 Jun 2015