Quantifying Uncertainty in Discrete-Continuous and Skewed Data with
Bayesian Deep Learning

Quantifying Uncertainty in Discrete-Continuous and Skewed Data with Bayesian Deep Learning

13 February 2018

Jennifer Dy

Papers citing "Quantifying Uncertainty in Discrete-Continuous and Skewed Data with Bayesian Deep Learning"

5 / 5 papers shown

Title
Towards Learning in Grey Spatiotemporal Systems: A Prophet to Non-consecutive Spatiotemporal Dynamics Zhengyang Zhou Yang Kuo Wei Sun Binwu Wang Mingxing Zhou Yunan Zong Yang Wang AI4TS 24 3 0 17 Aug 2022
Practical Conditional Neural Processes Via Tractable Dependent Predictions Stratis Markou James Requeima W. Bruinsma Anna Vaughan Richard E. Turner UQCV AI4CE 20 24 0 16 Mar 2022
Bridging observation, theory and numerical simulation of the ocean using Machine Learning Maike Sonnewald Redouane Lguensat Daniel C. Jones P. Dueben J. Brajard Venkatramani Balaji AI4Cl AI4CE 40 100 0 26 Apr 2021
Accurate Uncertainty Estimation and Decomposition in Ensemble Learning J. Liu John Paisley M. Kioumourtzoglou B. Coull UQCV UD PER 19 83 0 11 Nov 2019
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 279 9,136 0 06 Jun 2015