Deep Ensembles: A Loss Landscape Perspective

Deep Ensembles: A Loss Landscape Perspective

5 December 2019

Balaji Lakshminarayanan

Papers citing "Deep Ensembles: A Loss Landscape Perspective"

11 / 161 papers shown

Title
A benchmark study on reliable molecular supervised learning via Bayesian learning Doyeong Hwang Grace Lee Hanseok Jo Seyoul Yoon Seongok Ryu 32 9 0 12 Jun 2020
Efficient Ensemble Model Generation for Uncertainty Estimation with Bayesian Approximation in Segmentation Hong Joo Lee S. T. Kim Hakmin Lee Nassir Navab Yong Man Ro UQCV 16 7 0 21 May 2020
Superkernel Neural Architecture Search for Image Denoising Marcin Mo.zejko Tomasz Latkowski Lukasz Treszczotko Michal Szafraniuk K. Trojanowski SupR 27 16 0 19 Apr 2020
Reinforcement Learning through Active Inference Alexander Tschantz Beren Millidge A. Seth Christopher L. Buckley AI4CE 31 69 0 28 Feb 2020
Bayesian Deep Learning and a Probabilistic Perspective of Generalization A. Wilson Pavel Izmailov UQCV BDL OOD 24 642 0 20 Feb 2020
BatchEnsemble: An Alternative Approach to Efficient Ensemble and Lifelong Learning Yeming Wen Dustin Tran Jimmy Ba OOD FedML UQCV 32 483 0 17 Feb 2020
TRADI: Tracking deep neural network weight distributions for uncertainty estimation Gianni Franchi Andrei Bursuc Emanuel Aldea Séverine Dubuisson Isabelle Bloch UQCV 26 51 0 24 Dec 2019
Emergent properties of the local geometry of neural loss landscapes Stanislav Fort Surya Ganguli 16 50 0 14 Oct 2019
Predicting materials properties without crystal structure: Deep representation learning from stoichiometry Rhys E. A. Goodall A. Lee 21 254 0 01 Oct 2019
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 276 5,695 0 05 Dec 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 287 9,167 0 06 Jun 2015