Constraining the Parameters of High-Dimensional Models with Active Learning

19 May 2019

Papers citing "Constraining the Parameters of High-Dimensional Models with Active Learning"

7 / 7 papers shown

Title
Deep Ensemble Bayesian Active Learning : Addressing the Mode Collapse issue in Monte Carlo dropout via Ensembles Remus Pop Patric Fulop UQCV 45 41 0 09 Nov 2018
GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration Jacob R. Gardner Geoff Pleiss D. Bindel Kilian Q. Weinberger A. Wilson GP 77 1,088 0 28 Sep 2018
Dropout-based Active Learning for Regression Evgenii Tsymbalov Maxim Panov Alexander Shapeev BDL UQCV 20 56 0 26 Jun 2018
What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? Alex Kendall Y. Gal BDL OOD UD UQCV PER 296 4,667 0 15 Mar 2017
QBDC: Query by dropout committee for training deep supervised architecture Mélanie Ducoffe F. Precioso OOD MU 25 19 0 19 Nov 2015
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 526 9,233 0 06 Jun 2015
Adam: A Method for Stochastic Optimization Diederik P. Kingma Jimmy Ba ODL 1.0K 149,474 0 22 Dec 2014