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Highly Efficient Representation and Active Learning Framework and Its
  Application to Imbalanced Medical Image Classification

Highly Efficient Representation and Active Learning Framework and Its Application to Imbalanced Medical Image Classification

25 February 2021
Heng Hao
H. Moon
Sima Didari
J. Woo
P. Bangert
    AI4TS
ArXivPDFHTML

Papers citing "Highly Efficient Representation and Active Learning Framework and Its Application to Imbalanced Medical Image Classification"

3 / 3 papers shown
Title
Adversarial Examples, Uncertainty, and Transfer Testing Robustness in
  Gaussian Process Hybrid Deep Networks
Adversarial Examples, Uncertainty, and Transfer Testing Robustness in Gaussian Process Hybrid Deep Networks
John Bradshaw
A. G. Matthews
Zoubin Ghahramani
BDL
AAML
62
171
0
08 Jul 2017
Dropout as a Bayesian Approximation: Representing Model Uncertainty in
  Deep Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCV
BDL
285
9,136
0
06 Jun 2015
Manifold Gaussian Processes for Regression
Manifold Gaussian Processes for Regression
Roberto Calandra
Jan Peters
C. Rasmussen
M. Deisenroth
86
271
0
24 Feb 2014
1