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In image classification tasks, the ability of deep CNNs to deal with complex image data has proven to be unrivalled.
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Batch mode active learning and its application to medical image classification
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Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Bayesian learning for neural networks , volume 118
Radford M Neal · 2012
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Burr Settles · 2012
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Matthew D Hoffman, David M Blei, Chong Wang, and John Paisley · 2013
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Diederik P Kingma and Max Welling · 2013
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Monte Carlo statistical methods
Christian Robert and George Casella · 2013
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Yarin Gal, Riashat Islam, and Zoubin Ghahramani · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
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A deep learning-based radiomics model for prediction of survival in glioblastoma multiforme
Jiangwei Lao, Yinsheng Chen, Zhi-Cheng Li, Qihua Li, Ji Zhang, Jing Liu, and Guangtao Zhai · 2017
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Christian Leibig, Vaneeda Allken, Murat Seçkin Ayhan, Philipp Berens, and Siegfried Wahl · 2017
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Active learning for visual question answering: An empirical study
Xiao Lin and Devi Parikh · 2017
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Dropout: A simple way to prevent neural networks from overfitting
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Alex Kendall, Vijay Badrinarayanan, and Roberto Cipolla · 2015
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Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
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Zheng Wang and Jieping Ye · 2015
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Deep active learning for named entity recognition
Yanyao Shen, Hyokun Yun, Zachary C Lipton, Yakov Kronrod, and Animashree Anandkumar · 2017
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Active discriminative text representation learning
Ye Zhang, Matthew Lease, and Byron C Wallace · 2017
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The power of ensembles for active learning in image classification
William H Beluch, Tim Genewein, Andreas Nürnberger, and Jan M Köhler · 2018
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Clinically applicable deep learning for diagnosis and referral in retinal disease
Jeffrey De Fauw, Joseph R Ledsam, Bernardino Romera-Paredes, Stanislav Nikolov, Nenad Tomasev, Sam Blackwell, Harry Askham, Xavier Glorot, Brendan O’Donoghue, Daniel Visentin, et al · 2018
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