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We design a new algorithm for batch active learning with deep neural network models.
Improving generalization with active learning
David Cohn, Les Atlas, and Richard Ladner · 1994
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Gradient-based learning applied to document recognition
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Less is more: Active learning with support vector machines
Greg Schohn and David Cohn · 2000
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Support vector machine active learning with applications to text classification
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Online choice of active learning algorithms
Yoram Baram, Ran El Yaniv, and Kobi Luz · 2004
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Combining active and semi-supervised learning for spoken language understanding
Gokhan Tur, Dilek Hakkani-Tür, and Robert E Schapire · 2005
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Agnostic active learning
Maria-Florina Balcan, Alina Beygelzimer, and John Langford · 2006
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Margin-based active learning for structured output spaces
Dan Roth and Kevin Small · 2006
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k-means++: The advantages of careful seeding
David Arthur and Sergei Vassilvitskii · 2007
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Discriminative batch mode active learning
Yuhong Guo and Dale Schuurmans · 2008
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Multiple-instance active learning
Burr Settles, Mark Craven, and Soumya Ray · 2008
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Robust bounds for classification via selective sampling
Nicolo Cesa-Bianchi, Claudio Gentile, and Francesco Orabona · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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Agnostic active learning without constraints
Alina Beygelzimer, Daniel J Hsu, John Langford, and Tong Zhang · 2010
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Active learning literature survey
Burr Settles · 2010
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Active learning by querying informative and representative examples
Sheng-Jun Huang, Rong Jin, and Zhi-Hua Zhou · 2010
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k-dpps: Fixed-size determinantal point processes
Alex Kulesza and Ben Taskar · 2011
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Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng · 2011
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Two faces of active learning
Sanjoy Dasgupta · 2011
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Determinantal point processes for machine learning
Alex Kulesza, Ben Taskar, et al · 2012
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Fast determinantal point process sampling with application to clustering
Byungkon Kang · 2013
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A new active labeling method for deep learning
Dan Wang and Yi Shang · 2014
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Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
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Deep active learning over the long tail
Yonatan Geifman and Ran El-Yaniv · 2017
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Deep bayesian active learning with image data
Yarin Gal, Riashat Islam, and Zoubin Ghahramani · 2017
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Active bias: Training more accurate neural networks by emphasizing high variance samples
Haw-Shiuan Chang, Erik Learned-Miller, and Andrew McCallum · 2017
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Reverse iterative volume sampling for linear regression
Michał Dereziński and Manfred K Warmuth · 2018
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Fast determinantal point processes via distortion-free intermediate sampling
Michał Dereziński · 2018
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Theory of disagreement-based active learning
Steve Hanneke · 2014
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Active learning by learning
Wei-Ning Hsu and Hsuan-Tien Lin · 2015
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Querying discriminative and representative samples for batch mode active learning
Zheng Wang and Jieping Ye · 2015
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Submodularity in data subset selection and active learning
Kai Wei, Rishabh Iyer, and Jeff Bilmes · 2015
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Monte carlo markov chain algorithms for sampling strongly rayleigh distributions and determinantal point processes
Nima Anari, Shayan Oveis Gharan, and Alireza Rezaei · 2016
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Active learning for convolutional neural networks: A core-set approach
Ozan Sener and Silvio Savarese · 2018
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Adversarial active learning for deep networks: a margin based approach
Melanie Ducoffe and Frederic Precioso · 2018
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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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Optimal subsampling for large sample logistic regression
HaiYing Wang, Rong Zhu, and Ping Ma · 2018
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Optimal subsampling with influence functions
Daniel Ting and Eric Brochu · 2018
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Uncertainty sampling is preconditioned stochastic gradient descent on zero-one loss
Stephen Mussmann and Percy S Liang · 2018
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On the difficulty of warm-starting neural network training
Jordan T Ash and Ryan P Adams · 2019
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Discriminative active learning
Daniel Gissin and Shai Shalev-Shwartz · 2019
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Batchbald: Efficient and diverse batch acquisition for deep bayesian active learning
Andreas Kirsch, Joost van Amersfoort, and Yarin Gal · 2019
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Batch active learning using determinantal point processes
Erdem Bıyık, Kenneth Wang, Nima Anari, and Dorsa Sadigh · 2019
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