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Active learning has proven to be useful for minimizing labeling costs by selecting the most informative samples.
An analysis of approximations for maximizing submodular set functions—i
George L Nemhauser, Laurence A Wolsey, and Marshall L Fisher · 1978
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Backpropagation applied to handwritten zip code recognition
Yann LeCun, Bernhard Boser, John S Denker, Donnie Henderson, Richard E Howard, Wayne Hubbard, and Lawrence D Jackel · 1989
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Selective sampling using the query by committee algorithm
Yoav Freund, H Sebastian Seung, Eli Shamir, and Naftali Tishby · 1997
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Query learning with large margin classifiers
Colin Campbell, Nello Cristianini, Alex Smola, et al · 2000
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Less is more: Active learning with support vector machines
Greg Schohn and David Cohn · 2000
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Query by committee, linear separation and random walks
Shai Fine, Ran Gilad-Bachrach, and Eli Shamir · 2002
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Submodular functions and optimization
Satoru Fujishige · 2005
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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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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Active learning literature survey
Burr Settles · 2009
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Mnist handwritten digit database. at&t labs, 2010
Yann LeCun, Corinna Cortes, and CJ Burges · 2010
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Learning with submodular functions: A convex optimization perspective
Francis Bach · 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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Determinantal point processes for machine learning
Alex Kulesza and Ben Taskar · 2012
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One billion word benchmark for measuring progress in statistical language modeling
Ciprian Chelba, Tomas Mikolov, Mike Schuster, Qi Ge, Thorsten Brants, Phillipp Koehn, and Tony Robinson · 2013
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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A new active labeling method for deep learning
Dan Wang and Yi Shang · 2014
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Active nearest neighbors in changing environments
Christopher Berlind and Ruth Urner · 2015
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Submodular optimization and machine learning: Theoretical results, unifying and scalable algorithms, and applications
Rishabh Krishnan Iyer · 2015
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Alireza Makhzani, Jonathon Shlens, Navdeep Jaitly, Ian Goodfellow, and Brendan Frey · 2015
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Lazier than lazy greedy
Deep batch active learning by diverse, uncertain gradient lower bounds
Jordan T Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford, and Alekh Agarwal · 2019
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Submodular functions: from discrete to continuous domains
Francis Bach · 2019
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A memoization framework for scaling submodular optimization to large scale problems
Rishabh Iyer and Jeffrey Bilmes · 2019
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Learning from less data: A unified data subset selection and active learning framework for computer vision
Vishal Kaushal, Rishabh Iyer, Suraj Kothawade, Rohan Mahadev, Khoshrav Doctor, and Ganesh Ramakrishnan · 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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Baharan Mirzasoleiman, Ashwinkumar Badanidiyuru, Amin Karbasi, Jan Vondrák, and Andreas Krause · 2015
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Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 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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Character-level convolutional networks for text classification
Xiang Zhang, Junbo Zhao, and Yann LeCun · 2015
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Aligning books and movies: Towards story-like visual explanations by watching movies and reading books
Yukun Zhu, Ryan Kiros, Rich Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, and Sanja Fidler · 2015
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Youtube-8m: A large-scale video classification benchmark
Sami Abu-El-Haija, Nisarg Kothari, Joonseok Lee, Paul Natsev, George Toderici, Balakrishnan Varadarajan, and Sudheendra Vijayanarasimhan · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
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Variational adversarial active learning
Samarth Sinha, Sayna Ebrahimi, and Trevor Darrell · 2019
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nuscenes: A multimodal dataset for autonomous driving
Holger Caesar, Varun Bankiti, Alex H Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom · 2020
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Deep active learning for biased datasets via fisher kernel self-supervision
Denis Gudovskiy, Alec Hodgkinson, Takuya Yamaguchi, and Sotaro Tsukizawa · 2020
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The online submodular cover problem
Anupam Gupta and Roie Levin · 2020
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Glister: Generalization based data subset selection for efficient and robust learning
Krishnateja Killamsetty, Durga Sivasubramanian, Ganesh Ramakrishnan, and Rishabh Iyer · 2020
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Submodularity in action: From machine learning to signal processing applications
Ehsan Tohidi, Rouhollah Amiri, Mario Coutino, David Gesbert, Geert Leus, and Amin Karbasi · 2020
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Submodular combinatorial information measures with applications in machine learning
Rishabh Iyer, Ninad Khargoankar, Jeff Bilmes, and Himanshu Asanani · 2021
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Fairface: Face attribute dataset for balanced race, gender, and age for bias measurement and mitigation
Kimmo Karkkainen and Jungseock Joo · 2021
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Vishal Kaushal, Suraj Kothawade, Ganesh Ramakrishnan, Jeff Bilmes, and Rishabh Iyer · 2021
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