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Data selection methods, such as active learning and core-set selection, are useful tools for machine learning on large datasets.
Query by committee
H Sebastian Seung, Manfred Opper, and Haim Sompolinsky · 1992
Earlier work this paper cites.
Heterogeneous uncertainty sampling for supervised learning
David D Lewis and Jason Catlett · 1994
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A sequential algorithm for training text classifiers
David D Lewis and William A Gale · 1994
Earlier work this paper cites.
Semi-supervised self-training of object detection models, January 2005
Charles Rosenberg, Martial Hebert, and Henry Schneiderman · 2005
Earlier work this paper cites.
Core vector machines: Fast svm training on very large data sets
Ivor W Tsang, James T Kwok, and Pak-Ming Cheung · 2005
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Smaller coresets for k-median and k-means clustering
Sariel Har-Peled and Akash Kushal · 2007
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An approach to text corpus construction which cuts annotation costs and maintains reusability of annotated data
Katrin Tomanek, Joachim Wermter, and Udo Hahn · 2007
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
Earlier work this paper cites.
Bayesian active learning for classification and preference learning
Neil Houlsby, Ferenc Huszár, Zoubin Ghahramani, and Máté Lengyel · 2011
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From theories to queries: Active learning in practice
Burr Settles · 2011
Earlier work this paper cites.
Facility location: concepts, models, algorithms and case studies., 2011
Gert W Wolf · 2011
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
Earlier work this paper cites.
Active learning
Burr Settles · 2012
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Using document summarization techniques for speech data subset selection
Kai Wei, Yuzong Liu, Katrin Kirchhoff, and Jeff Bilmes · 2013
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
Earlier work this paper cites.
Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
Earlier work this paper cites.
Learning mixtures of submodular functions for image collection summarization
Sebastian Tschiatschek, Rishabh K Iyer, Haochen Wei, and Jeff A Bilmes · 2014
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Submodular subset selection for large-scale speech training data
Kai Wei, Yuzong Liu, Katrin Kirchhoff, Chris Bartels, and Jeff Bilmes · 2014
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Unsupervised data selection and word-morph mixed language model for tamil low-resource keyword search
Chongjia Ni, Cheung-Chi Leung, Lei Wang, Nancy F Chen, and Bin Ma · 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
Cited alongside, same era.
Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
Cited alongside, same era.
Text understanding from scratch
Xiang Zhang and Yann LeCun · 2015
Cited alongside, same era.
Accurate, large minibatch sgd: Training imagenet in 1 hour
Priya Goyal, Piotr Dollár, Ross Girshick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia, and Kaiming He · 2017
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Neural collaborative filtering
Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua · 2017
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
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Paulius Micikevicius, Sharan Narang, Jonah Alben, Gregory Diamos, Erich Elsen, David Garcia, Boris Ginsburg, Michael Houston, Oleksii Kuchaiev, Ganesh Venkatesh, et al · 2017
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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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Character-level convolutional networks for text classification
Xiang Zhang, Junbo Zhao, and Yann LeCun · 2015
Cited alongside, same era.
Very deep convolutional networks for text classification
Alexis Conneau, Holger Schwenk, Loïc Barrault, and Yann Lecun · 2016
Cited alongside, same era.
Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani · 2016
Cited alongside, same era.
Coresets for scalable bayesian logistic regression
Jonathan Huggins, Trevor Campbell, and Tamara Broderick · 2016
Cited alongside, same era.
Squeezenet: Alexnet-level accuracy with 50x fewer parameters and< 0.5 mb model size
Forrest N Iandola, Song Han, Matthew W Moskewicz, Khalid Ashraf, William J Dally, and Kurt Keutzer · 2016
Cited alongside, same era.
Bag of tricks for efficient text classification
Armand Joulin, Edouard Grave, Piotr Bojanowski, and Tomas Mikolov · 2016
Cited alongside, same era.
Exploring the limits of language modeling
Rafal Jozefowicz, Oriol Vinyals, Mike Schuster, Noam Shazeer, and Yonghui Wu · 2016
Cited alongside, same era.
Deep active learning for named entity recognition
Yanyao Shen, Hyokun Yun, Zachary Lipton, Yakov Kronrod, and Animashree Anandkumar · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Aggregated residual transformations for deep neural networks
Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He · 2017
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Bayesian coreset construction via greedy iterative geodesic ascent
Trevor Campbell and Tamara Broderick · 2018
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The lottery ticket hypothesis: Finding sparse, trainable neural networks
Jonathan Frankle and Michael Carbin · 2018
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On the relationship between data efficiency and error for uncertainty sampling
Stephen Mussmann and Percy Liang · 2018
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Active learning for interactive neural machine translation of data streams
Álvaro Peris and Francisco Casacuberta · 2018
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Mobilenetv2: Inverted residuals and linear bottlenecks
Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen · 2018
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Active learning for convolutional neural networks: A core-set approach
Ozan Sener and Silvio Savarese · 2018
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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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An empirical study of example forgetting during deep neural network learning
Mariya Toneva, Alessandro Sordoni, Remi Tachet des Combes, Adam Trischler, Yoshua Bengio, and Geoffrey J. Gordon · 2019
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