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Coreset of a given dataset and loss function is usually a small weighed set that approximates this loss for every query from a given set of queries.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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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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Very large svm training using core vector machines
Ivor W Tsang, James Tin-Yau Kwok, and Pak-Ming Cheung · 2005
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Improved approximation algorithms for large matrices via random projections
Tamas Sarlos · 2006
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Generalized core vector machines
IW-H Tsang, JT-Y Kwok, and Jacek M Zurada · 2006
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Maximum margin coresets for active and noise tolerant learning
Sariel Har-Peled, Dan Roth, and Dav Zimak · 2007
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Sampling algorithms and coresets for \ \backslash ell_p regression
Anirban Dasgupta, Petros Drineas, Boulos Harb, Ravi Kumar, and Michael W Mahoney · 2009
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Universal ε \varepsilon -approximators for integrals
Michael Langberg and Leonard J Schulman · 2010
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Scalable training of mixture models via coresets
Dan Feldman, Matthew Faulkner, and Andreas Krause · 2011
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A unified framework for approximating and clustering data
Dan Feldman and Michael Langberg · 2011
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Subspace embeddings for the l1-norm with applications
Christian Sohler and David P Woodruff · 2011
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A coreset-based semi-supverised clustering using one-class support vector machines
Lei Gu · 2012
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Turning big data into tiny data: Constant-size coresets for k-means, pca and projective clustering
Dan Feldman, Melanie Schmidt, and Christian Sohler · 2013
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Building accurate 3d spatial networks to enable next generation intelligent transportation systems
Manohar Kaul, Bin Yang, and Christian S Jensen · 2013
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Dimensionality reduction for k-means clustering and low rank approximation
Michael B Cohen, Sam Elder, Cameron Musco, Christopher Musco, and Madalina Persu · 2015
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Lp row sampling by lewis weights
Michael B Cohen and Richard Peng · 2015
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Learning both weights and connections for efficient neural network
Song Han, Jeff Pool, John Tran, and William Dally · 2015
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New frameworks for offline and streaming coreset constructions
Vladimir Braverman, Dan Feldman, and Harry Lang · 2016
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Coresets for scalable bayesian logistic regression
Jonathan Huggins, Trevor Campbell, and Tamara Broderick · 2016
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Pruning filters for efficient convnets
Hao Li, Asim Kadav, Igor Durdanovic, Hanan Samet, and Hans Peter Graf · 2016
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Strong coresets for hard and soft bregman clustering with applications to exponential family mixtures
Mario Lucic, Olivier Bachem, and Andreas Krause · 2016
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Fifty years of pulsar candidate selection: from simple filters to a new principled real-time classification approach
Robert J Lyon, BW Stappers, Sally Cooper, JM Brooke, and JD Knowles · 2016
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Jeff M Phillips · 2016
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Domain adaptation of dnn acoustic models using knowledge distillation
T. Asami, R. Masumura, Y. Yamaguchi, H. Masataki, and Y. Aono · 2017
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More is less: A more complicated network with less inference complexity
Xuanyi Dong, Junshi Huang, Yi Yang, and Shuicheng Yan · 2017
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Large-scale domain adaptation via teacher-student learning
Jinyu Li, Michael L. Seltzer, Xi Wang, Rui Zhao, and Yifan Gong · 2017
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Learning efficient convolutional networks through network slimming
Zhuang Liu, Jianguo Li, Zhiqiang Shen, Gao Huang, Shoumeng Yan, and Changshui Zhang · 2017
Cited alongside, same era.
Gradient episodic memory for continual learning
Felipe Petroski Such, Aditya Rawal, Joel Lehman, Kenneth O. Stanley, and Jeff Clune · 2019
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Prototype reminding for continual learning
Mengmi Zhang, Tao Wang, Joo Hwee Lim, and Jiashi Feng · 2019
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Coresets via bilevel optimization for continual learning and streaming
Zalán Borsos, Mojmir Mutny, and Andreas Krause · 2020
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Storage efficient and dynamic flexible runtime channel pruning via deep reinforcement learning
Jianda Chen, Shangyu Chen, and Sinno Jialin Pan · 2020
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Dan Feldman · 2020
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David Lopez-Paz and Marc’Aurelio Ranzato · 2017
Cited alongside, same era.
Thinet: A filter level pruning method for deep neural network compression
Jian-Hao Luo, Jianxin Wu, and Weiyao Lin · 2017
Cited alongside, same era.
icarl: Incremental classifier and representation learning
Sylvestre-Alvise Rebuffi, Alexander Kolesnikov, Georg Sperl, and Christoph H. Lampert · 2017
Cited alongside, same era.
Soft filter pruning for accelerating deep convolutional neural networks
Yang He, Guoliang Kang, Xuanyi Dong, Yanwei Fu, and Yi Yang · 2018
Cited alongside, same era.
Learning without forgetting
Zhizhong Li and Derek Hoiem · 2018
Cited alongside, same era.
On coresets for logistic regression
Alexander Munteanu, Chris Schwiegelshohn, Christian Sohler, and David Woodruff · 2018
Cited alongside, same era.
Dataset distillation, 2018
Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba, and Alexei A. Efros · 2018
Cited alongside, same era.
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Federated learning via synthetic data
Jack Goetz and Ambuj Tewari · 2020
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What does a pruned deep neural network forgets?
Sara Hooker, Aaron Courville, Yann Dauphin, and Andrea Frome · 2020
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Sets clustering
Ibrahim Jubran, Murad Tukan, Alaa Maalouf, and Dan Feldman · 2020
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Operation-aware soft channel pruning using differentiable masks
Minsoo Kang and Bohyung Han · 2020
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Provable filter pruning for efficient neural networks
Lucas Liebenwein, Cenk Baykal, Harry Lang, Dan Feldman, and Daniela Rus · 2020
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Tight sensitivity bounds for smaller coresets
Alaa Maalouf, Adiel Statman, and Dan Feldman · 2020
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Coresets for data-efficient training of machine learning models
Baharan Mirzasoleiman, Jeff Bilmes, and Jure Leskovec · 2020
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Data-independent structured pruning of neural networks via coresets
Ben Mussay, Daniel Feldman, Samson Zhou, Vladimir Braverman, and Margarita Osadchy · 2020
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Data-independent neural pruning via coresets
Ben Mussay, Margarita Osadchy, Vladimir Braverman, Samson Zhou, and Dan Feldman · 2020
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Coresets for near-convex functions
Morad Tukan, Alaa Maalouf, and Dan Feldman · 2020
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On coresets for support vector machines
Murad Tukan, Cenk Baykal, Dan Feldman, and Daniela Rus · 2020
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Good subnetworks provably exist: Pruning via greedy forward selection
Mao Ye, Chengyue Gong, Lizhen Nie, Denny Zhou, Adam Klivans, and Qiang Liu · 2020
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Dataset condensation with gradient matching
Bo Zhao, Konda Reddy Mopuri, and Hakan Bilen · 2020
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Open source code for all the algorithms presented in this paper, 2021
Code · 2021
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Coresets for decision trees of signals
Ibrahim Jubran, Ernesto Evgeniy Sanches Shayda, Ilan Newman, and Dan Feldman · 2021
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Coresets for the average case error for finite query sets
Alaa Maalouf, Ibrahim Jubran, Murad Tukan, and Dan Feldman · 2021
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Deep learning meets projective clustering
Alaa Maalouf, Harry Lang, Daniela Rus, and Dan Feldman · 2021
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