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With ever-increasing dataset sizes, subset selection techniques are becoming increasingly important for a plethora of tasks.
Submodularity in data subset selection and active learning
Wei, K.; Iyer, R.; and Bilmes, J. 2015 · 1963
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An analysis of approximations for maximizing submodular set functions—I
Nemhauser, G. L.; Wolsey, L. A.; and Fisher, M. L. 1978 · 1978
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Backpropagation applied to handwritten zip code recognition
LeCun, Y.; Boser, B.; Denker, J. S.; Henderson, D.; Howard, R. E.; Hubbard, W.; and Jackel, L. D. 1989 · 1989
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Gradient-based learning applied to document recognition
LeCun, Y.; Bottou, L.; Bengio, Y.; and Haffner, P. 1998 · 1998
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Submodular functions and optimization
Fujishige, S. 2005 · 2005
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Learning multiple layers of features from tiny images
Krizhevsky, A.; Hinton, G.; et al. 2009 · 2009
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Active learning literature survey
Settles, B. 2009 · 2009
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A class of submodular functions for document summarization
Lin, H.; and Bilmes, J. 2011 · 2011
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Reading digits in natural images with unsupervised feature learning
Netzer, Y.; Wang, T.; Coates, A.; Bissacco, A.; Wu, B.; and Ng, A. Y. 2011 · 2011
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GLISTER: Generalization based Data Subset Selection for Efficient and Robust Learning
Killamsetty, K.; Sivasubramanian, D.; Ramakrishnan, G.; and Iyer, R. 2020 · 2012
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Multi-document summarization via submodularity
Li, J.; Li, L.; and Li, T. 2012 · 2012
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Submodularity in natural language processing: algorithms and applications
Lin, H. 2012 · 2012
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Learning mixtures of submodular shells with application to document summarization
Lin, H.; and Bilmes, J. A. 2012 · 2012
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Submodularity for Data Selection in Machine Translation
Kirchhoff, K.; and Bilmes, J. 2014 · 2014
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Submodular function maximization
Krause, A.; and Golovin, D. 2014 · 2014
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Learning mixtures of submodular functions for image collection summarization
Tschiatschek, S.; Iyer, R. K.; Wei, H.; and Bilmes, J. A. 2014 · 2014
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Learning deep features for scene recognition using places database
Zhou, B.; Lapedriza, A.; Xiao, J.; Torralba, A.; and Oliva, A. 2014 · 2014
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Video summarization by learning submodular mixtures of objectives
Gygli, M.; Grabner, H.; and Gool, L. 2015 · 2015
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Lazier than lazy greedy
Mirzasoleiman, B.; Badanidiyuru, A.; Karbasi, A.; Vondrák, J.; and Krause, A. 2015 · 2015
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Deep residual learning for image recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
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Query-focused extractive video summarization
Sharghi, A.; Gong, B.; and Shah, M. 2016 · 2016
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Video summarization with attention-based encoder-decoder networks
Ji, Z.; Xiong, K.; Pang, Y.; and Li, X. 2019 · 2019
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Hierarchical Variational Network for User-Diversified & Query-Focused Video Summarization
Jiang, P.; and Han, Y. 2019 · 2019
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Learning from less data: A unified data subset selection and active learning framework for computer vision
Kaushal, V.; Iyer, R.; Kothawade, S.; Mahadev, R.; Doctor, K.; and Ramakrishnan, G. 2019a · 2019
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A Framework Towards Domain Specific Video Summarization
Kaushal, V.; Iyer, R.; Kothawade, S.; Subramanian, S.; and Ramakrishnan, G. 2019b · 2019
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Demystifying Multi-Faceted Video Summarization: Tradeoff Between Diversity, Representation, Coverage and Importance
Kaushal, V.; Iyer, R. K.; Doctor, K.; Sahoo, A.; Dubal, P.; Kothawade, S.; Mahadev, R.; Dargan, K.; and Ramakrishnan, G. 2019c · 2019
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Towards abstractive multi-document summarization using submodular function-based framework, sentence compression and merging
Chali, Y.; Tanvee, M.; and Nayeem, M. T. 2017 · 2017
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Query-focused video summarization: Dataset, evaluation, and a memory network based approach
Sharghi, A.; Laurel, J. S.; and Gong, B. 2017 · 2017
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Query-adaptive video summarization via quality-aware relevance estimation
Vasudevan, A. B.; Gygli, M.; Volokitin, A.; and Van Gool, L. 2017 · 2017
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Recent advances in document summarization
Yao, J.-g.; Wan, X.; and Xiao, J. 2017 · 2017
Cited alongside, same era.
Places: A 10 million Image Database for Scene Recognition
Zhou, B.; Lapedriza, A.; Khosla, A.; Oliva, A.; and Torralba, A. 2017 · 2017
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Identifying medical diagnoses and treatable diseases by image-based deep learning
Kermany, D. S.; Goldbaum, M.; Cai, W.; Valentim, C. C.; Liang, H.; Baxter, S. L.; McKeown, A.; Yang, G.; Wu, X.; Yan, F.; et al. 2018 · 2018
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Diverse Neural Photo Album Summarization
Ozkose, Y. E.; Celikkale, B.; Erdem, E.; and Erdem, A. 2019 · 2019
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Image Corpus Representative Summarization
Singh, A.; Virmani, L.; and Subramanyam, A. 2019 · 2019
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Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds
Ash, J. T.; Zhang, C.; Krishnamurthy, A.; Langford, J.; and Agarwal, A. 2020 · 2020
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Implicit Diversity in Image Summarization
Celis, L. E.; and Keswani, V. 2020 · 2020
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The Online Submodular Cover Problem
Gupta, A.; and Levin, R. 2020 · 2020
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Coresets for data-efficient training of machine learning models
Mirzasoleiman, B.; Bilmes, J.; and Leskovec, J. 2020 · 2020
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Convolutional Hierarchical Attention Network for Query-Focused Video Summarization
Xiao, S.; Zhao, Z.; Zhang, Z.; Yan, X.; and Yang, M. 2020 · 2020
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Submodular combinatorial information measures with applications in machine learning
Iyer, R.; Khargoankar, N.; Bilmes, J.; and Asanani, H. 2021 · 2021
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GRAD-MATCH: A Gradient Matching Based Data Subset Selection for Efficient Learning
Killamsetty, K.; Sivasubramanian, D.; Mirzasoleiman, B.; Ramakrishnan, G.; De, A.; and Iyer, R. 2021 · 2021
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Medmnist classification decathlon: A lightweight automl benchmark for medical image analysis
Yang, J.; Shi, R.; and Ni, B. 2021 · 2021
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