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The great success of modern machine learning models on large datasets is contingent on extensive computational resources with high financial and environmental costs.
An analysis of approximations for maximizing submodular set functions—i
Nemhauser, G. L., Wolsey, L. A., and Fisher, M. L · 1978
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An analysis of the greedy algorithm for the submodular set covering problem
Wolsey, L. A · 1982
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Clustering by means of medoids in statistical data analysis based on the, 1987
Kaufman, L., Rousseeuw, P., and Dodge, Y · 1987
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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
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Individual comparisons by ranking methods
Wilcoxon, F · 1992
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On coresets for k-means and k-median clustering
Har-Peled, S. and Mazumdar, S · 2004
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Submodular functions and optimization
Fujishige, S · 2005
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Learning multiple layers of features from tiny images
Krizhevsky, A · 2009
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Coresets, sparse greedy approximation, and the frank-wolfe algorithm
Clarkson, K. L · 2010
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Mnist handwritten digit database
LeCun, Y., Cortes, C., and Burges, C · 2010
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Das, A. and Kempe, D · 2011
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Coresets for robust training of neural networks against noisy labels
Mirzasoleiman, B., Cao, K., and Leskovec, J · 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
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Facility location: concepts, models, algorithms and case studies. series: Contributions to management science: edited by zanjirani farahani, reza and hekmatfar, masoud, heidelberg, germany, physica-verlag, 2009, 2011
Wolf, G. W · 2011
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Submodularity for data selection in machine translation
Kirchhoff, K. and Bilmes, J · 2014
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Submodular subset selection for large-scale speech training data
Wei, K., Liu, Y., Kirchhoff, K., Bartels, C., and Bilmes, J · 2014
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Unsupervised submodular subset selection for speech data
Wei, K., Liu, Y., Kirchhoff, K., and Bilmes, J · 2014
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Distributed submodular cover: Succinctly summarizing massive data
Mirzasoleiman, B., Karbasi, A., Badanidiyuru, A., and Krause, A · 2015
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Non-submodular function maximization subject to a matroid constraint, with applications
Gatmiry, K. and Gomez-Rodriguez, M · 2018
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Jia, X., Song, S., He, W., Wang, Y., Rong, H., Zhou, F., Xie, L., Guo, Z., Yang, Y., Yu, L., et al · 2018
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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 · 2019
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Schwartz, R., Dodge, J., Smith, N. A., and Etzioni, O · 2019
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Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A. C., and Fei-Fei, L · 2015
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Submodularity in data subset selection and active learning
Wei, K., Iyer, R., and Bilmes, J · 2015
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Sgdr: Stochastic gradient descent with warm restarts, 2017
Loshchilov, I. and Hutter, F · 2017
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Automatic differentiation in pytorch
Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., and Lerer, A · 2017
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Bayesian coreset construction via greedy iterative geodesic ascent
Campbell, T. and Broderick, T · 2018
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Restricted strong convexity implies weak submodularity
Elenberg, E. R., Khanna, R., Dimakis, A. G., Negahban, S., et al · 2018
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Strubell, E., Ganesh, A., and McCallum, A · 2019
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An empirical study of example forgetting during deep neural network learning
Toneva, M., Sordoni, A., des Combes, R. T., Trischler, A., Bengio, Y., and Gordon, G. J · 2019
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E2-train: Training state-of-the-art cnns with over 80% energy savings
Wang, Y., Jiang, Z., Chen, X., Xu, P., Zhao, Y., Lin, Y., and Wang, Z · 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
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Selection via proxy: Efficient data selection for deep learning, 2020
Coleman, C., Yeh, C., Mussmann, S., Mirzasoleiman, B., Bailis, P., Liang, P., Leskovec, J., and Zaharia, M · 2020
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Core-sets: Updated survey
Feldman, D · 2020
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The cost of training nlp models: A concise overview
Sharir, O., Peleg, B., and Shoham, Y · 2020
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Glister: Generalization based data subset selection for efficient and robust learning
Killamsetty, K., Sivasubramanian, D., Ramakrishnan, G., and Iyer, R · 2021
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