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This paper introduces Selective-Backprop, a technique that accelerates the training of deep neural networks (DNNs) by prioritizing examples with high loss at each iteration.
Are loss functions all the same?
Rosasco, L., De Vito, E., Caponnetto, A., Piana, M., and Verri, A · 2004
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To recognize shapes, first learn to generate images
Hinton, G. E · 2007
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Curriculum learning
Bengio, Y., Louradour, J., Collobert, R., and Weston, J · 2009
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Learning multiple layers of features from tiny images
Krizhevsky, A · 2009
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Self-paced learning for latent variable models
Kumar, M. P., Packer, B., and Koller, D · 2010
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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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Variance reduction in sgd by distributed importance sampling
Alain, G., Lamb, A., Sankar, C., Courville, A., and Bengio, Y · 2015
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Active Sampler: Light-weight accelerator for complex data analytics at scale
Gao, J., Jagadish, H. V., and Ooi, B. C · 2015
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Self-paced curriculum learning
Jiang, L., Meng, D., Zhao, Q., Shan, S., and Hauptmann, A. G · 2015
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Online batch selection for faster training of neural networks
Loshchilov, I. and Hutter, F · 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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Prioritized experience replay
Schaul, T., Quan, J., Antonoglou, I., and Silver, D · 2016
Cited alongside, same era.
Training region-based object detectors with online hard example mining
Shrivastava, A., Gupta, A., and Girshick, R · 2016
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Zagoruyko, S. and Komodakis, N · 2016
Cited alongside, same era.
Active Bias: Training a more accurate neural network by emphasizing high variance samples
Chang, H.-S., Learned-Miller, E. G., and McCallum, A · 2017
Cited alongside, same era.
Improved regularization of convolutional neural networks with cutout
DeVries, T. and Taylor, G. W · 2017
Cited alongside, same era.
Densely connected convolutional networks
Demystifying parallel and distributed deep learning: An in-depth concurrency analysis
Ben-Nun, T. and Hoefler, T · 2018
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signSGD: Compressed Optimisation for Non-Convex Problems
Bernstein, J., Wang, Y.-X., Azizzadenesheli, K., and Anandkumar, A · 2018
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MentorNet: Learning data-driven curriculum for very deep neural networks on corrupted labels
Jiang, L., Zhou, Z., Leung, T., Li, L.-J., and Fei-Fei, L · 2018
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Training deep models faster with robust, approximate importance sampling
Johnson, T. B. and Guestrin, C · 2018
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Not all samples are created equal: Deep learning with importance sampling
Katharopoulos, A. and Fleuret, F · 2018
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Deep k-nearest neighbors: Towards confident, interpretable and robust deep learning
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Huang, G., Liu, Z., van der Maaten, L., and Weinberger, K. Q · 2017
Cited alongside, same era.
In-datacenter performance analysis of a tensor processing unit
Jouppi, N. P., Young, C., Patil, N., Patterson, D., Agrawal, G., Bajwa, R., Bates, S., Bhatia, S., Boden, N., Borchers, A., Boyle, R., Cantin, P.-l., Chao, C., Clark, C., Coriell, J., Daley, M., Dau, M., Dean, J., Gelb, B., Ghaemmaghami, T. V., Gottipati, R., Gulland, W., Hagmann, R., Ho, C. R., Hogberg, D., Hu, J., Hundt, R., Hurt, D., Ibarz, J., Jaffey, A., Jaworski, A., Kaplan, A., Khaitan, H., Killebrew, D., Koch, A., Kumar, N., Lacy, S., Laudon, J., Law, J., Le, D., Leary, C., Liu, Z., Lucke, K., Lundin, A., MacKean, G., Maggiore, A., Mahony, M., Miller, K., Nagarajan, R., Narayanaswami, R., Ni, R., Nix, K., Norrie, T., Omernick, M., Penukonda, N., Phelps, A., Ross, J., Ross, M., Salek, A., Samadiani, E., Severn, C., Sizikov, G., Snelham, M., Souter, J., Steinberg, D., Swing, A., Tan, M., Thorson, G., Tian, B., Toma, H., Tuttle, E., Vasudevan, V., Walter, R., Wang, W., Wilcox, E., and Yoon, D. H · 2017
Cited alongside, same era.
Biased importance sampling for deep neural network training
Katharopoulos, A. and Fleuret, F · 2017
Cited alongside, same era.
Focal loss for dense object detection
Lin, T.-Y., Goyal, P., Girshick, R. B., He, K., and Dollár, P · 2017
Cited alongside, same era.
Self-paced co-training
Ma, F., Meng, D., Xie, Q., Li, Z., and Dong, X · 2017
Cited alongside, same era.
Convnet-Benchmarks
Chintala, S
Cited in the paper.
Papernot, N. and McDaniel, P · 2018
Later among the works it cites.
Learning to reweight examples for robust deep learning
Ren, M., Zeng, W., Yang, B., and Urtasun, R · 2018
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MobileNetV2: Inverted residuals and linear bottlenecks
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.-C · 2018
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Train CIFAR10 with PyTorch
Kuang, L · 2019
Closest in time.
Autoassist: A framework to accelerate training of deep neural networks
Zhang, J., Yu, H.-F., and Dhillon, I. S · 2019
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