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We provide theoretical investigation of curriculum learning in the context of stochastic gradient descent when optimizing the convex linear regression loss.
The need for biases in learning generalizations
Mitchell, T. M · 1980
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The discipline of machine learning , volume 9
Mitchell, T. M · 2006
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Curriculum learning
Bengio, Y., Louradour, J., Collobert, R., and Weston, J · 2009
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ImageNet: A Large-Scale Hierarchical Image Database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
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Learning multiple layers of features from tiny images
Krizhevsky, A. and Hinton, G · 2009
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Flexible shaping: How learning in small steps helps
Krueger, K. A. and Dayan, P · 2009
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An analysis of single-layer networks in unsupervised feature learning
Coates, A., Lee, H., and Ng, A. Y · 2010
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Learning to learn
Thrun, S. and Pratt, L · 2012
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Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., and Fergus, R · 2013
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Cnn features off-the-shelf: an astounding baseline for recognition
Sharif Razavian, A., Azizpour, H., Sullivan, J., and Carlsson, S · 2014
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Zaremba, W. and Sutskever, I · 2014
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Compressing neural networks with the hashing trick
Chen, W., Wilson, J., Tyree, S., Weinberger, K., and Chen, Y · 2015
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Long-term recurrent convolutional networks for visual recognition and description
Basic level categorization facilitates visual object recognition
Wang, P. and Cottrell, G. W · 2015
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Deep speech 2: End-to-end speech recognition in english and mandarin
Amodei, D., Ananthanarayanan, S., Anubhai, R., Bai, J., Battenberg, E., Case, C., Casper, J., Catanzaro, B., Cheng, Q., Chen, G., et al · 2016
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Learning structured inference neural networks with label relations
Hu, H., Zhou, G.-T., Deng, Z., Liao, Z., and Mori, G · 2016
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Training region-based object detectors with online hard example mining
Shrivastava, A., Gupta, A., and Girshick, R. B · 2016
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Automated curriculum learning for neural networks
Graves, A., Bellemare, M. G., Menick, J., Munos, R., and Kavukcuoglu, K · 2017
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Donahue, J., Anne Hendricks, L., Guadarrama, S., Rohrbach, M., Venugopalan, S., Saenko, K., and Darrell, T · 2015
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Compression of deep convolutional neural networks for fast and low power mobile applications
Kim, Y.-D., Park, E., Yoo, S., Choi, T., Yang, L., and Shin, D · 2015
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Cased: Curriculum adaptive sampling for extreme data imbalance
Jesson, A., Guizard, N., Ghalehjegh, S. H., Goblot, D., Soudan, F., and Chapados, N · 2017
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