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More accurate machine learning models often demand more computation and memory at test time, making them difficult to deploy on CPU- or memory-constrained devices.
Extracting tree-structured representations of trained networks
Craven, M. and Shavlik, J. W · 1996
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Gradient-based learning applied to document recognition
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Random forests
Breiman, L · 2001
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Multiscale structural similarity for image quality assessment
Wang, Z., Simoncelli, E. P., and Bovik, A. C · 2003
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Model compression
Bucila, C., Caruana, R., and Niculescu-Mizil, A · 2006
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Learning multiple layers of features from tiny images
Krizhevsky, A. and Hinton, G · 2009
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Scikit-learn: Machine learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E · 2011
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L1-based compression of random forest models
Joly, A., Schnitzler, F., Geurts, P., and Wehenkel, L · 2012
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Predicting parameters in deep learning
Denil, M., Shakibi, B., Dinh, L., De Freitas, N., et al · 2013
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Do deep nets really need to be deep?
Ba, J. and Caruana, R · 2014
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Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
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Caffe: Convolutional architecture for fast feature embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., and Darrell, T · 2014
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Learning small-size dnn with output-distribution-based criteria
Li, J., Zhao, R., Huang, J.-T., and Gong, Y · 2014
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Network in network
Lin, M., Chen, Q., and Yan, S · 2014
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TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., and Zheng, X · 2015
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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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Learning both weights and connections for efficient neural network
Han, S., Pool, J., Tran, J., and Dally, W · 2015
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Distilling the knowledge in a neural network
Hinton, G., Vinyals, O., and Dean, J · 2015
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Unsupervised representation learning with deep convolutional generative adversarial networks
UCI machine learning repository, 2017
Dheeru, D. and Karra Taniskidou, E · 2017
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Distilling a neural network into a soft decision tree
Frosst, N. and Hinton, G · 2017
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Mlbench: How good are machine learning clouds for binary classification tasks on structured data
Liu, Y., Zhang, H., Zeng, L., Wu, W., and Zhang, C · 2017
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Conditional image synthesis with auxiliary classifier GANs
Odena, A., Olah, C., and Shlens, J · 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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https://github.com/King-Of-Knights/Keras-ACGAN-CIFAR10
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Radford, A., Metz, L., and Chintala, S · 2015
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A note on the evaluation of generative models
Theis, L., Oord, A. v. d., and Bethge, M · 2015
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https://github.com/chengshengchan/model_compression
Chan, J · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Compressing random forests
Painsky, A. and Rosset, S · 2016
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Improved techniques for training gans
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., and Chen, X · 2016
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Zagoruyko, S. and Komodakis, N · 2016
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Globally induced forest: A prepruning compression scheme
Begon, J.-M., Joly, A., and Geurts, P · 2017
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Tuya · 2017
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Do deep convolutional nets really need to be deep and convolutional?
Urban, G., Geras, K. J., Kahou, S. E., Aslan, O., Wang, S., Caruana, R., Mohamed, A., Philipose, M., and Richardson, M · 2017
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Seqgan: Sequence generative adversarial nets with policy gradient
Yu, L., Zhang, W., Wang, J., and Yu, Y · 2017
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Gan augmentation: Augmenting training data using generative adversarial networks
Bowles, C., Chen, L., Guerrero, R., Bentley, P., Gunn, R., Hammers, A., Dickie, D. A., Hernández, M. V., Wardlaw, J., and Rueckert, D · 2018
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https://github.com/peterliht/knowledge-distillation-pytorch
Li, H · 2018
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Lossless (and lossy) compression of random forests
Painsky, A. and Rosset, S · 2018
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Adversarial learning of portable student networks
Wang, Y., Xu, C., Xu, C., and Tao, D · 2018
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Training student networks for acceleration with conditional adversarial networks
Xu, Z., Hsu, Y.-C., and Huang, J · 2018
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