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Reducing the test time resource requirements of a neural network while preserving test accuracy is crucial for running inference on resource-constrained devices.
Comparing biases for minimal network construction with back-propagation
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Optimal brain damage
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A practical bayesian framework for backpropagation networks
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Efficient matlab computations with sparse and factored tensors
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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 · 2009
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Practical variational inference for neural networks
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Machine Learning: A Probabilistic Perspective
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Denton, E. L., Zaremba, W., Bruna, J., LeCun, Y., and Fergus, R · 2014
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Speeding up convolutional neural networks with low rank expansions
Jaderberg, M., Vedaldi, A., and Zisserman, A · 2014
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Speeding-up convolutional neural networks using fine-tuned cp-decomposition
Lebedev, V., Ganin, Y., Rakhuba, M., Oseledets, I., and Lempitsky, V · 2014
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New insights and perspectives on the natural gradient method
Martens, J · 2014
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Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A · 2014
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S. and Szegedy, C · 2015
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Neural networks with few multiplications
Lin, Z., Courbariaux, M., Memisevic, R., and Bengio, Y · 2015
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Optimizing neural networks with kronecker-factored approximate curvature
Martens, J. and Grosse, R · 2015
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Channel pruning for accelerating very deep neural networks
He, Y., Zhang, X., and Sun, J · 2017
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Mobilenets: Efficient convolutional neural networks for mobile vision applications
Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H · 2017
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Learning efficient convolutional networks through network slimming
Liu, Z., Li, J., Shen, Z., Huang, G., Yan, S., and Zhang, C · 2017
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Thinet: A filter level pruning method for deep neural network compression
Luo, J.-H., Wu, J., and Lin, W · 2017
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Noisy natural gradient as variational inference
Zhang, G., Sun, S., Duvenaud, D., and Grosse, R · 2017
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George, T., Laurent, C., Bouthillier, X., Ballas, N., and Vincent, P · 2018
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A scalable laplace approximation for neural networks
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MLPrune: Multi-layer pruning for automated neural network compression, 2019
Zeng, W. and Urtasun, R · 2019
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