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Neural networks have achieved state of the art performance across a wide variety of machine learning tasks, often with large and computation-heavy models.
Rethinking full connectivity in recurrent neural networks
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Second order derivatives for network pruning: Optimal brain surgeon, in: NeurIPS
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Pruning algorithms – A survey
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The MNIST database of handwritten digits
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Neural machine translation by jointly learning to align and translate, in: ICLR
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Exploiting linear structure within convolutional networks for efficient evaluation, in: NeurIPS
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Adam: A method for stochastic optimization, in: ICLR
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Recurrent neural network regularization
Zaremba, W., Sutskever, I., Vinyals, O., 2014 · 2014
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Distilling the knowledge in a neural network
Hinton, G., Vinyals, O., Dean, J., 2015 · 2015
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Learning neural network architectures using backpropagation, in: ICLR
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Dynamic capacity networks, in: ICML
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Learning sparse neural networks through l _ 0 l\_0 regularization, in: ICLR
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Variational dropout sparsifies deep neural networks, in: ICML
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Outrageously large neural networks: The sparsely-gated mixture-of-experts layer, in: ICLR
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Learning intrinsic sparse structures within long short-term memory, in: ICLR
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To prune, or not to prune: exploring the efficacy of pruning for model compression
Zhu, M., Gupta, S., 2017 · 2017
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Courbariaux, M., Hubara, I., Soudry, D., El-Yaniv, R., Bengio, Y., 2016 · 2016
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Dynamic network surgery for efficient DNNs, in: NeurIPS
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Eie: efficient inference engine on compressed deep neural network, in: ISCA
Han, S., Liu, X., Mao, H., Pu, J., Pedram, A., Horowitz, M.A., Dally, W.J., 2016 · 2016
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Network trimming: A data-driven neuron pruning approach towards efficient deep architectures
Hu, H., Peng, R., Tai, Y.W., Tang, C.K., 2016 · 2016
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Pointer sentinel mixture models, in: ICLR
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Learning structured sparsity in deep neural networks, in: NeurIPS
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Adaptive neural networks for fast test-time prediction, in: ICML
Bolukbasi, T., Wang, J., Dekel, O., Saligrama, V., 2017 · 2017
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Predefined sparseness in recurrent sequence models, in: CoNLL
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Dynamic channel pruning: Feature boosting and suppression, in: ICLR
Gao, X., Zhao, Y., Dudziak, L., Mullins, R., Xu, C.z., 2018 · 2018
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Soft filter pruning for accelerating deep convolutional neural networks, in: IJCAI
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Optimization based layer-wise magnitude-based pruning for DNN compression., in: IJCAI
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Hierarchical block sparse neural networks
Vooturi, D.T., Mudigree, D., Avancha, S., 2018 · 2018
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Blockdrop: Dynamic inference paths in residual networks, in: CVPR
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You look twice: GaterNet for dynamic filter selection in CNNs, in: CVPR
Chen, Z., Li, Y., Bengio, S., Si, S., 2019 · 2019
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Dynamic block sparse reparameterization of convolutional neural networks, in: ICCV Workshops
Varma, G., Kothapalli, K., et al., 2019 · 2019
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Analyzing multi-head self-attention: Specialized heads do the heavy lifting, the rest can be pruned, in: ACL
Voita, E., Talbot, D., Moiseev, F., Sennrich, R., Titov, I., 2019 · 2019
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Neural network distiller
Zmora, N., Jacob, G., Novik, G., 2019 · 2019
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