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Deep neural networks have dramatically transformed machine learning, but their memory and energy demands are substantial.
A back-propagation algorithm with optimal use of hidden units
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Comparing biases for minimal network construction with back-propagation
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Optimal brain damage
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Memory bounded deep convolutional networks
Collins, M. D. and Kohli, P · 2014
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Compressing Deep Convolutional Networks using Vector Quantization
Gong, Y., Liu, L., Yang, M., and Bourdev, L · 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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Learning both weights and connections for efficient neural network
Han, S., Pool, J., Tran, J., and Dally, W · 2015
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
He, K., Zhang, X., Ren, S., and Sun, J · 2015
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2015
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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 · 2015
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Deep learning
LeCun, Y., Bengio, Y., and Hinton, G · 2015
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Deep learning in neural networks: An overview
Schmidhuber, J · 2015
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Dynamic network surgery for efficient dnns
Guo, Y., Yao, A., and Chen, Y · 2016
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Pruning filters for efficient convnets
Li, H., Kadav, A., Durdanovic, I., Samet, H., and Graf, H. P · 2017
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Variational dropout sparsifies deep neural networks
Molchanov, D., Ashukha, A., and Vetrov, D · 2017
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Mastering the game of Go without human knowledge
Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., Hubert, T., Baker, L., Lai, M., Bolton, A., Chen, Y., Lillicrap, T., Hui, F., Sifre, L., Driessche, G. v. d., Graepel, T., and Hassabis, D · 2017
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Principles of Neural Design
Sterling, P. and Laughlin, S · 2017
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms, 2017
Xiao, H., Rasul, K., and Vollgraf, R · 2017
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Reservoir transfer on analog neuromorphic hardware
He, X., Liu, T., Hadaeghi, F., and Jaeger, H · 2019
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Deep Learning Hardware: Past, Present, and Future
LeCun, Y · 2019
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Enhanced convolutional neural tangent kernels, 2019
Li, Z., Wang, R., Yu, D., Du, S. S., Hu, W., Salakhutdinov, R., and Arora, S · 2019
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Rethinking the value of network pruning
Liu, Z., Sun, M., Zhou, T., Huang, G., and Darrell, T · 2019
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One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers
Morcos, A., Yu, H., Paganini, M., and Tian, Y · 2019
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Surrogate Gradient Learning in Spiking Neural Networks: Bringing the Power of Gradient-based optimization to spiking neural networks, November 2019
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Designing energy-efficient convolutional neural networks using energy-aware pruning
Yang, T., Chen, Y., and Sze, V · 2017
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Deep rewiring: Training very sparse deep networks
Bellec, G., Kappel, D., Maass, W., and Legenstein, R · 2018
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JAX: composable transformations of Python+NumPy programs, 2018
Bradbury, J., Frostig, R., Hawkins, P., Johnson, M. J., Leary, C., Maclaurin, D., and Wanderman-Milne, S · 2018
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”learning-compression” algorithms for neural net pruning
Carreira-Perpinan, M. A. and Idelbayev, Y · 2018
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Neural tangent kernel: Convergence and generalization in neural networks
Jacot, A., Gabriel, F., and Hongler, C · 2018
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Learning sparse neural networks through l0 regularization
Louizos, C., Welling, M., and Kingma, D. P · 2018
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Neftci, E. O., Mostafa, H., and Zenke, F · 2019
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A deep learning framework for neuroscience
Richards, B. A., Lillicrap, T. P., Beaudoin, P., Bengio, Y., Bogacz, R., Christensen, A., Clopath, C., Costa, R. P., de Berker, A., Ganguli, S., Gillon, C. J., Hafner, D., Kepecs, A., Kriegeskorte, N., Latham, P., Lindsay, G. W., Miller, K. D., Naud, R., Pack, C. C., Poirazi, P., Roelfsema, P., Sacramento, J., Saxe, A., Scellier, B., Schapiro, A. C., Senn, W., Wayne, G., Yamins, D., Zenke, F., Zylberberg, J., Therien, D., and Kording, K. P · 2019
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Uncoupled isotonic regression via minimum Wasserstein deconvolution
Rigollet, P. and Weed, J · 2019
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Towards spike-based machine intelligence with neuromorphic computing
Roy, K., Jaiswal, A., and Panda, P · 2019
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Uncoupled regression from pairwise comparison data
Xu, L., Honda, J., Niu, G., and Sugiyama, M · 2019
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Efficient reward-based structural plasticity on a spinnaker 2 prototype
Yan, Y., Kappel, D., Neumärker, F., Partzsch, J., Vogginger, B., Höppner, S., Furber, S., Maass, W., Legenstein, R., and Mayr, C · 2019
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A critique of pure learning and what artificial neural networks can learn from animal brains
Zador, A. M · 2019
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What is the state of neural network pruning?, 2020
Blalock, D., Ortiz, J. J. G., Frankle, J., and Guttag, J · 2020
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Rigging the lottery: Making all tickets winners
Evci, U., Gale, T., Menick, J., Castro, P. S., and Elsen, E · 2020
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A signal propagation perspective for pruning neural networks at initialization
Lee, N., Ajanthan, T., Gould, S., and Torr, P. H. S · 2020
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Neural tangents: Fast and easy infinite neural networks in python
Novak, R., Xiao, L., Hron, J., Lee, J., Alemi, A. A., Sohl-Dickstein, J., and Schoenholz, S. S · 2020
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Picking winning tickets before training by preserving gradient flow
Wang, C., Zhang, G., and Grosse, R · 2020
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