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Most artificial networks today rely on dense representations, whereas biological networks rely on sparse representations.
Dropout: A Simple Way to Prevent Neural Networks from Overfitting
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Sparse Distributed Memory
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Backpropagation Applied to Handwritten Zip Code Recognition
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On the k-winners-take-all network
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Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · 1998
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Sparse deep belief net model for visual area V2
Lee, H., Ekanadham, C., & Ng, A. Y. (2008) · 2008
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Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations
Lee, H., Grosse, R., Ranganath, R., & Ng, A. Y. (2009) · 2009
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3D Object Recognition with Deep Belief Nets
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Cortical Learning Algorithm and Hierarchical Temporal Memory. URL http://numenta.org/resources/HTM{_}CorticalLearningAlgorithms.pdf
Hawkins, J., Ahmad, S., & Dubinsky, D. (2011) · 2011
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k-Sparse Autoencoders. URL http://arxiv.org/abs/1312.5663
Makhzani, A., & Frey, B. (2013) · 2013
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Compete to Compute
Srivastava, R. K., Masci, J., Kazerounian, S., Gomez, F., & Schmidhuber, J. (2013) · 2013
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Intriguing properties of neural networks. URL http://arxiv.org/abs/1312.6199
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., & Fergus, R. (2013) · 2013
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Simonyan, K., & Zisserman, A. (2014) · 2014
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Learning both Weights and Connections for Efficient Neural Network
Han, S., Pool, J., Tran, J., & Dally, W. (2015) · 2015
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Ioffe, S., & Szegedy, C. (2015) · 2015
Variational Dropout Sparsifies Deep Neural Networks. URL http://arxiv.org/abs/1701.05369
Molchanov, D., Ashukha, A., & Vetrov, D. (2017) · 2017
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Deep Residual Learning for Small-Footprint Keyword Spotting. URL https://arxiv.org/abs/1710.10361
Tang, R., & Lin, J. (2017) · 2017
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Speech Commands: A public dataset for single-word speech recognition
Warden, P. (2017) · 2017
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The Sparse Manifold Transform
Chen, Y., Paiton, D., & Olshausen, B. (2018) · 2018
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Frankle, J., & Carbin, M. (2018) · 2018
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Cited alongside, same era.
Winner-take-all autoencoders
Makhzani, A., & Frey, B. (2015) · 2015
Cited alongside, same era.
Convolutional neural networks for small-footprint keyword spotting
Sainath, T. N., & Parada, C. (2015) · 2015
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Ahmad, S., & Hawkins, J. (2016) · 2016
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Pruning Filters for Efficient ConvNets. URL http://arxiv.org/abs/1608.08710
Li, H., Kadav, A., Durdanovic, I., Samet, H., & Graf, H. P. (2016) · 2016
Cited alongside, same era.
The HTM Spatial Pooler – a neocortical algorithm for online sparse distributed coding
Cui, Y., Ahmad, S., & Hawkins, J. (2017) · 2017
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Deep Residual Learning for Image Recognition. URL http://arxiv.org/abs/1512.03385
He, K., Zhang, X., Ren, S., & Sun, J. (2015a)
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He, K., Zhang, X., Ren, S., & Sun, J. (2015b)
Cited in the paper.
Sparse DNNs with Improved Adversarial Robustness
Guo, Y., Zhang, C., Zhang, C., & Chen, Y. (2018) · 2018
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Lee, N., Ajanthan, T., & Torr, P. H. S. (2018) · 2018
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Sparse Unsupervised Capsules Generalize Better. URL http://arxiv.org/abs/1804.06094
Rawlinson, D., Ahmed, A., & Kowadlo, G. (2018) · 2018
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The Elephant in the Room. URL http://arxiv.org/abs/1808.03305
Rosenfeld, A., Zemel, R., & Tsotsos, J. K. (2018) · 2018
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pytorch-speech-commands. URL https://github.com/tugstugi/pytorch-speech-commands
Tuguldur, E.-O. (2018) · 2018
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