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Neural networks are often represented as graphs of connections between neurons.
On the evolution of random graphs
Erdős, P. and Rényi, A · 1960
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The maximum connectivity of a graph
Harary, F · 1962
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Distributed representations
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Parallel distributed processing
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Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P · 1998
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Collective dynamics of ‘small-world’networks
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Efficient behavior of small-world networks
Latora, V. and Marchiori, M · 2001
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Statistical mechanics of complex networks
Albert, R. and Barabási, A.-L · 2002
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Graph theory methods for the analysis of neural connectivity patterns
Sporns, O · 2003
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Bassett, D. S. and Bullmore, E · 2006
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Krizhevsky, A · 2009
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Deep sparse rectifier neural networks
Glorot, X., Bordes, A., and Bengio, Y · 2011
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012
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Graph analysis of the human connectome: promise, progress, and pitfalls
Fornito, A., Zalesky, A., and Breakspear, M · 2013
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Information and efficiency in the nervous system—a synthesis
Sengupta, B., Stemmler, M. B., and Friston, K. J · 2013
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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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ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A. C., and Fei-Fei, L · 2015
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Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A · 2015
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Going deeper with convolutions
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Network science
Barabási, A.-L. and Pósfai, M · 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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Sgdr: Stochastic gradient descent with warm restarts
Loshchilov, I. and Hutter, F · 2016
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Xception: Deep learning with depthwise separable convolutions
Chollet, F · 2017
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Gpu kernels for block-sparse weights
Gray, S., Radford, A., and Kingma, D. P · 2017
Spontaneous behaviors drive multidimensional, brain-wide population activity
Stringer, C., Pachitariu, M., Steinmetz, N., Reddy, C. B., Carandini, M., and Harris, K. D · 2018
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Graph attention networks
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Group normalization
Wu, Y. and He, K · 2018
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Convolutional neural networks with alternately updated clique
Yang, Y., Zhong, Z., Shen, T., and Lin, Z · 2018
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ShuffleNet: An extremely efficient convolutional neural network for mobile devices
Zhang, X., Zhou, X., Lin, M., and Sun, J · 2018
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Learning transferable architectures for scalable image recognition
Zoph, B., Vasudevan, V., Shlens, J., and Le, Q. V · 2018
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Inductive representation learning on large graphs
Hamilton, W., Ying, Z., and Leskovec, 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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Densely connected convolutional networks
Huang, G., Liu, Z., van der Maaten, L., and Weinberger, K. Q · 2017
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Semi-supervised classification with graph convolutional networks
Kipf, T. N. and Welling, M · 2017
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Aggregated residual transformations for deep neural networks
Xie, S., Girshick, R., Dollár, P., Tu, Z., and He, K · 2017
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Neural architecture search with reinforcement learning
Zoph, B. and Le, Q. V · 2017
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Butterfly transform: An efficient fft based neural architecture design
Alizadeh, K., Farhadi, A., and Rastegari, M · 2019
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On the equivalence between graph isomorphism testing and function approximation with gnns
Chen, Z., Villar, S., Chen, L., and Bruna, J · 2019
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Learning fast algorithms for linear transforms using butterfly factorizations
Dao, T., Gu, A., Eichhorn, M., Rudra, A., and Ré, C · 2019
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Elsen, E., Dukhan, M., Gale, T., and Simonyan, K · 2019
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DARTS: Differentiable architecture search
Liu, H., Simonyan, K., and Yang, Y · 2019
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Towards artificial general intelligence with hybrid tianjic chip architecture
Pei, J., Deng, L., Song, S., Zhao, M., Zhang, Y., Wu, S., Wang, G., Zou, Z., Wu, Z., He, W., et al · 2019
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Regularized evolution for image classifier architecture search
Real, E., Aggarwal, A., Huang, Y., and Le, Q. V · 2019
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Efficientnet: Rethinking model scaling for convolutional neural networks
Tan, M. and Le, Q. V · 2019
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Discovering neural wirings
Wortsman, M., Farhadi, A., and Rastegari, M · 2019
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Exploring randomly wired neural networks for image recognition
Xie, S., Kirillov, A., Girshick, R., and He, K · 2019
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Cross-channel communication networks
Yang, J., Ren, Z., Gan, C., Zhu, H., and Parikh, D · 2019
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Nas-bench-101: Towards reproducible neural architecture search
Ying, C., Klein, A., Real, E., Christiansen, E., Murphy, K., and Hutter, F · 2019
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