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Convolutional neural networks are among the most successful architectures in deep learning with this success at least partially attributable to the efficacy of spatial invariance as an inductive bias.
Shape and arrangement of columns in cat’s striate cortex
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Receptive fields and functional architecture of monkey striate cortex
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Self-organization of orientation sensitive cells in the striate cortex
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Cognitron: A self-organizing multilayered neural network
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Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position
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Dendritic field size and morphology of midget and parasol ganglion cells of the human retina
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Simplifying neural networks by soft weight-sharing
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Natural image statistics and efficient coding
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Natural image statistics and neural representation
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Eccentricity bias as an organizing principle for human high-order object areas
Hasson, U., Levy, I., Behrmann, M., Hendler, T., and Malach, R · 2002
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Retinotopic organization of human ventral visual cortex
Arcaro, M. J., McMains, S. A., Singer, B. D., and Kastner, S · 2009
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Large-scale deep unsupervised learning using graphics processors
Raina, R., Madhavan, A., and Ng, A. Y · 2009
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Large-scale object recognition with cuda-accelerated hierarchical neural networks
Uetz, R. and Behnke, S · 2009
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Emergence of complex-like cells in a temporal product network with local receptive fields
Gregor, K. and LeCun, Y · 2010
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Tiled convolutional neural networks
Le, Q. V., Ngiam, J., Chen, Z., Chia, D., Koh, P. W., and Ng, A. Y · 2010
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Large scale distributed deep networks
Dean, J., Corrado, G., Monga, R., Chen, K., Devin, M., Mao, M., Ranzato, M., Senior, A., Tucker, P., Yang, K., et al · 2012
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Improving neural networks by preventing co-adaptation of feature detectors
Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R. R · 2012
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Building high-level features using large scale unsupervised learning
Le, Q. V., Ranzato, M., Monga, R., Devin, M., Chen, K., Corrado, G. S., Dean, J., and Ng, A. Y · 2012
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Deep learning with cots hpc systems
Coates, A., Huval, B., Wang, T., Wu, D., Catanzaro, B., and Andrew, N · 2013
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Multi-digit number recognition from street view imagery using deep convolutional neural networks
Goodfellow, I. J., Bulatov, Y., Ibarz, J., Arnoud, S., and Shet, V · 2013
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Parallel, multi-stage processing of colors, faces and shapes in macaque inferior temporal cortex
Lafer-Sousa, R. and Conway, B. R · 2013
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Deep autoregressive networks
Gregor, K., Danihelka, I., Mnih, A., Blundell, C., and Wierstra, D · 2014
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One weird trick for parallelizing convolutional neural networks
Krizhevsky, A · 2014
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Retinotopy versus face selectivity in macaque visual cortex
Rajimehr, R., Bilenko, N. Y., Vanduffel, W., and Tootell, R. B · 2014
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Development of the macaque face-patch system
Livingstone, M. S., Vincent, J. L., Arcaro, M. J., Srihasam, K., Schade, P. F., and Savage, T · 2017
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Deciding how to decide: Dynamic routing in artificial neural networks
McGill, M. and Perona, P · 2017
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Dynamic routing between capsules
Sabour, S., Frosst, N., and Hinton, G. E · 2017
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
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Residual attention network for image classification
Wang, F., Jiang, M., Qian, C., Yang, S., Li, C., Zhang, H., Wang, X., and Tang, X · 2017
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Assessing the scalability of biologically-motivated deep learning algorithms and architectures
Bartunov, S., Santoro, A., Richards, B., Marris, L., Hinton, G. E., and Lillicrap, T · 2018
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Novel domain formation reveals proto-architecture in inferotemporal cortex
Srihasam, K., Vincent, J. L., and Livingstone, M. S · 2014
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Deep learning face representation from predicting 10,000 classes
Sun, Y., Wang, X., and Tang, X · 2014
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DeepFace: Closing the gap to human-level performance in face verification
Taigman, Y., Yang, M., Ranzato, M., and Wolf, L · 2014
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Neural machine translation by jointly learning to align and translate
Bahdanau, D., Cho, K., and Bengio, Y · 2015
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Deep learning
LeCun, Y., Bengio, Y., and Hinton, G · 2015
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FaceNet: A unified embedding for face recognition and clustering
Schroff, F., Kalenichenko, D., and Philbin, J · 2015
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Deepid3: Face recognition with very deep neural networks
Sun, Y., Liang, D., Wang, X., and Tang, X · 2015
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Squeeze-and-excitation networks
Hu, J., Shen, L., and Sun, G · 2018
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Learn to pay attention
Jetley, S., Lord, N. A., Lee, N., and Torr, P · 2018
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An intriguing failing of convolutional neural networks and the CoordConv solution
Liu, R., Lehman, J., Molino, P., Such, F. P., Frank, E., Sergeev, A., and Yosinski, J · 2018
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Bayesian deep convolutional networks with many channels are gaussian processes
Novak, R., Xiao, L., Bahri, Y., Lee, J., Yang, G., Hron, J., Abolafia, D. A., Pennington, J., and Sohl-Dickstein, J · 2018
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Non-local neural networks
Wang, X., Girshick, R., Gupta, A., and He, K · 2018
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CBAM: Convolutional block attention module
Woo, S., Park, J., Lee, J.-Y., and So Kweon, I · 2018
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Attention augmented convolutional networks
Bello, I., Zoph, B., Vaswani, A., Shlens, J., and Le, Q. V · 2019
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Finding the needle in the haystack with convolutions: on the benefits of architectural bias
d’Ascoli, S., Sagun, L., Biroli, G., and Bruna, J · 2019
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Attention branch network: Learning of attention mechanism for visual explanation
Fukui, H., Hirakawa, T., Yamashita, T., and Fujiyoshi, H · 2019
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Local relation networks for image recognition
Hu, H., Zhang, Z., Xie, Z., and Lin, S · 2019
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Learning what and where to attend with humans in the loop
Linsley, D., Shiebler, D., Eberhardt, S., and Serre, T · 2019
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Stand-alone self-attention in vision models
Ramachandran, P., Parmar, N., Vaswani, A., Bello, I., Levskaya, A., and Shlens, J · 2019
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Condconv: Conditionally parameterized convolutions for efficient inference
Yang, B., Bender, G., Le, Q. V., and Ngiam, J · 2019
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Latentgnn: Learning efficient non-local relations for visual recognition
Zhang, S., He, X., and Yan, S · 2019
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