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The use of Convolutional Neural Networks (CNNs) is widespread in Deep Learning due to a range of desirable model properties which result in an efficient and effective machine learning framework.
Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P · 1998
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Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
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Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
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An analysis of single-layer networks in unsupervised feature learning
Coates, A., Ng, A., and Lee, H · 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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Learning separable filters
Rigamonti, R., Sironi, A., Lepetit, V., and Fua, P · 2013
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Convolutional neural networks for speech recognition
Abdel-Hamid, O., Mohamed, A.-r., Jiang, H., Deng, L., Penn, G., and Yu, D · 2014
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Rigid-motion scattering for texture classification
Sifre, L. and Mallat, S · 2014
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Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A · 2014
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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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A simple way to initialize recurrent networks of rectified linear units
Le, Q. V., Jaitly, N., and Hinton, G. E · 2015
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U-net: Convolutional networks for biomedical image segmentation
Ronneberger, O., Fischer, P., and Brox, T · 2015
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Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A · 2015
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Cited alongside, same era.
Sgdr: Stochastic gradient descent with warm restarts
Loshchilov, I. and Hutter, F · 2016
Cited alongside, same era.
Wavenet: A generative model for raw audio
Oord, A. v. d., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., Kalchbrenner, N., Senior, A., and Kavukcuoglu, K · 2016
Cited alongside, same era.
Wavenet: A generative model for raw audio
Van Den Oord, A., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., Kalchbrenner, N., Senior, A. W., and Kavukcuoglu, K · 2016
Cited alongside, same era.
Dilated recurrent neural networks
Chang, S., Zhang, Y., Han, W., Yu, M., Guo, X., Tan, W., Cui, X., Witbrock, M., Hasegawa-Johnson, M. A., and Huang, T. S · 2017
Cited alongside, same era.
Pointconv: Deep convolutional networks on 3d point clouds
Wu, W., Qi, Z., and Fuxin, L · 2019
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Hydra - a framework for elegantly configuring complex applications
Yadan, O · 2019
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Experiment tracking with weights and biases, 2020
Biewald, L · 2020
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Principled weight initialization for hypernetworks
Chang, O., Flokas, L., and Lipson, H · 2020
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Hippo: Recurrent memory with optimal polynomial projections
Gu, A., Dao, T., Ermon, S., Rudra, A., and Ré, C · 2020
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Neural controlled differential equations for irregular time series
Kidger, P., Morrill, J., Foster, J., and Lyons, T · 2020
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Xception: Deep learning with depthwise separable convolutions
Chollet, F · 2017
Cited alongside, same era.
Decoupled weight decay regularization
Loshchilov, I. and Hutter, F · 2017
Cited alongside, same era.
Pointnet: Deep learning on point sets for 3d classification and segmentation
Qi, C. R., Su, H., Mo, K., and Guibas, L. J · 2017
Cited alongside, same era.
Schnet: A continuous-filter convolutional neural network for modeling quantum interactions
Schütt, K., Kindermans, P.-J., Sauceda Felix, H. E., Chmiela, S., Tkatchenko, A., and Müller, K.-R · 2017
Cited alongside, same era.
An empirical evaluation of generic convolutional and recurrent networks for sequence modeling
Bai, S., Kolter, J. Z., and Koltun, V · 2018
Cited alongside, same era.
Speech commands: A dataset for limited-vocabulary speech recognition
Warden, P · 2018
Cited alongside, same era.
Pytorch lightning
Falcon et al., W · 2019
Cited alongside, same era.
Implicit neural representations with periodic activation functions
Sitzmann, V., Martel, J., Bergman, A., Lindell, D., and Wetzstein, G · 2020
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On layer normalization in the transformer architecture
Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., and Liu, T · 2020
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Multiplicative filter networks
Fathony, R., Sahu, A. K., Willmott, D., and Kolter, J. Z · 2021
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Exploiting redundancy: Separable group convolutional networks on lie groups
Knigge, D. M., Romero, D. W., and Bekkers, E. J · 2021
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Long range arena : A benchmark for efficient transformers
Tay, Y., Dehghani, M., Abnar, S., Shen, Y., Bahri, D., Pham, P., Rao, J., Yang, L., Ruder, S., and Metzler, D · 2021
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Efficiently modeling long sequences with structured state spaces
Gu, A., Goel, K., and Re, C · 2022
Closest in time.
Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., and Xie, S · 2022
Closest in time.