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This paper introduces a new Convolutional Neural Network (ConvNet) architecture inspired by a class of partial differential equations (PDEs) called quasi-linear hyperbolic systems.
Strichartz, R.: A Guide to Distribution Theory and Fourier Transforms. Studies in Advanced Mathematics, CRC-Press (1994)
1994
Earlier work this paper cites.
Alinhac, S.: Hyperbolic partial differential equations. Springer Science & Business Media (2009)
2009
Earlier work this paper cites.
Cohen, T., Welling, M.: Group equivariant convolutional networks. In: International Conference on Machine Learning. pp. 2990–2999. PMLR (2016)
2016
Earlier work this paper cites.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition. pp. 770–778 (2016)
2016
Earlier work this paper cites.
Chollet, F.: Xception: Deep learning with depthwise separable convolutions. In: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition. pp. 1251–1258 (2017)
2017
Earlier work this paper cites.
Dauphin, Y.N., Fan, A., Auli, M., Grangier, D.: Language modeling with gated convolutional networks. In: International conference on machine learning. pp. 933–941. PMLR (2017)
2017
Earlier work this paper cites.
E, W.: A proposal on machine learning via dynamical systems. Communications in Mathematics and Statistics 1
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition. pp. 4700–4708 (2017)
2017
Earlier work this paper cites.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in Neural Information Processing Systems 30
2017
Earlier work this paper cites.
Chen, R.T., Rubanova, Y., Bettencourt, J., Duvenaud, D.K.: Neural ordinary differential equations. Advances in Neural Information Processing Systems 31
2018
Earlier work this paper cites.
Long, Z., Lu, Y., Ma, X., Dong, B.: PDE-Net: Learning PDEs from data. In: International Conference on Machine Learning. pp. 3208–3216. PMLR (2018)
2018
Cited alongside, same era.
Ruthotto, L., Haber, E.: Deep neural networks motivated by partial differential equations. Journal of Mathematical Imaging and Vision 62
2018
Cited alongside, same era.
Zhang, H., Weng, T.W., Chen, P.Y., Hsieh, C.J., Daniel, L.: Efficient neural network robustness certification with general activation functions. Advances in Neural Information Processing Systems 31
2018
Cited alongside, same era.
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: PyTorch: An imperative style, high-performance deep learning library. Advances in Neural Information Processing Systems 32
2019
Cited alongside, same era.
2021
Later among the works it cites.
Chrysos, G.G., Moschoglou, S., Bouritsas, G., Deng, J., Panagakis, Y., Zafeiriou, S.: Deep polynomial neural networks. IEEE transactions on pattern analysis and machine intelligence 44
2021
Later among the works it cites.
Ding, X., Zhang, X., Han, J., Ding, G.: Diverse branch block: Building a convolution as an Inception-like unit. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 10886–10895 (2021)
2021
Later among the works it cites.
E, W., Han, J., Jentzen, A.: Algorithms for solving high dimensional PDEs: from nonlinear Monte Carlo to machine learning. Nonlinearity 35
2021
Later among the works it cites.
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Raissi, M., Perdikaris, P., Karniadakis, G.E.: Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational Physics 378
2019
Cited alongside, same era.
Wightman, R.: Pytorch image models. https://github.com/rwightman/pytorch-image-models (2019). https://doi.org/10.5281/zenodo.4414861
2019
Cited alongside, same era.
Chrysos, G.G., Moschoglou, S., Bouritsas, G., Panagakis, Y., Deng, J., Zafeiriou, S.: P-nets: Deep polynomial neural networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 7325–7335 (2020)
2020
Cited alongside, same era.
Li, Q.: Dynamical systems and machine learning. Summer School, Peking University: Beijing, China (2020)
2020
Cited alongside, same era.
Olah, C., Cammarata, N., Schubert, L., Goh, G., Petrov, M., Carter, S.: Zoom in: An introduction to circuits. Distill 5
2020
Cited alongside, same era.
Shazeer, N.: GLU variants improve transformer. arXiv preprint arXiv:2002.05202 (2020)
2020
Cited alongside, same era.
Bai, B., Liang, J., Zhang, G., Li, H., Bai, K., Wang, F.: Why attentions may not be interpretable? In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. pp. 25–34 (2021)
2021
Cited alongside, same era.
Li, Z., Kovachki, N., Azizzadenesheli, K., Liu, B., Bhattacharya, K., Stuart, A., Anandkumar, A.: Fourier neural operator for parametric partial differential equations. In: International Conference on Learning Representations (2021)
2021
Later among the works it cites.
Wightman, R., Touvron, H., Jégou, H.: ResNet strikes back: An improved training procedure in timm. In: NeurIPS 2021 Workshop on ImageNet: Past, Present, and Future (2021)
2021
Later among the works it cites.
E, W., Han, J., Li, Q.: Dynamical systems and optimal control approach to deep learning. Mathematical Aspects of Deep Learning pp. 422–438 (2022)
2022
Later among the works it cites.
He, L., Chen, Y., Shen, Z., Yang, Y., Lin, Z.: Neural ePDOs: Spatially adaptive equivariant partial differential operator based networks. In: The Eleventh International Conference on Learning Representations (2022)
2022
Later among the works it cites.
Sander, M., Ablin, P., Peyré, G.: Do residual neural networks discretize neural ordinary differential equations? Advances in Neural Information Processing Systems 35
2022
Later among the works it cites.
Zhao, B., Ganev, I., Walters, R., Yu, R., Dehmamy, N.: Symmetries, flat minima, and the conserved quantities of gradient flow. In: International Conference on Learning Representations (2023), to appear
2023
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