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Deep residual networks (ResNets) have shown state-of-the-art performance in various real-world applications.
Journal of Differential equations 4
J.P. LaSalle, Stability theory for ordinary differential equations · 1968
Earlier work this paper cites.
Neural networks 2
K. Hornik, M. Stinchcombe, H. White, Multilayer feedforward networks are universal approximators · 1989
Earlier work this paper cites.
Journal of the American Statistical Association 84
H. White, Some asymptotic results for learning in single hidden-layer feedforward network models · 1989
Earlier work this paper cites.
C.M. Bishop, et al., Neural networks for pattern recognition (Oxford university press, 1995)
1995
Earlier work this paper cites.
R.H. Bartels, J.C. Beatty, B.A. Barsky, An introduction to splines for use in computer graphics and geometric modeling (Morgan Kaufmann, 1995)
1995
Earlier work this paper cites.
Proceedings of the IEEE 86
Y. LeCun, L. Bottou, Y. Bengio, P. Haffner, Gradient-based learning applied to document recognition · 1998
Earlier work this paper cites.
B.D. Ripley, Pattern recognition and neural networks (Cambridge university press, 2007)
2007
Earlier work this paper cites.
Journal of the American Statistical Association 103
J. Chen, H. Wu, Efficient local estimation for time-varying coefficients in deterministic dynamic models with applications to hiv-1 dynamics · 2008
Earlier work this paper cites.
A. Krizhevsky, G. Hinton, et al., Learning multiple layers of features from tiny images (2009)
2009
Earlier work this paper cites.
Annals of statistics 38
H. Xue, H. Miao, H. Wu, Sieve estimation of constant and time-varying coefficients in nonlinear ordinary differential equation models by considering both numerical error and measurement error · 2010
Earlier work this paper cites.
The annals of applied statistics 4
H. Liang, H. Miao, H. Wu, Estimation of constant and time-varying dynamic parameters of hiv infection in a nonlinear differential equation model · 2010
Earlier work this paper cites.
V.I. Arnold, Geometrical methods in the theory of ordinary differential equations , vol. 250 (Springer Science & Business Media, 2012)
2012
Earlier work this paper cites.
2013 IEEE international conference on acoustics, speech and signal processing pp. 6645–6649 (2013)
A. Graves, A.r. Mohamed, G. Hinton, Speech recognition with deep recurrent neural networks · 2013
Earlier work this paper cites.
arXiv preprint arXiv:1409.0473 (2014)
D. Bahdanau, K. Cho, Y. Bengio, Neural machine translation by jointly learning to align and translate · 2014
Earlier work this paper cites.
Advances in neural information processing systems 27
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial nets · 2014
Earlier work this paper cites.
Proc. interspeech (2014)
L. Deng, J. Platt, Ensemble deep learning for speech recognition · 2014
Cited alongside, same era.
Journal of the American Statistical Association 109
H. Miao, H. Wu, H. Xue, Generalized ordinary differential equation models · 2014
Cited alongside, same era.
Proceedings of the IEEE conference on computer vision and pattern recognition pp. 3431–3440 (2015)
J. Long, E. Shelhamer, T. Darrell, Fully convolutional networks for semantic segmentation · 2015
Cited alongside, same era.
Applied Intelligence 42
K. Noda, Y. Yamaguchi, K. Nakadai, H.G. Okuno, T. Ogata, Audio-visual speech recognition using deep learning · 2015
Cited alongside, same era.
Nonlinear Dynamics 81
S. Tang, B. Tang, A. Wang, Y. Xiao, Holling ii predator–prey impulsive semi-dynamic model with complex poincaré map · 2015
Cited alongside, same era.
International conference on machine learning pp. 448–456 (2015)
S. Ioffe, C. Szegedy, Batch normalization: Accelerating deep network training by reducing internal covariate shift · 2015
arXiv preprint arXiv:1710.09513 (2017)
Q. Li, L. Chen, C. Tai, et al., Maximum principle based algorithms for deep learning · 2017
Later among the works it cites.
PloS one 12
D. Yu, Q. Lin, A.P. Chiu, D. He, Effects of reactive social distancing on the 1918 influenza pandemic · 2017
Later among the works it cites.
A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, A. Lerer, Automatic differentiation in pytorch (2017)
2017
Later among the works it cites.
ieee Computational intelligenCe magazine 13
T. Young, D. Hazarika, S. Poria, E. Cambria, Recent trends in deep learning based natural language processing · 2018
Later among the works it cites.
Proceedings of the AAAI Conference on Artificial Intelligence 32
B. Chang, L. Meng, E. Haber, L. Ruthotto, D. Begert, E. Holtham, Reversible architectures for arbitrarily deep residual neural networks · 2018
Later among the works it cites.
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Cited alongside, same era.
D. Yu, L. Deng, Automatic speech recognition , vol. 1 (Springer, 2016)
2016
Cited alongside, same era.
Proceedings of the IEEE conference on computer vision and pattern recognition pp. 770–778 (2016)
K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition · 2016
Cited alongside, same era.
European conference on computer vision pp. 630–645 (2016)
K. He, X. Zhang, S. Ren, J. Sun, Identity mappings in deep residual networks · 2016
Cited alongside, same era.
G.F. Simmons, Differential equations with applications and historical notes (CRC Press, 2016)
2016
Cited alongside, same era.
Commun. Math. Biol. Neurosci. 2016
D. Yu, S. Tang, Y. Lou, Revisiting logistic population model for assessing periodically harvested closures · 2016
Cited alongside, same era.
proceedings of the IEEE International Conference on Computer Vision pp. 5533–5541 (2017)
Z. Qiu, T. Yao, T. Mei, Learning spatio-temporal representation with pseudo-3d residual networks · 2017
Cited alongside, same era.
International Conference on Machine Learning pp. 3276–3285 (2018)
Y. Lu, A. Zhong, Q. Li, B. Dong, Beyond finite layer neural networks: Bridging deep architectures and numerical differential equations · 2018
Later among the works it cites.
Advances in neural information processing systems 31
R.T. Chen, Y. Rubanova, J. Bettencourt, D.K. Duvenaud, Neural ordinary differential equations · 2018
Later among the works it cites.
Proceedings of the European conference on computer vision (ECCV) pp. 3–19 (2018)
Y. Wu, K. He, Group normalization · 2018
Later among the works it cites.
BMC medical research methodology 19
A. Perperoglou, W. Sauerbrei, M. Abrahamowicz, M. Schmid, A review of spline function procedures in r · 2019
Later among the works it cites.
arXiv preprint arXiv:1912.10382 (2019)
Q. Li, T. Lin, Z. Shen, Deep learning via dynamical systems: An approximation perspective · 2019
Later among the works it cites.
arXiv preprint arXiv:1906.00875 (2019)
X. Shen, C. Jiang, L. Sakhanenko, Q. Lu, Asymptotic properties of neural network sieve estimators · 2019
Later among the works it cites.
Physica D: Nonlinear Phenomena 404
A. Sherstinsky, Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network · 2020
Later among the works it cites.
Infectious Disease Modelling 6
D. Yu, G. Zhu, X. Wang, C. Zhang, B. Soltanalizadeh, X. Wang, S. Tang, H. Wu, Assessing effects of reopening policies on covid-19 pandemic in texas with a data-driven transmission model · 2021
Later among the works it cites.
Methods in Ecology and Evolution 12
W. Bonnaffé, B.C. Sheldon, T. Coulson, Neural ordinary differential equations for ecological and evolutionary time-series analysis · 2021
Later among the works it cites.