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Neural Ordinary Differential Equation (Neural ODE) has been proposed as a continuous approximation to the ResNet architecture.
A monte carlo method for sensitivity analysis and parametric optimization of nonlinear stochastic systems
Jichuan Yang and Harold J Kushner · 1991
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Training with noise is equivalent to tikhonov regularization
Chris M Bishop · 1995
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The effects of adding noise during backpropagation training on a generalization performance
Guozhong An · 1996
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Stochastic differential equations
Bernt Øksendal · 2003
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Sensitivity analysis using itô–malliavin calculus and martingales, and application to stochastic optimal control
Emmanuel Gobet and Rémi Munos · 2005
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Stochastic differential equations and applications
Xuerong Mao · 2007
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Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Deep networks with stochastic depth
Gao Huang, Yu Sun, Zhuang Liu, Daniel Sedra, and Kilian Q Weinberger · 2016
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A proposal on machine learning via dynamical systems
Weinan E · 2017
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Xavier Gastaldi · 2017
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2017
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Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2017
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Neural ordinary differential equations
Tian Qi Chen, Yulia Rubanova, Jesse Bettencourt, and David K Duvenaud · 2018
Beyond finite layer neural networks: Bridging deep architectures and numerical differential equations
Yiping Lu, Aoxiao Zhong, Quanzheng Li, and Bin Dong · 2018
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Analyzing inverse problems with invertible neural networks
Lynton Ardizzone, Jakob Kruse, Sebastian Wirkert, Daniel Rahner, Eric W Pellegrini, Ralf S Klessen, Lena Maier-Hein, Carsten Rother, and Ullrich Köthe · 2018
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Ffjord: Free-form continuous dynamics for scalable reversible generative models
Will Grathwohl, Ricky TQ Chen, Jesse Betterncourt, Ilya Sutskever, and David Duvenaud · 2018
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Stochastic training of residual networks: a differential equation viewpoint
Qi Sun, Yunzhe Tao, and Qiang Du · 2018
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Cited alongside, same era.
Towards robust neural networks via random self-ensemble
Xuanqing Liu, Minhao Cheng, Huan Zhang, and Cho-Jui Hsieh · 2018
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Certified robustness to adversarial examples with differential privacy
Mathias Lecuyer, Vaggelis Atlidakis, Roxana Geambasu, Daniel Hsu, and Suman Jana · 2018
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Maziar Raissi · 2018
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Dropblock: A regularization method for convolutional networks
Golnaz Ghiasi, Tsung-Yi Lin, and Quoc V Le · 2018
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Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas Dietterich · 2019
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