Neural ODE

Neural ODE

¹ Neural ODE

Published: 2022-08-13

Category: { Machine Learning }

Tags:

#Deep Learning #Neural ODE

References: - He K, Zhang X, Ren S, Sun J. Deep Residual Learning for Image Recognition. arXiv [cs.CV]. 2015. Available: http://arxiv.org/abs/1512.03385 - Srivastava RK, Greff K, Schmidhuber J. Highway Networks. arXiv [cs.LG]. 2015. Available: http://arxiv.org/abs/1505.00387 - Zhang X, Li Z, Loy CC, Lin D. PolyNet: A Pursuit of Structural Diversity in Very Deep Networks. arXiv [cs.CV]. 2016. Available: http://arxiv.org/abs/1611.05725 - Larsson G, Maire M, Shakhnarovich G. FractalNet: Ultra-Deep Neural Networks without Residuals. arXiv [cs.CV]. 2016. Available: http://arxiv.org/abs/1605.07648 - Gomez AN, Ren M, Urtasun R, Grosse RB. The Reversible Residual Network: Backpropagation Without Storing Activations. arXiv [cs.CV]. 2017. Available: http://arxiv.org/abs/1707.04585 - Lu Y, Zhong A, Li Q, Dong B. Beyond Finite Layer Neural Networks: Bridging Deep Architectures and Numerical Differential Equations. arXiv [cs.CV]. 2017. Available: http://arxiv.org/abs/1710.10121 - msurtsukov. msurtsukov/neural-ode: Jupyter notebook with Pytorch implementation of Neural Ordinary Differential Equations. In: GitHub [Internet]. [cited 21 Aug 2022]. Available: https://github.com/msurtsukov/neural-ode - Chen RTQ, Rubanova Y, Bettencourt J, Duvenaud D. Neural Ordinary Differential Equations. arXiv [cs.LG]. 2018. Available: http://arxiv.org/abs/1806.07366

Summary: The concepts and ideas of neural ODE

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