zymrael / awesome-neural-ode Goto Github PK

Solving PDEs:
Fourier Neural Operator for Parametric Partial Differential Equations
Neural Operator: Learning Maps Between Function Spaces

Solving ODEs:
Neural Flows: Efficient Alternative to Neural ODEs

Thanks for your list of neuralode papers, I'm also working on using deep learning to approximate dynamic systems. I'm writing this email to introduce my recent work

Our work starts from bidging deep architects are numerical schemes:
Yiping Lu, Aoxiao Zhong, Quanzheng Li, Bin Dong. "Beyond Finite Layer Neural Network:Bridging Deep Architects and Numerical
Differential Equations" Thirty-fifth International Conference on Machine Learning (ICML), 2018
（On arxiv 2017.11，iclr workshop track paper）

using deep networks to find out the PDE behind data
Zichao long*, Yiping Lu*, Xianzhong Ma*, Bin Dong. "PDE-Net:Learning PDEs From Data",Thirty-fifth International Conference on Machine Learning (ICML), 2018(*equal contribution)

We also show that Using ODE cam help design optimization method for neural networks【especially adversarial training！we are 5 times faster！
Dinghuai Zhang*, Tianyuan Zhang*,Yiping Lu*, Zhanxing Zhu, Bin Dong. "You Only Propagate Once: Painless Adversarial Training Using Maximal Principle." (*equal contribution) Submitted. arXiv preprint:1905.00877 （Neurips2019）

We also aim to use ODE to understand NLP/sequence modeling
Yiping Lu*, Zhuohan Li*, Di He, Zhiqing Sun, Bin Dong, Tao Qin, Liwei Wang, Tie-yan Liu "Understanding and Improving Transformer From a Multi-Particle Dynamic System Point of View." (*equal contribution) Submitted. arXiv preprint:1906.02762

to learn early stopping for image restoration using optimal control and reinforcement learning
Xiaoshuai Zhang*, Yiping Lu*, Jiaying Liu, Bin Dong. "Dynamically Unfolding Recurrent Restorer: A Moving Endpoint Control Method for Image Restoration" Seventh International Conference on Learning Representations(ICLR) 2019(*equal contribution)

welcome to my homepage to explore more
https://web.stanford.edu/~yplu/
we will have more related works recently

Also I want to figure out some missing reference
for theory paper
Thorpe M, van Gennip Y. Deep limits of residual neural networks[J]. arXiv preprint arXiv:1810.11741, 2018.
A mean-field optimal control formulation of deep learning arxiv 2018

For optimization using ODE papers you can see
Maximum Principle Based Algorithms for Deep Learning JMLR2018， arxiv 2017
An Optimal Control Approach to Deep Learning and Applications to Discrete-Weight Neural Networks ICML2018

And the first paper introducing the ode idea is
Weinan E. A proposal on machine learning via dynamical systems[J]. Communications in Mathematics and Statistics, 2017, 5(1): 1-11.

Hello,

I wrote a paper (UAI 2021) on Mixed Effect Neural ODE to analyze the dynamics of panel data. Here is repo, in case it is useful: https://github.com/JurijsNazarovs/panel_me_ode

Best,
Jurijs

Hello - thanks for this resource! I find it really helpful.

I've been meaning to ask if it would be acceptable to add a reference to our recent paper https://arxiv.org/abs/2005.08926. I'm happy to open a pull request adding this if you like.