Fetching the paper…
Reading the bibliography…
Neural Ordinary Differential Equations (NODEs), a framework of continuous-depth neural networks, have been widely applied, showing exceptional efficacy in coping with some representative datasets.
You only propagate once: Painless adversarial training using maximal principle
Dinghuai Zhang, Tianyuan Zhang, Yiping Lu, Zhanxing Zhu, and Bin Dong · 1905
Earlier work this paper cites.
The mathematical theory of optimal processes
LS Pontryagin, VG Boltyanskij, RV Gamkrelidze, and EF Mishchenko · 1962
Earlier work this paper cites.
Learning representations by back-propagating errors
David E Rumelhart, Geoffrey E Hinton, and Ronald J Williams · 1986
Earlier work this paper cites.
Finding structure in time
Jeffrey L Elman · 1990
Earlier work this paper cites.
Numerical modelling in biosciences using delay differential equations
Gennadii A Bocharov and Fathalla A Rihan · 2000
Earlier work this paper cites.
Solving ddes in matlab
Lawrence F Shampine and Skip Thompson · 2001
Earlier work this paper cites.
Applied delay differential equations , volume 3
Thomas Erneux · 2009
Earlier work this paper cites.
Calculus of variations and optimal control theory: a concise introduction
Daniel Liberzon · 2011
Earlier work this paper cites.
Construction of lyapunov functionals for delay differential equations in virology and epidemiology
Tsuyoshi Kajiwara, Toru Sasaki, and Yasuhiro Takeuchi · 2012
Earlier work this paper cites.
David Ha, Andrew Dai, and Quoc V Le · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Multi-level residual networks from dynamical systems view
Bo Chang, Lili Meng, Eldad Haber, Frederick Tung, and David Begert · 2017
Earlier work this paper cites.
A proposal on machine learning via dynamical systems
Weinan E · 2017
Earlier work this paper cites.
Stable architectures for deep neural networks
Eldad Haber and Lars Ruthotto · 2017
Cited alongside, same era.
Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
Cited alongside, same era.
Maximum principle based algorithms for deep learning
Qianxiao Li, Long Chen, Cheng Tai, and E Weinan · 2017
Cited alongside, same era.
Neural ordinary differential equations
Ricky TQ Chen, Yulia Rubanova, Jesse Bettencourt, and David K Duvenaud · 2018
Cited alongside, same era.
Brain-inspired constructive learning algorithms with evolutionally additive nonlinear neurons
Leheng Fang, Wei Lin, and Qiang Luo · 2018
Cited alongside, same era.
Solving high-dimensional partial differential equations using deep learning
Jiequn Han, Arnulf Jentzen, and E Weinan · 2018
Cited alongside, same era.
A mean-field optimal control formulation of deep learning
Weinan E, Jiequn Han, and Qianxiao Li · 2019
Later among the works it cites.
Ode-inspired network design for single image super-resolution
Xiangyu He, Zitao Mo, Peisong Wang, Yang Liu, Mingyuan Yang, and Jian Cheng · 2019
Later among the works it cites.
Albert: A lite bert for self-supervised learning of language representations
Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut · 2019
Later among the works it cites.
Neural sde: Stabilizing neural ode networks with stochastic noise
Xuanqing Liu, Tesi Xiao, Si Si, Qin Cao, Sanjiv Kumar, and Cho-Jui Hsieh · 2019
Later among the works it cites.
Pde-net 2.0: Learning pdes from data with a numeric-symbolic hybrid deep network
Zichao Long, Yiping Lu, and Bin Dong · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
An optimal control approach to deep learning and applications to discrete-weight neural networks
Qianxiao Li and Shuji Hao · 2018
Cited alongside, same era.
Pde-net: Learning pdes from data
Zichao Long, Yiping Lu, Xianzhong Ma, and Bin Dong · 2018
Cited alongside, same era.
Beyond finite layer neural networks: Bridging deep architectures and numerical differential equations
Yiping Lu, Aoxiao Zhong, Quanzheng Li, and Bin Dong · 2018
Cited alongside, same era.
Model-free prediction of large spatiotemporally chaotic systems from data: A reservoir computing approach
Jaideep Pathak, Brian Hunt, Michelle Girvan, Zhixin Lu, and Edward Ott · 2018
Cited alongside, same era.
Stochastic training of residual networks: a differential equation viewpoint
Qi Sun, Yunzhe Tao, and Qiang Du · 2018
Cited alongside, same era.
Antisymmetricrnn: A dynamical system view on recurrent neural networks
Bo Chang, Minmin Chen, Eldad Haber, and Ed H Chi · 2019
Cited alongside, same era.
Snode: Spectral discretization of neural odes for system identification
Alessio Quaglino, Marco Gallieri, Jonathan Masci, and Jan Koutník · 2019
Later among the works it cites.
Deep neural networks motivated by partial differential equations
Lars Ruthotto and Eldad Haber · 2019
Later among the works it cites.
Pointflow: 3d point cloud generation with continuous normalizing flows
Guandao Yang, Xun Huang, Zekun Hao, Ming-Yu Liu, Serge Belongie, and Bharath Hariharan · 2019
Later among the works it cites.
Detecting unstable periodic orbits based only on time series: When adaptive delayed feedback control meets reservoir computing
Qunxi Zhu, Huanfei Ma, and Wei Lin · 2019
Later among the works it cites.
Learning to encode position for transformer with continuous dynamical model
Xuanqing Liu, Hsiang-Fu Yu, Inderjit Dhillon, and Cho-Jui Hsieh · 2020
Later among the works it cites.
Neupde: Neural network based ordinary and partial differential equations for modeling time-dependent data
Yifan Sun, Linan Zhang, and Hayden Schaeffer · 2020
Later among the works it cites.
Introduction to focus issue: When machine learning meets complex systems: Networks, chaos, and nonlinear dynamics
Yang Tang, Jürgen Kurths, Wei Lin, Edward Ott, and Ljupco Kocarev · 2020
Later among the works it cites.