Fetching the paper…
Reading the bibliography…
Circuits of biological neurons, such as in the functional parts of the brain can be modeled as networks of coupled oscillators.
Biological rhythms and the behavior of populations of coupled oscillators
A. T. Winfree · 1967
Earlier work this paper cites.
Local and global self-entrainment in oscillator lattices
S. Shinomoto H. Sakaguchi and Y. Kuramoto · 1987
Earlier work this paper cites.
Nonlinear oscillations, dynamical systems, and bifurcations of vector fields
J. Guckenheimer and P. Holmes · 1990
Earlier work this paper cites.
Predictability: A problem partly solved
Edward N Lorenz · 1996
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
Earlier work this paper cites.
Fast sigmoidal networks via spiking neurons
W. Maass · 2001
Earlier work this paper cites.
Exploring complex networks
S. H. Strogatz · 2001
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
Earlier work this paper cites.
Learning word vectors for sentiment analysis
Andrew L Maas, Raymond E Daly, Peter T Pham, Dan Huang, Andrew Y Ng, and Christopher Potts · 2011
Earlier work this paper cites.
Human activity recognition on smartphones using a multiclass hardware-friendly support vector machine
Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra, and Jorge L Reyes-Ortiz · 2012
Earlier work this paper cites.
On the difficulty of training recurrent neural networks
Razvan Pascanu, Tomas Mikolov, and Yoshua Bengio · 2013
Earlier work this paper cites.
Learning phrase representations using rnn encoder-decoder for statistical machine translation
Kyunghyun Cho, B van Merrienboer, Caglar Gulcehre, F Bougares, H Schwenk, and Yoshua Bengio · 2014
Earlier work this paper cites.
Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D Manning · 2014
Earlier work this paper cites.
Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
Cited alongside, same era.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
Cited alongside, same era.
A simple way to initialize recurrent networks of rectified linear units
Quoc V Le, Navdeep Jaitly, and Geoffrey E. Hinton · 2015
Cited alongside, same era.
Deep learning
Y. LeCun, Y. Bengio, and G. Hinton · 2015
Cited alongside, same era.
Nonlinear Dynamics and Chaos
S. Strogatz · 2015
Cited alongside, same era.
Unitary evolution recurrent neural networks
Martin Arjovsky, Amar Shah, and Yoshua Bengio · 2016
Neural ordinary differential equations
Ricky TQ Chen, Yulia Rubanova, Jesse Bettencourt, and David K Duvenaud · 2018
Later among the works it cites.
Echo state networks are universal
Lyudmila Grigoryeva and Juan-Pablo Ortega · 2018
Later among the works it cites.
Orthogonal recurrent neural networks with scaled cayley transform
Kyle Helfrich, Devin Willmott, and Qiang Ye · 2018
Later among the works it cites.
Fastgrnn: A fast, accurate, stable and tiny kilobyte sized gated recurrent neural network
Aditya Kusupati, Manish Singh, Kush Bhatia, Ashish Kumar, Prateek Jain, and Manik Varma · 2018
Later among the works it cites.
Independently recurrent neural network (indrnn): Building a longer and deeper rnn
Shuai Li, Wanqing Li, Chris Cook, Ce Zhu, and Yanbo Gao · 2018
Later among the works it cites.
Trivializations for gradient-based optimization on manifolds
Mario Lezcano Casado · 2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Recurrent orthogonal networks and long-memory tasks
Mikael Henaff, Arthur Szlam, and Yann LeCun · 2016
Cited alongside, same era.
Neurons as oscillators
K. M. Stiefel and G. B. Ermentrout · 2016
Cited alongside, same era.
Full-capacity unitary recurrent neural networks
Scott Wisdom, Thomas Powers, John Hershey, Jonathan Le Roux, and Les Atlas · 2016
Cited alongside, same era.
Dilated recurrent neural networks
Shiyu Chang, Yang Zhang, Wei Han, Mo Yu, Xiaoxiao Guo, Wei Tan, Xiaodong Cui, Michael Witbrock, Mark A Hasegawa-Johnson, and Thomas S Huang · 2017
Cited alongside, same era.
Gate-variants of gated recurrent unit (gru) neural networks
Rahul Dey and Fathi M Salemt · 2017
Cited alongside, same era.
A proposal on machine learning via dynamical systems
Weinan E · 2017
Cited alongside, same era.
Later among the works it cites.
Antisymmetricrnn: A dynamical system view on recurrent neural networks
Bo Chang, Minmin Chen, Eldad Haber, and Ed H. Chi · 2019
Later among the works it cites.
Hamiltonian neural networks
Samuel Greydanus, Misko Dzamba, and Jason Yosinski · 2019
Later among the works it cites.
Non-normal recurrent neural network (nnrnn): learning long time dependencies while improving expressivity with transient dynamics
Giancarlo Kerg, Kyle Goyette, Maximilian Puelma Touzel, Gauthier Gidel, Eugene Vorontsov, Yoshua Bengio, and Guillaume Lajoie · 2019
Later among the works it cites.
Cheap orthogonal constraints in neural networks: A simple parametrization of the orthogonal and unitary group
Mario Lezcano-Casado and David Martínez-Rubio · 2019
Later among the works it cites.
Latent ordinary differential equations for irregularly-sampled time series
Yulia Rubanova, Ricky T. Q. Chen, and David K Duvenaud · 2019
Later among the works it cites.
Symplectic recurrent neural networks
Zhengdao Chen, Jianyu Zhang, Martín Arjovsky, and Léon Bottou · 2020
Closest in time.
Lipschitz recurrent neural networks
N Benjamin Erichson, Omri Azencot, Alejandro Queiruga, and Michael W Mahoney · 2020
Closest in time.
Rnns incrementally evolving on an equilibrium manifold: A panacea for vanishing and exploding gradients?
Anil Kag, Ziming Zhang, and Venkatesh Saligrama · 2020
Closest in time.