Fetching the paper…
Reading the bibliography…
Recurrent neural networks (RNNs) with continuous-time hidden states are a natural fit for modeling irregularly-sampled time series.
Perceptrons
Minsky Marvin and A Papert Seymour · 1969
Earlier work this paper cites.
A family of embedded runge-kutta formulae
John R Dormand and Peter J Prince · 1980
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.
Generalization of backpropagation with application to a recurrent gas market model
Paul J Werbos · 1988
Earlier work this paper cites.
Backpropagation through time: what it does and how to do it
Paul J Werbos · 1990
Earlier work this paper cites.
Untersuchungen zu dynamischen neuronalen netzen [in german] diploma thesis
Sepp Hochreiter · 1991
Earlier work this paper cites.
Approximation of dynamical systems by continuous time recurrent neural networks
Ken-ichi Funahashi and Yuichi Nakamura · 1993
Earlier work this paper cites.
Learning long-term dependencies with gradient descent is difficult
Yoshua Bengio, Patrice Simard, and Paolo Frasconi · 1994
Earlier work this paper cites.
Wavelets for period analysis of unevenly sampled time series
Grant Foster · 1996
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
Earlier work this paper cites.
Bidirectional recurrent neural networks
Mike Schuster and Kuldip K Paliwal · 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.
Learning to forget: Continual prediction with lstm
Felix A Gers, Jürgen Schmidhuber, and Fred Cummins · 1999
Earlier work this paper cites.
Imbalanced clustering for microarray time-series
Ronald Pearson, Gregory Goney, and James Shwaber · 2003
Earlier work this paper cites.
Transcripts: An algebraic approach to coupled time series
José M Amigó, Roberto Monetti, Thomas Aschenbrenner, and Wolfram Bunk · 2012
Earlier work this paper cites.
Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude
Tijmen Tieleman and Geoffrey Hinton · 2012
Earlier work this paper cites.
Mujoco: A physics engine for model-based control
Emanuel Todorov, Tom Erez, and Yuval Tassa · 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.
Empirical evaluation of gated recurrent neural networks on sequence modeling
Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio · 2014
Earlier work this paper cites.
TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dan Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng · 2015
Earlier work this paper cites.
Scalable linear causal inference for irregularly sampled time series with long range dependencies
Francois W Belletti, Evan R Sparks, Michael J Franklin, Alexandre M Bayen, and Joseph E Gonzalez · 2016
Cited alongside, same era.
Openai gym, 2016
Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
Cited alongside, same era.
Lstm: A search space odyssey
Klaus Greff, Rupesh K Srivastava, Jan Koutník, Bas R Steunebrink, and Jürgen Schmidhuber · 2016
Cited alongside, same era.
A scalable end-to-end gaussian process adapter for irregularly sampled time series classification
Steven Cheng-Xian Li and Benjamin M Marlin · 2016
Cited alongside, same era.
Phased lstm: Accelerating recurrent network training for long or event-based sequences
Daniel Neil, Michael Pfeiffer, and Shih-Chii Liu · 2016
Cited alongside, same era.
Anode: Unconditionally accurate memory-efficient gradients for neural odes
Amir Gholami, Kurt Keutzer, and George Biros · 2019
Later among the works it cites.
A statistical investigation of long memory in language and music
Alexander Greaves-Tunnell and Zaid Harchaoui · 2019
Later among the works it cites.
Neural jump stochastic differential equations
Junteng Jia and Austin R Benson · 2019
Later among the works it cites.
Functional autoregression for sparsely sampled data
Daniel R Kowal, David S Matteson, and David Ruppert · 2019
Later among the works it cites.
Designing worm-inspired neural networks for interpretable robotic control
Mathias Lechner, Ramin Hasani, Manuel Zimmer, Thomas A Henzinger, and Radu Grosu · 2019
Later among the works it cites.
Pytorch: An imperative style, high-performance deep learning library
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Dheeru Dua and Casey Graff · 2017
Cited alongside, same era.
The neural hawkes process: A neurally self-modulating multivariate point process
Hongyuan Mei and Jason M Eisner · 2017
Cited alongside, same era.
Discrete event, continuous time rnns
Michael C Mozer, Denis Kazakov, and Robert V Lindsey · 2017
Cited alongside, same era.
Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
Cited alongside, same era.
Brits: Bidirectional recurrent imputation for time series
Wei Cao, Dong Wang, Jian Li, Hao Zhou, Lei Li, and Yitan Li · 2018
Cited alongside, same era.
Recurrent neural networks for multivariate time series with missing values
Zhengping Che, Sanjay Purushotham, Kyunghyun Cho, David Sontag, and Yan Liu · 2018
Cited alongside, same era.
Neural ordinary differential equations
Tian Qi Chen, Yulia Rubanova, Jesse Bettencourt, and David K Duvenaud · 2018
Cited alongside, same era.
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
Later among the works it cites.
Latent ordinary differential equations for irregularly-sampled time series
Yulia Rubanova, Tian Qi Chen, and David K Duvenaud · 2019
Later among the works it cites.
State-regularized recurrent neural networks
Cheng Wang and Mathias Niepert · 2019
Later among the works it cites.
On robustness of neural ordinary differential equations
YAN Hanshu, DU Jiawei, TAN Vincent, and FENG Jiashi · 2020
Closest in time.
Ramin Hasani, Mathias Lechner, Alexander Amini, Daniela Rus, and Radu Grosu · 2020
Closest in time.
The natural lottery ticket winner: Reinforcement learning with ordinary neural circuits
Ramin Hasani, Mathias Lechner, Alexander Amini, Daniela Rus, and Radu Grosu · 2020
Closest in time.
Learning to control pdes with differentiable physics
Philipp Holl, Vladlen Koltun, and Nils Thuerey · 2020
Closest in time.
Neural controlled differential equations for irregular time series
Patrick Kidger, James Morrill, James Foster, and Terry Lyons · 2020
Closest in time.
Neural circuit policies enabling auditable autonomy
Mathias Lechner, Ramin Hasani, Alexander Amini, Thomas A Henzinger, Daniela Rus, and Radu Grosu · 2020
Closest in time.
Gershgorin loss stabilizes the recurrent neural network compartment of an end-to-end robot learning scheme
Mathias Lechner, Ramin Hasani, Daniela Rus, and Radu Grosu · 2020
Closest in time.
Snode: Spectral discretization of neural odes for system identification
Alessio Quaglino, Marco Gallieri, Jonathan Masci, and Jan Koutník · 2020
Closest in time.
Robust landsat-based crop time series modelling
DP Roy and L Yan · 2020
Closest in time.
Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network
Alex Sherstinsky · 2020
Closest in time.
Adaptive checkpoint adjoint method for gradient estimation in neural ode
Juntang Zhuang, Nicha Dvornek, Xiaoxiao Li, Sekhar Tatikonda, Xenophon Papademetris, and James Duncan · 2020
Closest in time.