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Neural differential equations are a promising new member in the neural network family.
A steepest ascent method for solving optimum programming problems
Arthur E. Bryson, Jr · 1962
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Learning state space trajectories in recurrent neural networks
Barak A. Pearlmutter · 1989
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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A public domain dataset for human activity recognition using smartphones
Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra, and Jorge Luis Reyes-Ortiz · 2013
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Learning phrase representations using rnn encoder-decoder for statistical machine translation
Kyunghyun Cho, Bart Van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio · 2014
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.
Neural ordinary differential equations
Tian Qi Chen, Yulia Rubanova, Jesse Bettencourt, and David K. Duvenaud · 2018
Cited alongside, same era.
Reversible architectures for arbitrarily deep residual neural networks
Bo Chang, Lili Meng, Eldad Haber, Lars Ruthotto, David Begert, and Elliot Holtham · 2018
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Differentiable ODE solvers with full GPU support and O ( 1 ) O(1) -memory backpropagation, December 2018
Ricky Chen · 2018
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Latent ODEs for irregularly-sampled time series
Yulia Rubanova, Ricky TQ Chen, and David K. Duvenaud · 2019
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