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Learning workable representations of dynamical systems is becoming an increasingly important problem in a number of application areas.
“Variational methods, multisymplectic geometry and continuum mechanics”
Jerrold Marsden, Sergey Pekarsky, Steve Shkoller and Matthew West · 2001
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“An overview of variational integrators”
Adrian. Lew, Jerrold. Marsden, Michael Ortiz and Matthew West · 2004
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“Variational integrators”, 2004
Matthew West · 2004
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“An overview of Lie group variational integrators and their applications to optimal control”
Melvin Leok · 2007
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“Mathematics of complexity and dynamical systems”
Robert. Meyers · 2009
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“Auto-encoding variational Bayes”
Diederik Kingma and Max Welling · 2014
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“Stochastic backpropagation and approximate inference in deep generative models”
Danilo Rezende, Shakir Mohamed and Daan Wierstra · 2014
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“Neural embeddings of graphs in hyperbolic space”
Benjamin Chamberlain, James Clough and Marc Deisenroth · 2017
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“A proposal on machine learning via dynamical systems”
Weinan E · 2017
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“Stable architectures for deep neural networks”
Eldad Haber and Lars Ruthotto · 2017
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“Hyperbolic neural networks”
Octavian Ganea, Gary B“’ecigneul and Thomas Hofmann · 2018
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“Deep neural networks motivated by partial differential equations”
Lars Ruthotto and Eldad Haber · 2018
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“Augmented neural ODEs”
Emilien Dupont, Arnaud Doucet and Yee Teh · 2019
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“Reparameterizing distributions on Lie groups”
Luca Falorsi, Pim de Haan, Tim Davidson and Patrick Forr“’e · 2019
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Liyu Gong and Qiang Cheng · 2019
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“Hamiltonian neural networks”
Sam Greydanus, Misko Dzamba and Jason Yosinski · 2019
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Luca Falorsi et al · 2018
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Michael Lutter, Christian Ritter and Jan Peters · 2019
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Maziar Raissi, Paris Perdikaris and George Karniadakis · 2019
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