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(Conditional) Generative Adversarial Networks (GANs) have found great success in recent years, due to their ability to approximate (conditional) distributions over extremely high dimensional spaces.
“Long Short-Term Memory”
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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“Problems in stochastic analysis: connections between rough paths and noncommutative harmonic analysis”
Thomas Fawcett · 2003
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“Differential equations driven by rough paths”
Terry Lyons, Michael Caruana and Thierry Lévy · 2004
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“A Generalised Signature Method for Time Series”
James Morrill, Adeline Fermanian, Patrick Kidger and Terry. Lyons · 2006
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“Sig-Wasserstein GANs for Time Series Generation”
Hao Ni et al · 2006
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“Optimal Transport: Old and New”
Cédric Villani · 2009
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“Uniqueness for the signature of a path of bounded variation and the reduced path group”
Ben Hambly and Terry Lyons · 2010
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“Generative Adversarial Nets”
Ian Goodfellow et al · 2014
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“Conditional Generative Adversarial Nets”
Mehdi Mirza and Simon Osindero · 2014
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“A Primer on the Signature Method in Machine Learning”
Ilya Chevyrev and Andrey Kormilitzin · 2016
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“Wasserstein Generative Adversarial Networks”
Martin Arjovsky, Soumith Chintala and Léon Bottou · 2017
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Imanol Arribas · 2018
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“PyTorch: An Imperative Style, High-Performance Deep Learning Library”
“torchsde”
Xuechen Li · 2020
Later among the works it cites.
“Scalable Gradients for Stochastic Differential Equations”
Xuechen Li, Ting-Kam Wong, Ricky.. Chen and David Duvenaud · 2020
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“Conditional Sig-Wasserstein GANs for Time Series Generation”
Hao Ni et al · 2020
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“Efficient and Accurate Gradients for Neural SDEs”
Patrick Kidger, James Foster, Xuechen Li and Terry Lyons · 2021
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“Neural SDEs as Infinite-Dimensional GANs”
Patrick Kidger et al · 2021
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“Signatory: differentiable computations of the signature and logsignature transforms, on both CPU and GPU” https://github.com/patrick-kidger/signatory
Patrick Kidger and Terry Lyons · 2021
Later among the works it cites.
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Adam Paszke et al · 2019
Cited alongside, same era.