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Multilayer feedforward networks are universal approximators
Kurt Hornik, Maxwell Stinchcombe, and Halbert White · 1989
Earlier work this paper cites.
A stochastic estimator of the trace of the influence matrix for laplacian smoothing splines
Michael F Hutchinson · 1989
Earlier work this paper cites.
The variational formulation of the fokker–planck equation
Richard Jordan, David Kinderlehrer, and Felix Otto · 1998
Earlier work this paper cites.
An introduction to variational methods for graphical models
Michael I Jordan, Zoubin Ghahramani, Tommi S Jaakkola, and Lawrence K Saul · 1999
Earlier work this paper cites.
Greedy function approximation: a gradient boosting machine
Jerome H Friedman · 2001
Earlier work this paper cites.
Gradient flows: in metric spaces and in the space of probability measures
Luigi Ambrosio, Nicola Gigli, and Giuseppe Savaré · 2005
Earlier work this paper cites.
Optimal transport: old and new , volume 338
Cédric Villani · 2009
Earlier work this paper cites.
Incremental gradient, subgradient, and proximal methods for convex optimization: A survey
Dimitri P Bertsekas et al · 2011
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Matthew D Hoffman, Andrew Gelman, et al · 2014
Earlier work this paper cites.
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Gabriel Peyré · 2015
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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UCI machine learning repository, 2017
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