Forward-Backward Stochastic Differential Equations and their Applications
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Time discretization and Markovian iteration for coupled FBSDEs
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Least-squares Monte Carlo for backward SDEs
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Counterparty risk valuation: A marked branching diffusion approach
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
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Adam: a method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2015
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Multilevel Picard iterations for solving smooth semilinear parabolic heat equations
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Martin Hutzenthaler, Arnulf Jentzen, and Thomas Kruse · 2016
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The power of depth for feedforward neural networks
Ronen Eldan and Ohad Shamir · 2016
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On the expressive power of deep learning: A tensor analysis
Nadav Cohen, Or Sharir, and Amnon Shashua · 2016
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Deep vs. shallow networks: An approximation theory perspective
Hrushikesh N Mhaskar and Tomaso Poggio · 2016
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Efficient numerical Fourier methods for coupled forward–backward SDEs
T.P. Huijskens, M.J. Ruijter, and C.W. Oosterlee · 2016
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Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic differential equations
Weinan E, Jiequn Han, and Arnulf Jentzen · 2017
Cited alongside, same era.