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Convergence rates for the stochastic gradient descent method for non-convex objective functions., , and . CoRR, (2019)Deep Optimal Stopping., , and . J. Mach. Learn. Res., (2019)A proof that artificial neural networks overcome the curse of dimensionality in the numerical approximation of Black-Scholes partial differential equations., , , and . CoRR, (2018)Strong convergence rates on the whole probability space for space-time discrete numerical approximation schemes for stochastic Burgers equations., , , and . CoRR, (2019)Galerkin Approximations for the Stochastic Burgers Equation., and . SIAM J. Numerical Analysis, 51 (1): 694-715 (2013)Deep splitting method for parabolic PDEs., , , , and . CoRR, (2019)Deep neural network approximations for Monte Carlo algorithms., , and . CoRR, (2019)On Multilevel Picard Numerical Approximations for High-Dimensional Nonlinear Parabolic Partial Differential Equations and High-Dimensional Nonlinear Backward Stochastic Differential Equations., , , and . J. Sci. Comput., 79 (3): 1534-1571 (2019)Machine Learning Approximation Algorithms for High-Dimensional Fully Nonlinear Partial Differential Equations and Second-order Backward Stochastic Differential Equations., , and . J. Nonlinear Science, 29 (4): 1563-1619 (2019)A Milstein Scheme for SPDEs., and . Foundations of Computational Mathematics, 15 (2): 313-362 (2015)