Pages that link to "Item:Q1314507"

The following pages link to Approximation and estimation bounds for artificial neural networks (Q1314507):

Displaying 50 items.

• Output-based error estimation and mesh adaptation for unsteady turbulent flow simulations (Q2674074) (← links)
• Estimation of a regression function on a manifold by fully connected deep neural networks (Q2676904) (← links)
• Growing axons: greedy learning of neural networks with application to function approximation (Q2689211) (← links)
• Architecture-independent approximation of functions (Q2747214) (← links)
• Soft computing on small data sets (Q2758806) (← links)
• A note on the size of denoising neural networks (Q2797780) (← links)
• Risk bounds for mixture density estimation (Q3373741) (← links)
• Empirical prediction limit estimation methods for feed-forward neural networks (Q3426358) (← links)
• Minimization of Error Functionals over Perceptron Networks (Q3539962) (← links)
• (Q4220209) (← links)
• On function recovery by neural networks based on orthogonal expansions (Q4377199) (← links)
• Non-parametric regression for spatially dependent data with wavelets (Q4559353) (← links)
• Bayesian Neural Networks for Selection of Drug Sensitive Genes (Q4559675) (← links)
• (Q5011559) (← links)
• Deep learning volatility: a deep neural network perspective on pricing and calibration in (rough) volatility models (Q5014167) (← links)
• Optimal Approximation with Sparsely Connected Deep Neural Networks (Q5025773) (← links)
• (Q5043153) (← links)
• Discretization of parameter identification in PDEs using neural networks (Q5058109) (← links)
• Mean Field Analysis of Deep Neural Networks (Q5076694) (← links)
• Estimation of Dynamic Discrete Choice Models Using Artificial Neural Network Approximations (Q5080138) (← links)
• Full error analysis for the training of deep neural networks (Q5083408) (← links)
• Error bounds for approximations with deep ReLU neural networks in Ws,p norms (Q5132228) (← links)
• (Q5148959) (← links)
• Mean Field Analysis of Neural Networks: A Law of Large Numbers (Q5219306) (← links)
• Solving inverse problems using data-driven models (Q5230520) (← links)
• Measure Theoretic Results for Approximation by Neural Networks with Limited Weights (Q5365276) (← links)
• Structural, Syntactic, and Statistical Pattern Recognition (Q5466387) (← links)
• Artificial neural networks with a signed-rank objective function and applications (Q5866164) (← links)
• Molecular modeling by machine learning (Q5868457) (← links)
• Neural network approximation (Q5887830) (← links)
• A Proof that Artificial Neural Networks Overcome the Curse of Dimensionality in the Numerical Approximation of Black–Scholes Partial Differential Equations (Q5889064) (← links)
• An approximation result for nets in functional estimation (Q5951990) (← links)
• A deep network construction that adapts to intrinsic dimensionality beyond the domain (Q6054952) (← links)
• On the approximation of functions by tanh neural networks (Q6055124) (← links)
• Online learning of smooth functions (Q6057845) (← links)
• Randomized neural network with Petrov-Galerkin methods for solving linear and nonlinear partial differential equations (Q6058946) (← links)
• Deep learning methods for partial differential equations and related parameter identification problems (Q6070739) (← links)
• Towards understanding theoretical advantages of complex-reaction networks (Q6077000) (← links)
• Approximation Analysis of Convolutional Neural Networks (Q6090346) (← links)
• Towards Lower Bounds on the Depth of ReLU Neural Networks (Q6100606) (← links)
• Overall error analysis for the training of deep neural networks via stochastic gradient descent with random initialisation (Q6107984) (← links)
• A mathematical perspective of machine learning (Q6118171) (← links)
• Convergence rates for shallow neural networks learned by gradient descent (Q6137712) (← links)
• Lower bounds for artificial neural network approximations: a proof that shallow neural networks fail to overcome the curse of dimensionality (Q6155895) (← links)
• Neural network approximation and estimation of classifiers with classification boundary in a Barron class (Q6165247) (← links)
• Improved training of physics-informed neural networks for parabolic differential equations with sharply perturbed initial conditions (Q6171154) (← links)
• Optimal convergence rates of deep neural networks in a classification setting (Q6184926) (← links)
• Accelerating hypersonic reentry simulations using deep learning-based hybridization (with guarantees) (Q6187668) (← links)
• Normalization effects on deep neural networks (Q6194477) (← links)
• Analysis of the rate of convergence of two regression estimates defined by neural features which are easy to implement (Q6200889) (← links)