Pages that link to "Item:Q5001568"

The following pages link to Deep Neural Network Approximation Theory (Q5001568):

Displaying 50 items.

• Gabor neural networks with proven approximation properties (Q2050710) (← links)
• Solving the Kolmogorov PDE by means of deep learning (Q2051092) (← links)
• On the rate of convergence of fully connected deep neural network regression estimates (Q2054491) (← links)
• Theory of deep convolutional neural networks. II: Spherical analysis (Q2057723) (← links)
• High-dimensional distribution generation through deep neural networks (Q2062235) (← links)
• Constructive deep ReLU neural network approximation (Q2067309) (← links)
• Solving PDEs by variational physics-informed neural networks: an a posteriori error analysis (Q2084593) (← links)
• Design of the monodomain model by artificial neural networks (Q2098864) (← links)
• A phase transition for finding needles in nonlinear haystacks with LASSO artificial neural networks (Q2103975) (← links)
• A measure theoretical approach to the mean-field maximum principle for training NeurODEs (Q2105521) (← links)
• Explicitly antisymmetrized neural network layers for variational Monte Carlo simulation (Q2112448) (← links)
• DNN expression rate analysis of high-dimensional PDEs: application to option pricing (Q2117328) (← links)
• A theoretical analysis of deep neural networks and parametric PDEs (Q2117329) (← links)
• Approximation spaces of deep neural networks (Q2117336) (← links)
• Robust and resource-efficient identification of two hidden layer neural networks (Q2117339) (← links)
• Metric entropy limits on recurrent neural network learning of linear dynamical systems (Q2134114) (← links)
• On sharpness of an error bound for deep ReLU network approximation (Q2143606) (← links)
• Scientific machine learning through physics-informed neural networks: where we are and what's next (Q2162315) (← links)
• Variational physics informed neural networks: the role of quadratures and test functions (Q2162334) (← links)
• Universal approximation with quadratic deep networks (Q2185719) (← links)
• Function approximation by deep networks (Q2191837) (← links)
• Computational methods for deep learning. Theoretic, practice and applications (Q2214275) (← links)
• Phase transitions in rate distortion theory and deep learning (Q2684466) (← links)
• Deep learning for inverse problems with unknown operator (Q2689599) (← links)
• Computation and learning in high dimensions. Abstracts from the workshop held August 1--7, 2021 (hybrid meeting) (Q2693017) (← links)
• Sobolev-type embeddings for neural network approximation spaces (Q2700875) (← links)
• (Q3193691) (← links)
• A dilution algorithm for neural networks (Q4020972) (← links)
• (Q4584111) (← links)
• Deep Neural Networks in a Mathematical Framework (Q4644225) (← links)
• (Q4938227) (← links)
• Optimal Approximation with Sparsely Connected Deep Neural Networks (Q5025773) (← links)
• Analysis of the Generalization Error: Empirical Risk Minimization over Deep Artificial Neural Networks Overcomes the Curse of Dimensionality in the Numerical Approximation of Black--Scholes Partial Differential Equations (Q5037569) (← links)
• The Principles of Deep Learning Theory (Q5070199) (← links)
• Approximations with deep neural networks in Sobolev time-space (Q5075578) (← links)
• Mean Field Analysis of Deep Neural Networks (Q5076694) (← links)
• Full error analysis for the training of deep neural networks (Q5083408) (← links)
• Deep learning-based approximation of Goldbach partition function (Q5101877) (← links)
• Neural network approximation (Q5887830) (← links)
• Deep ReLU neural network approximation in Bochner spaces and applications to parametric PDEs (Q6062166) (← links)
• DEEP EQUILIBRIUM NETS (Q6067145) (← links)
• Convergence of deep convolutional neural networks (Q6077046) (← links)
• Relaxation approach for learning neural network regularizers for a class of identification problems (Q6087358) (← links)
• Physics-informed neural networks for approximating dynamic (hyperbolic) PDEs of second order in time: error analysis and algorithms (Q6087958) (← links)
• Exponential Convergence of Deep Operator Networks for Elliptic Partial Differential Equations (Q6108133) (← links)
• Deep Learning in High Dimension: Neural Network Expression Rates for Analytic Functions in \(\pmb{L^2(\mathbb{R}^d,\gamma_d)}\) (Q6109160) (← links)
• Drift estimation for a multi-dimensional diffusion process using deep neural networks (Q6123258) (← links)
• Invariant spectral foliations with applications to model order reduction and synthesis (Q6132386) (← links)
• A multivariate Riesz basis of ReLU neural networks (Q6144893) (← links)
• Collocation approximation by deep neural ReLU networks for parametric and stochastic PDEs with lognormal inputs (Q6148127) (← links)