Pages that link to "Item:Q4277151"

The following pages link to Universal approximation bounds for superpositions of a sigmoidal function (Q4277151):

Displaying 50 items.

• Analysis of convergence performance of neural networks ranking algorithm (Q1942699) (← links)
• Dynamic programming and value-function approximation in sequential decision problems: error analysis and numerical results (Q1949593) (← links)
• Essential rate for approximation by spherical neural networks (Q1952559) (← links)
• Using radial basis function networks for function approximation and classification (Q1954367) (← links)
• Approximation with neural networks activated by ramp sigmoids (Q1958429) (← links)
• Towards long-term prediction (Q1961655) (← links)
• Approximation rates for neural networks with general activation functions (Q1982446) (← links)
• Exponential convergence of the deep neural network approximation for analytic functions (Q1989902) (← links)
• Analysis of a two-layer neural network via displacement convexity (Q1996787) (← links)
• Selection dynamics for deep neural networks (Q2003969) (← links)
• Metamodeling of aircraft infrared signature dispersion (Q2006889) (← links)
• A robust solution of a statistical inverse problem in multiscale computational mechanics using an artificial neural network (Q2020855) (← links)
• Efficient approximation of solutions of parametric linear transport equations by ReLU DNNs (Q2026114) (← links)
• Topological properties of the set of functions generated by neural networks of fixed size (Q2031060) (← links)
• A selective overview of deep learning (Q2038303) (← links)
• Multivariate extensions of isotonic regression and total variation denoising via entire monotonicity and Hardy-Krause variation (Q2039786) (← links)
• Linearized two-layers neural networks in high dimension (Q2039801) (← links)
• The universal approximation property. Characterization, construction, representation, and existence (Q2043428) (← links)
• Nonlinear autoregressive sieve bootstrap based on extreme learning machines (Q2045710) (← links)
• Numerical solution of the parametric diffusion equation by deep neural networks (Q2049099) (← links)
• Gabor neural networks with proven approximation properties (Q2050710) (← 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 dynamics of generalization error in neural networks (Q2057778) (← links)
• High-dimensional distribution generation through deep neural networks (Q2062235) (← links)
• Optimal approximation rate of ReLU networks in terms of width and depth (Q2065073) (← links)
• Rates of approximation by neural network interpolation operators (Q2073064) (← links)
• Mean-field Langevin dynamics and energy landscape of neural networks (Q2077356) (← links)
• Supervised learning from noisy observations: combining machine-learning techniques with data assimilation (Q2077682) (← links)
• The construction and approximation of ReLU neural network operators (Q2086452) (← links)
• On the approximation of rough functions with deep neural networks (Q2089012) (← links)
• Excitable media store and transfer complicated information via topological defect motion (Q2094460) (← links)
• A priori and a posteriori error estimates for the deep Ritz method applied to the Laplace and Stokes problem (Q2095152) (← links)
• Correlations of random classifiers on large data sets (Q2100403) (← links)
• A phase transition for finding needles in nonlinear haystacks with LASSO artificial neural networks (Q2103975) (← links)
• Understanding neural networks with reproducing kernel Banach spaces (Q2105111) (← links)
• Generalization bounds for sparse random feature expansions (Q2105118) (← links)
• Do ideas have shape? Idea registration as the continuous limit of artificial neural networks (Q2111734) (← links)
• Representation formulas and pointwise properties for Barron functions (Q2113295) (← 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)
• Depth separations in neural networks: what is actually being separated? (Q2117335) (← links)
• Approximation spaces of deep neural networks (Q2117336) (← links)
• The Barron space and the flow-induced function spaces for neural network models (Q2117337) (← links)
• High-order approximation rates for shallow neural networks with cosine and \(\mathrm{ReLU}^k\) activation functions (Q2118396) (← links)
• A neural network based shock detection and localization approach for discontinuous Galerkin methods (Q2123860) (← links)
• Structure probing neural network deflation (Q2124019) (← links)
• Int-Deep: a deep learning initialized iterative method for nonlinear problems (Q2125440) (← links)
• Machine learning for prediction with missing dynamics (Q2128320) (← links)
• SelectNet: self-paced learning for high-dimensional partial differential equations (Q2131038) (← links)