Pages that link to "Item:Q4277151"
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The following pages link to Universal approximation bounds for superpositions of a sigmoidal function (Q4277151):
Displaying 50 items.
- Solving Fredholm integral equations using deep learning (Q2144736) (← links)
- Deep learning for the partially linear Cox model (Q2148978) (← links)
- Error estimates for deep learning methods in fluid dynamics (Q2149063) (← links)
- Numerical bifurcation analysis of PDEs from lattice Boltzmann model simulations: a parsimonious machine learning approach (Q2149520) (← links)
- Multivariate neural network interpolation operators (Q2151614) (← links)
- Approximation properties of deep ReLU CNNs (Q2157922) (← links)
- Uniform approximation rates and metric entropy of shallow neural networks (Q2157931) (← links)
- ReLU deep neural networks from the hierarchical basis perspective (Q2159911) (← links)
- Retracted: Model order reduction method based on machine learning for parameterized time-dependent partial differential equations (Q2161825) (← links)
- Nonconvex regularization for sparse neural networks (Q2168678) (← links)
- Voronovskaja type theorems and high-order convergence neural network operators with sigmoidal functions (Q2178844) (← links)
- Approximation-based fixed-time adaptive tracking control for a class of uncertain nonlinear pure-feedback systems (Q2179154) (← links)
- Standard representation and unified stability analysis for dynamic artificial neural network models (Q2179309) (← links)
- Optimal approximation of piecewise smooth functions using deep ReLU neural networks (Q2182898) (← links)
- Nonlinear approximation via compositions (Q2185653) (← links)
- Theory of deep convolutional neural networks: downsampling (Q2185717) (← links)
- Universal approximation with quadratic deep networks (Q2185719) (← links)
- Robust min-max optimal control design for systems with uncertain models: a neural dynamic programming approach (Q2185769) (← links)
- Deep neural network structures solving variational inequalities (Q2194605) (← links)
- A comparative analysis of optimization and generalization properties of two-layer neural network and random feature models under gradient descent dynamics (Q2197845) (← links)
- Correction of AI systems by linear discriminants: probabilistic foundations (Q2200569) (← links)
- EO-MTRNN: evolutionary optimization of hyperparameters for a neuro-inspired computational model of spatiotemporal learning (Q2210956) (← links)
- Fast construction of correcting ensembles for legacy artificial intelligence systems: algorithms and a case study (Q2213117) (← links)
- Nonparametric regression using deep neural networks with ReLU activation function (Q2215715) (← links)
- A review on deep learning in medical image reconstruction (Q2218098) (← links)
- Analysis of the rate of convergence of fully connected deep neural network regression estimates with smooth activation function (Q2222227) (← links)
- Data driven governing equations approximation using deep neural networks (Q2222362) (← links)
- Convergence of the deep BSDE method for coupled FBSDEs (Q2223111) (← links)
- Negative results for approximation using single layer and multilayer feedforward neural networks (Q2226355) (← links)
- Kolmogorov width decay and poor approximators in machine learning: shallow neural networks, random feature models and neural tangent kernels (Q2226529) (← links)
- Deep ReLU network expression rates for option prices in high-dimensional, exponential Lévy models (Q2238770) (← links)
- Estimation of agent-based models using Bayesian deep learning approach of BayesFlow (Q2246641) (← links)
- Almost optimal estimates for approximation and learning by radial basis function networks (Q2251472) (← links)
- Interpolation by neural network operators activated by ramp functions (Q2252489) (← links)
- Approximation by series of sigmoidal functions with applications to neural networks (Q2255377) (← links)
- Neural network modeling of vector multivariable functions in ill-posed approximation problems (Q2263827) (← links)
- Nonparametric nonlinear regression using polynomial and neural approximators: a numerical comparison (Q2271788) (← links)
- Approximation with random bases: pro et contra (Q2282874) (← links)
- Semi-nonparametric approximation and index options (Q2292040) (← links)
- Piecewise convexity of artificial neural networks (Q2292218) (← links)
- Insights into randomized algorithms for neural networks: practical issues and common pitfalls (Q2292952) (← links)
- Universality of deep convolutional neural networks (Q2300759) (← links)
- A machine learning framework for data driven acceleration of computations of differential equations (Q2305115) (← links)
- On deep learning as a remedy for the curse of dimensionality in nonparametric regression (Q2313286) (← links)
- Quantitative approximation by perturbed Kantorovich-Choquet neural network operators (Q2314635) (← links)
- MgNet: a unified framework of multigrid and convolutional neural network (Q2316958) (← links)
- A nonparametric ensemble binary classifier and its statistical properties (Q2322566) (← links)
- Diffusion nets (Q2325536) (← links)
- Machine learning approximation algorithms for high-dimensional fully nonlinear partial differential equations and second-order backward stochastic differential equations (Q2327815) (← links)
- On universal estimators in learning theory (Q2342272) (← links)
