Pages that link to "Item:Q4277151"

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

Displaying 50 items.

• Greedy approximation in convex optimization (Q2343051) (← links)
• Global Mittag-Leffler stability of complex valued fractional-order neural network with discrete and distributed delays (Q2374453) (← links)
• Estimates of covering numbers of convex sets with slowly decaying orthogonal subsets (Q2381815) (← links)
• Stein's identity, Fisher information, and projection pursuit: A triangulation (Q2382857) (← links)
• Density estimation with stagewise optimization of the empirical risk (Q2384149) (← links)
• Annealing stochastic approximation Monte Carlo algorithm for neural network training (Q2384162) (← links)
• Approximating the sheep milk production curve through the use of artificial neural networks and genetic algorithms (Q2387287) (← links)
• Approximation of level continuous fuzzy-valued functions by multilayer regular fuzzy neural networks (Q2390159) (← links)
• The errors of approximation for feedforward neural networks in thelpmetric (Q2390185) (← links)
• Approximation capabilities of multilayer fuzzy neural networks on the set of fuzzy-valued functions (Q2390390) (← links)
• High-dimensional change-point estimation: combining filtering with convex optimization (Q2397167) (← links)
• Neural network with unbounded activation functions is universal approximator (Q2399647) (← links)
• Approximation of discontinuous signals by sampling Kantorovich series (Q2408764) (← links)
• Cornell potential: a neural network approach (Q2414985) (← links)
• Pointwise and uniform approximation by multivariate neural network operators of the max-product type (Q2418225) (← links)
• Estimating composite functions by model selection (Q2438264) (← links)
• Nonparametric neural network estimation of Lyapunov exponents and a direct test for chaos (Q2439050) (← links)
• A collocation method for solving nonlinear Volterra integro-differential equations of neutral type by sigmoidal functions (Q2450721) (← links)
• Aggregation for Gaussian regression (Q2456016) (← links)
• Parameter redundancy in neural networks: an application of Chebyshev polynomials (Q2468329) (← links)
• A deletion/substitution/addition algorithm for classification neural networks, with applications to biomedical data (Q2475733) (← links)
• Efficient sampling in approximate dynamic programming algorithms (Q2477014) (← links)
• Approximation and learning by greedy algorithms (Q2477053) (← links)
• Weighted quadrature formulas and approximation by zonal function networks on the sphere (Q2496179) (← links)
• Simultaneous approximation by greedy algorithms (Q2498388) (← links)
• Algorithms and complexity in biological pattern formation problems (Q2498919) (← links)
• Relevance of functional flexibility for heterogeneous sales response models: a comparison of parametric and semi-nonparametric models (Q2503072) (← links)
• Path relinking and GRG for artificial neural networks (Q2570150) (← links)
• Model selection in neural networks: some difficulties (Q2572255) (← links)
• Boosting with early stopping: convergence and consistency (Q2583412) (← links)
• Rates of minimization of error functionals over Boolean variable-basis functions (Q2583503) (← links)
• Max-product neural network and quasi-interpolation operators activated by sigmoidal functions (Q2630379) (← links)
• A Sobolev-type upper bound for rates of approximation by linear combinations of Heaviside plane waves (Q2643849) (← links)
• Machine learning based data retrieval for inverse scattering problems with incomplete data (Q2660886) (← links)
• Learning nonlinear state-space models using autoencoders (Q2665158) (← links)
• Learning the mapping \(\mathbf{x}\mapsto \sum\limits_{i=1}^d x_i^2\): the cost of finding the needle in a haystack (Q2667355) (← links)
• Estimation of a regression function on a manifold by fully connected deep neural networks (Q2676904) (← links)
• A deep first-order system least squares method for solving elliptic PDEs (Q2679352) (← links)
• Active learning based sampling for high-dimensional nonlinear partial differential equations (Q2683063) (← links)
• A deep Fourier residual method for solving PDEs using neural networks (Q2683430) (← links)
• Side effects of learning from low-dimensional data embedded in a Euclidean space (Q2687305) (← links)
• Mini-workshop: Analysis of data-driven optimal control. Abstracts from the mini-workshop held May 9--15, 2021 (hybrid meeting) (Q2693004) (← links)
• Computation and learning in high dimensions. Abstracts from the workshop held August 1--7, 2021 (hybrid meeting) (Q2693017) (← links)
• Low-rank kernel approximation of Lyapunov functions using neural networks (Q2696116) (← links)
• Greedy training algorithms for neural networks and applications to PDEs (Q2699382) (← links)
• Neural network-based variational methods for solving quadratic porous medium equations in high dimensions (Q2699489) (← links)
• Sobolev-type embeddings for neural network approximation spaces (Q2700875) (← links)
• Specification of training sets and the number of hidden neurons for multilayer perceptrons (Q2770860) (← links)
• Reduction methods and chaos for quadratic systems of differential equations (Q2807452) (← links)
• Convergence for a family of neural network operators in Orlicz spaces (Q2965304) (← links)