The following pages link to (Q4938227):
Displaying 50 items.
- Numerical solution and bifurcation analysis of nonlinear partial differential equations with extreme learning machines (Q2236543) (← links)
- Neural network regression for Bermudan option pricing (Q2239248) (← links)
- Misspecified diffusion models with high-frequency observations and an application to neural networks (Q2239259) (← links)
- Absence of bottlenecks in a neural network determines its generic functional properties (Q2243704) (← links)
- Extreme learning machine collocation for the numerical solution of elliptic PDEs with sharp gradients (Q2246423) (← links)
- Interpolation by neural network operators activated by ramp functions (Q2252489) (← links)
- Convergence of a family of neural network operators of the Kantorovich type (Q2252929) (← links)
- Approximation by series of sigmoidal functions with applications to neural networks (Q2255377) (← links)
- Limitations of shallow nets approximation (Q2292226) (← links)
- Error bounds for approximations with deep ReLU networks (Q2292227) (← links)
- Universality of deep convolutional neural networks (Q2300759) (← links)
- MgNet: a unified framework of multigrid and convolutional neural network (Q2316958) (← links)
- On some aspects of approximation of ridge functions (Q2344299) (← links)
- Characterization of an extremal sum of ridge functions (Q2372916) (← links)
- Approximation of Sobolev classes by polynomials and ridge functions (Q2389529) (← links)
- Neural network with unbounded activation functions is universal approximator (Q2399647) (← links)
- Almost everywhere approximation capabilities of double Mellin approximate identity neural networks (Q2403275) (← links)
- A note on the equioscillation theorem for best ridge function approximation (Q2403626) (← links)
- On the representation by linear superpositions (Q2425409) (← links)
- A collocation method for solving nonlinear Volterra integro-differential equations of neutral type by sigmoidal functions (Q2450721) (← links)
- Approximate dynamic programming for stochastic \(N\)-stage optimization with application to optimal consumption under uncertainty (Q2450902) (← links)
- Assessing the effectiveness of artificial neural networks on problems related to elliptic curve cryptography (Q2470209) (← links)
- A recursive algorithm for nonlinear least-squares problems (Q2475618) (← links)
- Determining the number of real roots of polynomials through neural networks (Q2507014) (← links)
- Pseudo-dimension and entropy of manifolds formed by affine-invariant dictionary (Q2509171) (← links)
- On the error of approximation by ridge functions with two fixed directions (Q2628087) (← links)
- A Sobolev-type upper bound for rates of approximation by linear combinations of Heaviside plane waves (Q2643849) (← links)
- Wasserstein generative adversarial uncertainty quantification in physics-informed neural networks (Q2671386) (← links)
- DeepParticle: learning invariant measure by a deep neural network minimizing Wasserstein distance on data generated from an interacting particle method (Q2672762) (← links)
- A shallow Ritz method for elliptic problems with singular sources (Q2675616) (← links)
- A-WPINN algorithm for the data-driven vector-soliton solutions and parameter discovery of general coupled nonlinear equations (Q2677793) (← links)
- Neural networks in Fréchet spaces (Q2679424) (← links)
- Optimal control by deep learning techniques and its applications on epidemic models (Q2684035) (← links)
- DeepBND: a machine learning approach to enhance multiscale solid mechanics (Q2687559) (← links)
- Multi-scale fusion network: a new deep learning structure for elliptic interface problems (Q2691986) (← links)
- Mini-workshop: Analysis of data-driven optimal control. Abstracts from the mini-workshop held May 9--15, 2021 (hybrid meeting) (Q2693004) (← links)
- Control of partial differential equations via physics-informed neural networks (Q2696946) (← links)
- A three layer neural network can represent any multivariate function (Q2697707) (← links)
- Sobolev-type embeddings for neural network approximation spaces (Q2700875) (← links)
- Complexity of Shallow Networks Representing Finite Mappings (Q2814145) (← links)
- Convergence for a family of neural network operators in Orlicz spaces (Q2965304) (← links)
- How Deep Are Deep Gaussian Processes? (Q4558207) (← links)
- Deep distributed convolutional neural networks: Universality (Q4560301) (← links)
- Deep learning in high dimension: Neural network expression rates for generalized polynomial chaos expansions in UQ (Q4615657) (← links)
- An introduction to the use of neural networks in control systems (Q4790551) (← links)
- 10.1162/153244303322753724 (Q4827822) (← links)
- Suboptimal Policies for Stochastic $$N$$-Stage Optimization: Accuracy Analysis and a Case Study from Optimal Consumption (Q4979399) (← links)
- Data-Driven Learning of Nonautonomous Systems (Q4997352) (← links)
- Semiglobal optimal feedback stabilization of autonomous systems via deep neural network approximation (Q4999517) (← links)
- Higher-Order Quasi-Monte Carlo Training of Deep Neural Networks (Q5015302) (← links)
