Pages that link to "Item:Q1708845"
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The following pages link to Multilayer feedforward networks are universal approximators (Q1708845):
Displaying 50 items.
- Deep ReLU neural network approximation in Bochner spaces and applications to parametric PDEs (Q6062166) (← links)
- Rates of approximation by ReLU shallow neural networks (Q6062171) (← links)
- Learning topology optimization process via convolutional long‐short‐term memory autoencoder‐decoder (Q6062851) (← links)
- Laplace based Bayesian inference for ordinary differential equation models using regularized artificial neural networks (Q6063150) (← links)
- Neural network-based parametric system identification: a review (Q6063217) (← links)
- A neuro-structural framework for bankruptcy prediction (Q6063321) (← links)
- Uncertainty of feed forward neural networks recognizing quantum contextuality (Q6063374) (← links)
- An introduction to the mathematics of deep learning (Q6064555) (← links)
- Efficient goods inspection demand at ports: a comparative forecasting approach (Q6066629) (← links)
- DEEP EQUILIBRIUM NETS (Q6067145) (← links)
- Comprehensive study of variational Bayes classification for dense deep neural networks (Q6067625) (← links)
- Hybrid analysis and modeling, eclecticism, and multifidelity computing toward digital twin revolution (Q6068233) (← links)
- Universal approximation properties for an ODENet and a ResNet: mathematical analysis and numerical experiments (Q6070659) (← links)
- A Neural Network Approach to High-Dimensional Optimal Switching Problems with Jumps in Energy Markets (Q6070671) (← links)
- Deep learning methods for partial differential equations and related parameter identification problems (Q6070739) (← links)
- A two‐stage forecasting approach for short‐term intermodal freight prediction (Q6070984) (← links)
- Enhanced physics‐informed neural networks for hyperelasticity (Q6071403) (← links)
- Analytical solution of stochastic differential equation by multilayer perceptron neural network approximation of Fokker–Planck equation (Q6071665) (← links)
- Associative anticipatory learning and control of the cerebellar cortex based on the spike-timing-dependent plasticity of the parallel fiber-Purkinje cell synapses (Q6072432) (← links)
- On minimal representations of shallow ReLU networks (Q6072445) (← links)
- Universality of gradient descent neural network training (Q6072577) (← links)
- Finite-time prescribed performance optimal attitude control for quadrotor UAV (Q6072733) (← links)
- Learning-informed parameter identification in nonlinear time-dependent PDEs (Q6073849) (← links)
- Bayesian neural tree models for nonparametric regression (Q6075185) (← links)
- DNN-based speech watermarking resistant to desynchronization attacks (Q6076555) (← links)
- Successfully and efficiently training deep multi-layer perceptrons with logistic activation function simply requires initializing the weights with an appropriate negative mean (Q6077039) (← links)
- A singular Riemannian geometry approach to deep neural networks. I: Theoretical foundations (Q6077759) (← links)
- Nonclosedness of sets of neural networks in Sobolev spaces (Q6078695) (← links)
- Statistical foundation of variational Bayes neural networks (Q6078700) (← links)
- Fading memory echo state networks are universal (Q6078702) (← links)
- A survey on modern trainable activation functions (Q6078705) (← links)
- Empirical strategy for stretching probability distribution in neural-network-based regression (Q6078748) (← links)
- Towards a mathematical framework to inform neural network modelling via polynomial regression (Q6079052) (← links)
- A global neural network learning machine: coupled integer and fractional calculus operator with an adaptive learning scheme (Q6079137) (← links)
- Bayesian nonparametric quantile process regression and estimation of marginal quantile effects (Q6079853) (← links)
- Using deep neural networks for detecting spurious oscillations in discontinuous Galerkin solutions of convection-dominated convection-diffusion equations (Q6080848) (← links)
- <scp>Zero‐sum</scp> game optimal control for the nonlinear switched systems based on heuristic dynamic programming (Q6081048) (← links)
- Robust extreme learning machine neural approach for uncertain nonlinear hyper‐chaotic system identification (Q6082361) (← links)
- A stepwise physics‐informed neural network for solving large deformation problems of hypoelastic materials (Q6082603) (← links)
- \textsc{MLQD}: a package for machine learning-based quantum dissipative dynamics (Q6086773) (← links)
- SONets: sub-operator learning enhanced neural networks for solving parametric partial differential equations (Q6087965) (← links)
- Explainable generalized additive neural networks with independent neural network training (Q6089210) (← links)
- Hybrid approach to predict the effective properties of heterogeneous materials using artificial neural networks and micromechanical models (Q6089273) (← links)
- Deep Koopman model predictive control for enhancing transient stability in power grids (Q6089850) (← links)
- Single-track thermal analysis of laser powder bed fusion process: parametric solution through physics-informed neural networks (Q6094682) (← links)
- DeepStSNet: reconstructing the quantum state-resolved thermochemical nonequilibrium flowfield using deep neural operator learning with scarce data (Q6095078) (← links)
- A cusp-capturing PINN for elliptic interface problems (Q6095097) (← links)
- Deep reinforcement learning for adaptive mesh refinement (Q6095131) (← links)
- Time discretization in the solution of parabolic PDEs with ANNs (Q6096361) (← links)
- Probabilistic neural data fusion for learning from an arbitrary number of multi-fidelity data sets (Q6096443) (← links)
