Pages that link to "Item:Q1708845"

The following pages link to Multilayer feedforward networks are universal approximators (Q1708845):

Displaying 50 items.

• A clustering-based portfolio strategy incorporating momentum effect and market trend prediction (Q2201385) (← links)
• A further study on the inequality constraints in stochastic configuration networks (Q2215130) (← links)
• Nonparametric regression using deep neural networks with ReLU activation function (Q2215715) (← links)
• A review on deep learning in medical image reconstruction (Q2218098) (← links)
• System inference for the spatio-temporal evolution of infectious diseases: Michigan in the time of COVID-19 (Q2221739) (← links)
• Approximating smooth functions by deep neural networks with sigmoid activation function (Q2222228) (← links)
• ConvPDE-UQ: convolutional neural networks with quantified uncertainty for heterogeneous elliptic partial differential equations on varied domains (Q2222287) (← links)
• A mesh-free method for interface problems using the deep learning approach (Q2222664) (← links)
• Simulator-free solution of high-dimensional stochastic elliptic partial differential equations using deep neural networks (Q2223019) (← 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)
• On the pricing formula for the perpetual American volatility option under the mean-reverting processes (Q2233615) (← links)
• A global universality of two-layer neural networks with ReLU activations (Q2236407) (← links)
• Data-driven identification of 2D partial differential equations using extracted physical features (Q2236988) (← links)
• A deep learning driven pseudospectral PCE based FFT homogenization algorithm for complex microstructures (Q2237801) (← links)
• A statistician teaches deep learning (Q2241468) (← links)
• Quantile regression neural networks: a Bayesian approach (Q2241709) (← links)
• Hybrid adaptive control of nonlinear systems with non-Lipschitz nonlinearities (Q2242945) (← links)
• A goodness-of-fit test based on neural network sieve estimators (Q2244442) (← links)
• Parametric deep energy approach for elasticity accounting for strain gradient effects (Q2246296) (← links)
• Local extreme learning machines and domain decomposition for solving linear and nonlinear partial differential equations (Q2246361) (← links)
• Extreme learning machine collocation for the numerical solution of elliptic PDEs with sharp gradients (Q2246423) (← links)
• Optimal market-making strategies under synchronised order arrivals with deep neural networks (Q2246653) (← links)
• Artificial neural network approximations of Cauchy inverse problem for linear PDEs (Q2247118) (← links)
• A deep learning semiparametric regression for adjusting complex confounding structures (Q2247451) (← links)
• Estimating critical values for testing the i.i.d. in standardized residuals from GARCH models in finite samples (Q2255852) (← links)
• Mining the customer credit using classification and regression tree and multivariate adaptive regression splines (Q2257606) (← links)
• Optimal design method to automobile problems using holographic neural network's approximation (Q2258392) (← links)
• Neural network modeling of vector multivariable functions in ill-posed approximation problems (Q2263827) (← links)
• Recent developments in parameter estimation and structure identification of biochemical and genomic systems (Q2270528) (← links)
• RAD-NNET, a neural network based correlation developed for a realistic simulation of the non-gray radiative heat transfer effect in three-dimensional gas-particle mixtures (Q2270963) (← links)
• Neurons the decision makers. II: The firings of many neurons and their density; the neural network its connections and field of firings (Q2281713) (← links)
• Stochastic seismic waveform inversion using generative adversarial networks as a geological prior (Q2284094) (← links)
• The complexity of explaining neural networks through (group) invariants (Q2290685) (← links)
• A deep energy method for finite deformation hyperelasticity (Q2292258) (← links)
• A zero-gradient-sum algorithm for distributed cooperative learning using a feedforward neural network with random weights (Q2293057) (← links)
• Tracking control of nonaffine systems using bio-inspired networks with auto-tuning activation functions and self-growing neurons (Q2293069) (← links)
• Quantile forecasting and data-driven inventory management under nonstationary demand (Q2294360) (← links)
• Finite-horizon optimal tracking guidance for aircraft based on approximate dynamic programming (Q2298912) (← links)
• Data-driven acceleration of multiscale methods for uncertainty quantification: application in transient multiphase flow in porous media (Q2300559) (← links)
• Universality of deep convolutional neural networks (Q2300759) (← links)
• On the relative expressiveness of Bayesian and neural networks (Q2302782) (← links)
• Neural-network-based adaptive quasi-consensus of nonlinear multi-agent systems with communication constrains and switching topologies (Q2304065) (← links)
• The \(\delta \)-machine: classification based on distances towards prototypes (Q2304085) (← links)
• A machine learning framework for data driven acceleration of computations of differential equations (Q2305115) (← links)
• Observer-based adaptive consensus tracking control for nonlinear multi-agent systems with actuator hysteresis (Q2308144) (← links)
• Computational mechanics enhanced by deep learning (Q2310108) (← links)
• Neural network as a function approximator and its application in solving differential equations (Q2313205) (← links)
• Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations (Q2314336) (← links)
• Quantitative approximation by perturbed Kantorovich-Choquet neural network operators (Q2314635) (← links)