The following pages link to (Q4938227):
Displaying 50 items.
- Approximation Error Analysis of Some Deep Backward Schemes for Nonlinear PDEs (Q5021399) (← links)
- Optimization with learning-informed differential equation constraints and its applications (Q5024338) (← links)
- DIFFUSION ON FRACTAL OBJECTS MODELING AND ITS PHYSICS-INFORMED NEURAL NETWORK SOLUTION (Q5024806) (← links)
- Optimal Approximation with Sparsely Connected Deep Neural Networks (Q5025773) (← links)
- New Error Bounds for Deep ReLU Networks Using Sparse Grids (Q5025775) (← links)
- Deep Neural Networks, Generic Universal Interpolation, and Controlled ODEs (Q5037577) (← links)
- (Q5053184) (← links)
- (Q5054614) (← links)
- (Q5054641) (← links)
- A deep learning approach to Reduced Order Modelling of parameter dependent partial differential equations (Q5058646) (← links)
- Theoretical issues in deep networks (Q5073211) (← links)
- Trading Signals in VIX Futures (Q5075243) (← links)
- Deep ReLU Networks Overcome the Curse of Dimensionality for Generalized Bandlimited Functions (Q5079533) (← links)
- (Q5080573) (← links)
- Full error analysis for the training of deep neural networks (Q5083408) (← links)
- A note on the applications of one primary function in deep neural networks (Q5097859) (← links)
- (Q5104591) (← links)
- Deep ReLU networks and high-order finite element methods (Q5132226) (← links)
- Deep neural network expression of posterior expectations in Bayesian PDE inversion (Q5139333) (← links)
- (Q5148965) (← links)
- DeepXDE: A Deep Learning Library for Solving Differential Equations (Q5150214) (← links)
- Approximation Properties of Ridge Functions and Extreme Learning Machines (Q5154637) (← links)
- Plateau Phenomenon in Gradient Descent Training of RELU Networks: Explanation, Quantification, and Avoidance (Q5157837) (← links)
- Finite Neuron Method and Convergence Analysis (Q5162357) (← links)
- On the Convergence of Physics Informed Neural Networks for Linear Second-Order Elliptic and Parabolic Type PDEs (Q5162370) (← links)
- Variational Representations and Neural Network Estimation of Rényi Divergences (Q5162628) (← links)
- Deep Nitsche Method: Deep Ritz Method with Essential Boundary Conditions (Q5163229) (← links)
- Equivalence of approximation by convolutional neural networks and fully-connected networks (Q5218202) (← links)
- Approximation by sums of ridge functions with fixed directions (Q5369335) (← links)
- Computing the Approximation Error for Neural Networks with Weights Varying on Fixed Directions (Q5378401) (← links)
- A Single Hidden Layer Feedforward Network with Only One Neuron in the Hidden Layer Can Approximate Any Univariate Function (Q5380543) (← links)
- Spline representation and redundancies of one-dimensional ReLU neural network models (Q5873929) (← links)
- A New Function Space from Barron Class and Application to Neural Network Approximation (Q5878925) (← links)
- Deep advantage learning for optimal dynamic treatment regime (Q5879958) (← links)
- Neural network approximation (Q5887830) (← links)
- Turnpike in optimal control of PDEs, ResNets, and beyond (Q5887835) (← links)
- Convergence of Physics-Informed Neural Networks Applied to Linear Second-Order Elliptic Interface Problems (Q5887902) (← links)
- A Proof that Artificial Neural Networks Overcome the Curse of Dimensionality in the Numerical Approximation of Black–Scholes Partial Differential Equations (Q5889064) (← links)
- Complexity of neural network approximation with limited information: A worst case approach (Q5946400) (← links)
- Approximation bounds for norm constrained neural networks with applications to regression and GANs (Q6038825) (← links)
- A deep network construction that adapts to intrinsic dimensionality beyond the domain (Q6054952) (← links)
- On the approximation of functions by tanh neural networks (Q6055124) (← links)
- The generalized extreme learning machines: tuning hyperparameters and limiting approach for the Moore-Penrose generalized inverse (Q6055151) (← links)
- Approximation capabilities of neural networks on unbounded domains (Q6055159) (← links)
- On the capacity of deep generative networks for approximating distributions (Q6055167) (← links)
- On sharpness of error bounds for multivariate neural network approximation (Q6060396) (← links)
- Approximating smooth and sparse functions by deep neural networks: optimal approximation rates and saturation (Q6062170) (← links)
- Three ways to solve partial differential equations with neural networks — A review (Q6068232) (← links)
- Deep learning methods for partial differential equations and related parameter identification problems (Q6070739) (← links)
- Kähler geometry of framed quiver moduli and machine learning (Q6072334) (← links)
