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Found 6 papers, 1 papers with code
Deep Conditional Generative Learning: Model and Error Analysis
We introduce an Ordinary Differential Equation (ODE) based deep generative method for learning a conditional distribution, named the Conditional Follmer Flow.
Semi-Supervised Deep Sobolev Regression: Estimation, Variable Selection and Beyond
We propose SDORE, a semi-supervised deep Sobolev regressor, for the nonparametric estimation of the underlying regression function and its gradient.
Provable Advantage of Parameterized Quantum Circuit in Function Approximation
To achieve this, we utilize techniques from quantum signal processing and linear combinations of unitaries to construct PQCs that implement multivariate polynomials.
Current density impedance imaging with PINNs
In this paper, we introduce CDII-PINNs, a computationally efficient method for solving CDII using PINNs in the framework of Tikhonov regularization.
GAS: A Gaussian Mixture Distribution-Based Adaptive Sampling Method for PINNs
With the recent study of deep learning in scientific computation, the Physics-Informed Neural Networks (PINNs) method has drawn widespread attention for solving Partial Differential Equations (PDEs).
Deep Neural Networks with ReLU-Sine-Exponential Activations Break Curse of Dimensionality in Approximation on Hölder Class
In this paper, we construct neural networks with ReLU, sine and $2^x$ as activation functions.
