47 citations · 106 across the 22 of their papers we have counts for
8 papers · 1 filter
Sample-Efficient Sparse Phase Retrieval via Stochastic Alternating Minimization
Jian-Feng Cai, Yuling Jiao, Xiliang Lu +1
In this work we propose a nonconvex two-stage \underline{s}tochastic \underline{a}lternating \underline{m}inimizing (SAM) method for sparse phase retrieval. The proposed algorithm…
Just Least Squares: Binary Compressive Sampling with Low Generative Intrinsic Dimension
Yuling Jiao, Dingwei Li, Min Liu +2
In this paper, we consider recovering dimensional signals from binary measurements corrupted by noises and sign flips under the assumption that the target signals have low…
A Data-Driven Line Search Rule for Support Recovery in High-dimensional Data Analysis
Peili Li, Yuling Jiao, Xiliang Lu +1
In this work, we consider the algorithm to the (nonlinear) regression problems with penalty. The existing algorithms for based optimization problem are often carr…
Analysis of Deep Ritz Methods for Laplace Equations with Dirichlet Boundary Conditions
Chenguang Duan, Yuling Jiao, Yanming Lai +3
Deep Ritz methods (DRM) have been proven numerically to be efficient in solving partial differential equations. In this paper, we present a convergence rate in norm for dee…
Global Optimization via Schr{ö}dinger-F{ö}llmer Diffusion
Yin Dai, Yuling Jiao, Lican Kang +2
We study the problem of finding global minimizers of approximately via sampling from a probability distribution with density $p_σ(x)=…
Coordinate Descent for MCP/SCAD Penalized Least Squares Converges Linearly
Yuling Jiao, Dingwei Li, Min Liu +1
Recovering sparse signals from observed data is an important topic in signal/imaging processing, statistics and machine learning. Nonconvex penalized least squares have been attrac…
A rate of convergence of Physics Informed Neural Networks for the linear second order elliptic PDEs
Yuling Jiao, Yanming Lai, Dingwei Li +4
In recent years, physical informed neural networks (PINNs) have been shown to be a powerful tool for solving PDEs empirically. However, numerical analysis of PINNs is still missing…
Deep Neural Networks with ReLU-Sine-Exponential Activations Break Curse of Dimensionality in Approximation on Hölder Class
Yuling Jiao, Yanming Lai, Xiliang Lu +3
In this paper, we construct neural networks with ReLU, sine and as activation functions. For general continuous defined on with continuity modulus ,…