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06/24/2023
Current density impedance imaging with PINNs
In this paper, we introduce CDII-PINNs, a computationally efficient meth...
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04/17/2023
Deep Neural Network Approximation of Composition Functions: with application to PINNs
In this paper, we focus on approximating a natural class of functions th...
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03/28/2023
GAS: A Gaussian Mixture Distribution-Based Adaptive Sampling Method for PINNs
With recent study of the deep learning in scientific computation, the PI...
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07/14/2022
Stochastic mirror descent method for linear ill-posed problems in Banach spaces
Consider linear ill-posed problems governed by the system A_i x = y_i fo...
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04/14/2022
Efficient and practical quantum compiler towards multi-qubit systems with deep reinforcement learning
Efficient quantum compiling tactics greatly enhance the capability of qu...
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04/05/2022
Imaging Conductivity from Current Density Magnitude using Neural Networks
Conductivity imaging represents one of the most important tasks in medic...
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03/09/2022
Convergence Rate Analysis of Galerkin Approximation of Inverse Potential Problem
In this work we analyze the inverse problem of recovering the space-depe...
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12/15/2021
Sample-Efficient Sparse Phase Retrieval via Stochastic Alternating Minimization
In this work we propose a nonconvex two-stage stochastic alternating min...
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11/21/2021
A Data-Driven Line Search Rule for Support Recovery in High-dimensional Data Analysis
In this work, we consider the algorithm to the (nonlinear) regression pr...
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11/03/2021
Analysis of Deep Ritz Methods for Laplace Equations with Dirichlet Boundary Conditions
Deep Ritz methods (DRM) have been proven numerically to be efficient in ...
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09/18/2021
Coordinate Descent for MCP/SCAD Penalized Least Squares Converges Linearly
Recovering sparse signals from observed data is an important topic in si...
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09/04/2021
Convergence Analysis for the PINNs
In recent years, physical informed neural networks (PINNs) have been sho...
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03/24/2021
Convergence Rate Analysis for Deep Ritz Method
Using deep neural networks to solve PDEs has attracted a lot of attentio...
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02/28/2021
Deep Neural Networks with ReLU-Sine-Exponential Activations Break Curse of Dimensionality on Hölder Class
In this paper, we construct neural networks with ReLU, sine and 2^x as a...
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12/11/2020
Generative Learning With Euler Particle Transport
We propose an Euler particle transport (EPT) approach for generative lea...
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06/03/2020
Robust Decoding from Binary Measurements with Cardinality Constraint Least Squares
The main goal of 1-bit compressive sampling is to decode n dimensional s...
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05/18/2020
Sparse Signal Recovery From Phaseless Measurements via Hard Thresholding Pursuit
In this paper, we consider the sparse phase retrival problem, recovering...
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01/27/2020
On Newton Screening
Screening and working set techniques are important approaches to reducin...
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01/16/2020
A Support Detection and Root Finding Approach for Learning High-dimensional Generalized Linear Models
Feature selection is important for modeling high-dimensional data, where...
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06/14/2019
A stochastic alternating minimizing method for sparse phase retrieval
Sparse phase retrieval plays an important role in many fields of applied...
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10/09/2018
SNAP: A semismooth Newton algorithm for pathwise optimization with optimal local convergence rate and oracle properties
We propose a semismooth Newton algorithm for pathwise optimization (SNAP...
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09/06/2018
Oblique Stripe Removal in Remote Sensing Images via Oriented Variation
Destriping is a classical problem in remote sensing image processing. Al...
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11/03/2017
Robust Decoding from 1-Bit Compressive Sampling with Least Squares
In 1-bit compressive sensing (1-bit CS) where target signal is coded int...
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03/03/2014
A Primal Dual Active Set with Continuation Algorithm for the ℓ^0-Regularized Optimization Problem
We develop a primal dual active set with continuation algorithm for solv...
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10/04/2013