Hongkai Zhao | DeepAI

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Featured Co-authors

Jianfeng Lu
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Yuchen He
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Rongjie Lai
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Shijun Zhang
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Yimin Zhong
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Sijing Li
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∙ 07/13/2023

Deep Network Approximation: Beyond ReLU to Diverse Activation Functions

This paper explores the expressive power of deep neural networks for a d...

0 Shijun Zhang, et al. ∙

∙ 06/29/2023

Why Shallow Networks Struggle with Approximating and Learning High Frequency: A Numerical Study

In this work, a comprehensive numerical study involving analysis and exp...

0 Shijun Zhang, et al. ∙

∙ 01/29/2023

On Enhancing Expressive Power via Compositions of Single Fixed-Size ReLU Network

This paper studies the expressive power of deep neural networks from the...

0 Shijun Zhang, et al. ∙

∙ 06/10/2022

How much can one learn from a single solution of a PDE?

Linear evolution PDE ∂_t u(x,t) = -ℒ u, where ℒ is a strongly elliptic o...

0 Hongkai Zhao, et al. ∙

∙ 04/10/2022

How much can one learn a partial differential equation from its solution?

In this work we study the problem about learning a partial differential ...

0 Yuchen He, et al. ∙

∙ 12/22/2020

Quantitative PAT with simplified P_N approximation

The photoacoustic tomography (PAT) is a hybrid modality that combines th...

0 Hongkai Zhao, et al. ∙

∙ 07/26/2020

A Dual Iterative Refinement Method for Non-rigid Shape Matching

In this work, a simple and efficient dual iterative refinement (DIR) met...

0 Rui Xiang, et al. ∙

∙ 03/19/2020

Efficient and Robust Shape Correspondence via Sparsity-Enforced Quadratic Assignment

In this work, we introduce a novel local pairwise descriptor and then de...

0 Rui Xiang, et al. ∙

∙ 01/27/2020

A fast algorithm for time-dependent radiative transport equation based on integral formulation

In this work, we introduce a fast numerical algorithm to solve the time-...

0 Hongkai Zhao, et al. ∙

∙ 08/27/2019

Separability of the kernel function in an integral formulation for anisotropic radiative transfer equation

We study in this work an integral formulation for the radiative transfer...

0 Kui Ren, et al. ∙

∙ 07/01/2019

A data-driven approach for multiscale elliptic PDEs with random coefficients based on intrinsic dimension reduction

We propose a data-driven approach to solve multiscale elliptic PDEs with...

0 Sijing Li, et al. ∙

∙ 05/01/2018

Intrinsic Complexity And Scaling Laws: From Random Fields to Random Vectors

Random fields are commonly used for modeling of spatially (or timely) de...

0 Jennifer Bryson, et al. ∙

∙ 02/06/2016

Variational Hamiltonian Monte Carlo via Score Matching

Traditionally, the field of computational Bayesian statistics has been d...

0 Cheng Zhang, et al. ∙

∙ 06/18/2015

Hamiltonian Monte Carlo Acceleration Using Surrogate Functions with Random Bases

For big data analysis, high computational cost for Bayesian methods ofte...

0 Cheng Zhang, et al. ∙

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