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Found 8 papers, 2 papers with code
Deep Network Approximation: Beyond ReLU to Diverse Activation Functions
This paper explores the expressive power of deep neural networks for a diverse range of activation functions.
Why Shallow Networks Struggle with Approximating and Learning High Frequency: A Numerical Study
In this work, a comprehensive numerical study involving analysis and experiments shows why a two-layer neural network has difficulties handling high frequencies in approximation and learning when machine precision and computation cost are important factors in real practice.
On Enhancing Expressive Power via Compositions of Single Fixed-Size ReLU Network
This paper explores the expressive power of deep neural networks through the framework of function compositions.
A Dual Iterative Refinement Method for Non-rigid Shape Matching
The key idea is to use dual information, such as spatial and spectral, or local and global features, in a complementary and effective way, and extract more accurate information from current iteration to use for the next iteration.
Efficient and Robust Shape Correspondence via Sparsity-Enforced Quadratic Assignment
To solve the resulting quadratic assignment problem efficiently, the two key ideas of our iterative algorithm are: 1) select pairs with good (approximate) correspondence as anchor points, 2) solve a regularized quadratic assignment problem only in the neighborhood of selected anchor points through sparsity control.
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 random coefficients based on the intrinsic low dimension structure of the underlying elliptic differential operators.
Variational Hamiltonian Monte Carlo via Score Matching
Traditionally, the field of computational Bayesian statistics has been divided into two main subfields: variational methods and Markov chain Monte Carlo (MCMC).
Hamiltonian Monte Carlo Acceleration Using Surrogate Functions with Random Bases
To this end, we build a surrogate function to approximate the target distribution using properly chosen random bases and an efficient optimization process.
