Microsoft

Approximation Lasso Methods for Language Modeling

Lasso is a regularization method for parameter estimation in linear models. It optimizes the model parameters with respect to a loss function subject to model complexities. This paper explores the use ...
Frontiers

Research on error control method for polynomial approximation based on equal amplitude oscillation theorem

1 Warwick Mathematics Institute, The University of Warwick, Coventry, United Kingdom 2 School of Computer and Information Engineering, Luoyang Institute of Science and Technology, Luoyang, China To ...
IEEE

Successive Radial Basis Function Approximation for Finite-Horizon Nonlinear H∞ Control Problems

Abstract: In this paper, a successive radial basis function (RBF) approximation approach is proposed to solve the Hamilton-Jacobi-Isaacs (HJI) partial differential equation (PDE) associated with ...
GitHub

An efficient method of spline approximation for power function

Let $P(m, X, N)$ be an $m$-degree polynomial in $X\in\mathbb{R}$ having fixed non-negative integers $m$ and $N$. Essentially, the polynomial $P(m, X, N)$ is a result ...
IEEE

Nonlinear Value Function Approximation Method with Easy Hyperparameter Tuning and Convergence Guarantee

Abstract: Reinforcement Learning is a branch of machine learning to learn control strategies that achieve a given objective through trial-and-error in the environment ...
Science Daily

New scan method unveils lung function secrets

A new method of scanning lungs is able to show in real time how air moves in and out of the lungs as people take a breath in patients with asthma, chronic obstructive pulmonary disease (COPD), and ...
Scientific Research Publishing

Sarra, S.A. and Kansa, E.J. (2009) Multiquadric Radial Basis Function Approximation Methods for the Numerical Solution of Partial Differential Equations.

ABSTRACT: Accurately approximating higher order derivatives is an inherently difficult problem. It is shown that a random variable shape parameter strategy can improve the accuracy of approximating ...
Scientific Research Publishing

Sarra, S.A. and Sturgill, D. (2009) A Random Variable Shape Parameter Strategy for Radial Basis Function Approximation Methods.

ABSTRACT: Accurately approximating higher order derivatives is an inherently difficult problem. It is shown that a random variable shape parameter strategy can improve the accuracy of approximating ...
marktechpost

Kolmogorov-Arnold Networks (KANs): A New Era of Interpretability and Accuracy in Deep Learning

Multi-layer perceptrons (MLPs), or fully-connected feedforward neural networks, are fundamental in deep learning, serving as default models for approximating nonlinear functions. Despite their ...