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 ...

Research team debuts the first deterministic streaming algorithms for non-monotone submodular maximization, delivering superior approximation ratios with minimal memory and real-time throughput on ...

Multi-step temporal-difference (TD) learning, where the update targets contain information from multiple time steps ahead, is one of the most popular forms of TD learning for linear function ...

Abstract: This paper proposes a novel iterative gradient-based optimization approach aimed at achieving more precise and streamlined approximations for the Gaussian Q function—an essential element in ...

This fully updated volume explores a wide array of new and state-of-the-art tools and resources for protein function prediction. Beginning with in-depth overviews of essential underlying computational ...

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 ...

Two of the existing Models (IEEET1 #157, TGOV1 #151) define member functions with identical method signatures to implement a smooth approximation of a piecewise function. A static class or equivalent ...

The method of nested multiplication is commonly used in function evaluation routines to evaluate approximation polynomials. New polynomial evaluation methods have been developed in recent years which ...

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 ...

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 ...