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 ...

Investigators seize luxury goods over alleged fraud White men feel something 'much deeper' has changed that's 'fundamentally illiberal': Writer Jake Paul reacts after losing fight to Anthony Joshua: ...

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: Over-the-air (OTA) computing has emerged as a promising technique that utilizes the superposition property of the wireless multiple access channel (MAC) as a means for computation. In this ...

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 ...

Abstract: Nonlinear function approximation plays a critical role in the area efficiency and accuracy of transformer-based hardware accelerators. Functions such as softmax, GELU, SiLU, and layer ...

Scientists at Weill Cornell Medicine have developed a new algorithm, the Krakencoder, that merges multiple types of brain imaging data to better understand how the brain s wiring underpins behavior, ...

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 ...