Understanding a molecule that plays a key role in nitrogen fixing – a chemical process that enables life on Earth – has long ...

New research indicates that the structural organization of the human brain does not develop in a continuous, linear fashion ...

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 ...

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 ...

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 ...

Abstract: This paper compares the performance of activation function hardware under exponential function approximation techniques. The activation function is a key component of deep neural networks, ...

ABSTRACT: Radial Basis Function methods for scattered data interpolation and for the numerical solution of PDEs were originally implemented in a global manner. Subsequently, it was realized that the ...

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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 ...