An immense solution space that confounds QC and AI Modern cryptography assumes that mathematical expressions and ...

Quantum mechanics is full of states that seem to defy ordinary sense, but one of its strangest ideas has usually been built ...

Abstract: In this article, a global output feedback control scheme is developed for a class of uncertain nonlinear systems subject to input quantization and unknown output function. By employing a ...

Optical diffractive neural networks are emerging for improving speed and energy efficiency in machine learning. However, the challenges of nonlinear activation functions (e.g., latency issues, high ...

A new technical paper titled “Massively parallel and universal approximation of nonlinear functions using diffractive processors” was published by researchers at UCLA. “Nonlinear computation is ...

Activation functions play a critical role in AI inference, helping to ferret out nonlinear behaviors in AI models. This makes them an integral part of any neural network, but nonlinear functions can ...

Analog In-memory Computing (IMC) has demonstrated energy-efficient and low latency implementation of convolution and fully-connected layers in deep neural networks (DNN) by using physics for computing ...

The restoration of nonlinearly distorted audio signals, alongside the identification of the applied memoryless nonlinear operation, is studied. The paper focuses on the difficult but practically ...

I hope as usual to travel to Germany, India and USA - partly for work - partly for holiday However, there is the book to finish (Mathematical foundations of data science) In an effort to motivate ...

To address the limitations of commonly used cross-validation methods, the linear regression method (LR) was proposed to estimate population accuracy of predictions based on the implicit assumption ...

This perspective article reviews how the chemometrics community approached non-linear methods in the early years. In addition to the basic chemometric methods, some methods that fall under the term ...

In the realm of machine learning, the ability to effectively handle complex, non-linear data is a crucial challenge. Traditional linear models often fall short when confronted with intricate patterns ...