ABSTRACT: Artificial deep neural networks (ADNNs) have become a cornerstone of modern machine learning, but they are not immune to challenges. One of the most significant problems plaguing ADNNs is ...
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 ...
In many machine learning applications, particularly those involving sequence modeling or structured data, there is a need for layers that allow more flexible interactions between input features and ...
Abstract: We propose and experimentally demonstrate a reconfigurable nonlinear activation function (NAF) unit based on add-drop resonator Mach-Zehnder interferometers (ADRMZIs) for photonic neural ...
Abstract: We investigated the performance of a CVRC-based nonlinear equalizer designed to compensate for fiber-optic nonlinearity when varying the activation function. The performance tended to ...
Neural networks are one typical structure on which artificial intelligence can be based. The term neural describes their learning ability, which to some extent mimics the functioning of neurons in our ...
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 ...
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