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 ...
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 the ...
RNN regressor currently has linear for both hidden and final layer activations, which essentially defeats the purpose of using a neural network and reduces the whole setup to linear regression. If you ...
Researchers at the University of California, Los Angeles (UCLA) have developed an optical computing framework that performs large-scale nonlinear computations using linear materials. Reported in ...
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: The efficient training of Transformer-based neural networks on resource-constrained personal devices is attracting continuous attention due to domain adaptions and privacy concerns. However, ...
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 ...
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