"Moreover, the work shows that neural network models, despite their advancement in recent years, are unable to match human performance in this regard, which should lead to more AI research to learn from the neuroscience of human vision. This future research will be aided significantly by the d...
Over the past few decades, researchers have developed deep neural network-based models that can complete a broad range of tasks. Some of these techniques are specifically designed to process and generate coherent texts in multiple languages, translate texts, answer questions about a text and create ...
The Open Neural Network Exchange (ONNX) is an open standard format created to represent machine learning models. Supported by a robust community of partners, ONNX defines a common set of operators and a common file format to enable AI developers to use models with a variety of frameworks, ...
Neural mass models have a long history. Lopez da silva36,37, Jansen and Rit38,39, Wendling40,41, Wilson–Cowan42,43,44,45, Freeman46,47,48, and Wong–Wang49,50are some models that investigated the collective behavior of neurons. These models allow researchers to study different rhythms an...
Unlike other adaptive techniques, the impact of the RProp adaptation process is not affected by the unpredictable influence of derivative magnitude but solely relies on the temporal pattern of its sign. The resampling allows us to avoid neural network overfitting that distinguishes our approach from ...
3. Deep Neural Networks and Computational Graphs 3.1. Deep Neural Networks An artificial neural network (ANN) [54] can be seen as a deterministic non-linear function f ( · : W ) parametrized by a matrix W . An ANN with L hidden layers defines a mapping from a given input x to a ...
The Open Neural Network Exchange (ONNX) is an open standard format created to represent machine learning models. Supported by a robust community of partners, ONNX defines a common set of operators and a common file format to enable AI developers to use models with a variety of frameworks, to...
neural network-based (CNN) methods. Although most studies use hybrid transformer architectures, some have also built pure transformer-based models. For instance, Gao et al. [140] have proposed a hybrid transformer-based architecture UTNet by integrating a complexity-reduced self-attention into a ...
Thedeep neural networkis one of the most popular AI/ML models.The design for this deep learning model was inspired by the human brain and its neural network. This AI model uses layers of artificial neurons to combine multiple inputs and provide a single output value. Hence the name, deep ...
Research and applications of artificial neural network in pavement engineering: A state-of-the-art review 2021, Journal of Traffic and Transportation Engineering (English Edition) Citation Excerpt : On the other hand, researchers hope to discover more valuable or interesting knowledge behind the physica...