Innovative Topologies and Algorithms for Neural Networks

• Weitere Verfasser: Xibilia, Maria Gabriella (Herausgegeben von), Graziani, Salvatore (Herausgegeben von)
• Format: Elektronisch Buchkapitel
• Sprache: Englisch
• Veröffentlicht: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021
• Schlagworte: Information technology industries; facial image analysis; facial nerve paralysis; deep convolutional neural networks; image classification; Chinese text classification; long short-term memory; convolutional neural network; Arabic named entity recognition; bidirectional recurrent neural network; GRU; LSTM; natural language processing; word embedding; CNN; object detection network; attention mechanism; feature fusion; LSTM-CRF model; elements recognition; linguistic features; POS syntactic rules; action recognition; fused features; 3D convolution neural network; motion map; long short-term-memory; tooth-marked tongue; gradient-weighted class activation maps; ship identification; fully convolutional network; embedded deep learning; scalability; gesture recognition; human computer interaction; alternative fusion neural network; deep learning; sentiment attention mechanism; bidirectional gated recurrent unit; Internet of Things; convolutional neural networks; graph partitioning; distributed systems; resource-efficient inference; pedestrian attribute recognition; graph convolutional network; multi-label learning; autoencoders; long-short-term memory networks; convolution neural Networks; object recognition; sentiment analysis; text recognition; IoT (Internet of Thing) systems; medical applications
• Online-Zugang: DOAB: download the publication; DOAB: description of the publication

The introduction of new topologies and training procedures to deep neural networks has solicited a renewed interest in the field of neural computation. The use of deep structures has significantly improved state-of-the-art applications in many fields, such as computer vision, speech and text process...