Innovative Topologies and Algorithms for Neural Networks

• Autore: Xibilia Maria Gabriella
• Pubblicazione: Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021
• Descrizione fisica: 1 online resource (198 p.)
• Soggetto topico: Information technology industries
• Soggetto non controllato: 3D convolution neural network; action recognition; alternative fusion neural network; Arabic named entity recognition; attention mechanism; autoencoders; bidirectional gated recurrent unit; bidirectional recurrent neural network; Chinese text classification; CNN; convolution neural Networks; convolutional neural network; convolutional neural networks; deep convolutional neural networks; deep learning; distributed systems; elements recognition; embedded deep learning; facial image analysis; facial nerve paralysis; feature fusion; fully convolutional network; fused features; gesture recognition; gradient-weighted class activation maps; graph convolutional network; graph partitioning; GRU; human computer interaction; image classification; Internet of Things; IoT (Internet of Thing) systems; linguistic features; long short-term memory; long short-term-memory; long-short-term memory networks; LSTM; LSTM-CRF model; medical applications; motion map; multi-label learning; natural language processing; object detection network; object recognition; pedestrian attribute recognition; POS syntactic rules; resource-efficient inference; scalability; sentiment analysis; sentiment attention mechanism; ship identification; text recognition; tooth-marked tongue; word embedding
• Persona (resp. second.): GrazianiSalvatore; XibiliaMaria Gabriella
• Sommario/riassunto: 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 processing, medical applications, and IoT (Internet of Things). The probability of a successful outcome from a neural network is linked to selection of an appropriate network architecture and training algorithm. Accordingly, much of the recent research on neural networks has been devoted to the study and proposal of novel architectures, including solutions tailored to specific problems. This book gives significant contributions to the above-mentioned fields by merging theoretical aspects and relevant applications.
• Titolo autorizzato: Innovative Topologies and Algorithms for Neural Networks
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910557553903321
• Lo trovi qui: Univ. Federico II