Titlebook: Deep Learning: Concepts and Architectures; Witold Pedrycz,Shyi-Ming Chen Book 2020 Springer Nature Switzerland AG 2020 Computational Intel

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https://doi.org/10.1007/978-3-322-97122-7 power, the bandwidth and the energy requested by the current developments of the domain are very high. The solutions offered by the current architectural environment are far from being efficient. We propose a hybrid computational system for running efficiently the training and inference DNN algorit

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Sch?ffensprüche und Ratsurteile(ASR), Statistical Machine Translation (SMT), Sentence completion, Automatic Text Generation to name a few. Good Quality Language Model has been one of the key success factors for many commercial NLP applications. Since past three decades diverse research communities like psychology, neuroscience, d

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Deep Learning Architectures,, image detection, pattern recognition, and natural language?processing. Deep learning?architectures have revolutionized the analytical landscape for big data amidst wide-scale deployment of sensory networks and improved communication protocols. In this chapter, we will discuss multiple deep learnin

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Scaling Analysis of Specialized Tensor Processing Architectures for Deep Learning Models,ng complexity of the algorithmically different components of some deep neural networks (DNNs) was considered with regard to their further use on such TPAs. To demonstrate the crucial difference between TPU and GPU computing architectures, the real computing complexity of various algorithmically diff

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Assessment of Autoencoder Architectures for Data Representation,ning the representation of data with lower dimensions. Traditionally, autoencoders have been widely used for data compression in order to represent the structural data. Data compression is one of the most important tasks in applications based on Computer Vision, Information Retrieval, Natural Langua

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The Encoder-Decoder Framework and Its Applications,loyed the encoder-decoder based models to solve sophisticated tasks such as image/video captioning, textual/visual question answering, and text summarization. In this work we study the baseline encoder-decoder framework in machine translation and take a brief look at the encoder structures proposed

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Deep Learning for Learning Graph Representations,ng amount of network data in the recent years. However, the huge amount of network data has posed great challenges for efficient analysis. This motivates the advent of graph representation which maps the graph into a low-dimension vector space, keeping original graph structure and supporting graph i

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