標(biāo)題: Titlebook: Deep Learning: Concepts and Architectures; Witold Pedrycz,Shyi-Ming Chen Book 2020 Springer Nature Switzerland AG 2020 Computational Intel [打印本頁] 作者: ABS 時間: 2025-3-21 19:36
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書目名稱Deep Learning: Concepts and Architectures網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Deep Learning: Concepts and Architectures被引頻次
書目名稱Deep Learning: Concepts and Architectures被引頻次學(xué)科排名
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書目名稱Deep Learning: Concepts and Architectures讀者反饋
書目名稱Deep Learning: Concepts and Architectures讀者反饋學(xué)科排名
作者: 臥虎藏龍 時間: 2025-3-21 22:39 作者: 防止 時間: 2025-3-22 02:02
Deep Neural Networks for Corrupted Labels,AR-10, CIFAR-100 and ImageNet datasets and on a large-scale Clothing 1M dataset with inherent label noise. Further, we show that with the different initialization and the regularization of the noise model, we can apply this learning procedure to text classification?tasks as well. We evaluate the per作者: Obsessed 時間: 2025-3-22 06:57 作者: Painstaking 時間: 2025-3-22 12:11 作者: 完全 時間: 2025-3-22 14:45 作者: 完全 時間: 2025-3-22 17:16
1860-949X current trends in the design and analysis of deep learning topologies, the book offers practical guidelines and presents competitive solutions to various areas of language modeling, graph representation, and forecasting.978-3-030-31758-4978-3-030-31756-0Series ISSN 1860-949X Series E-ISSN 1860-9503 作者: observatory 時間: 2025-3-23 00:04 作者: 多產(chǎn)魚 時間: 2025-3-23 04:39
https://doi.org/10.1007/978-3-322-90228-3ompression. Due to wide availability of high-end processing chips and large datasets, deep learning has gained a lot attention from academia, industries and research centers to solve multitude of problems. Considering the state-of-the-art literature, autoencoders are widely used architectures in man作者: companion 時間: 2025-3-23 08:44 作者: 欲望 時間: 2025-3-23 09:50 作者: quiet-sleep 時間: 2025-3-23 14:29
https://doi.org/10.1007/978-3-322-97122-7nalyze the training results of a variety of model structures. While previous studies have applied convolutional neural networks to image or object recognition, our study proposes a specific encoding method that is integrated with deep learning in order to predict the results of future games. The pre作者: SPALL 時間: 2025-3-23 21:40 作者: 起來了 時間: 2025-3-24 01:58
https://doi.org/10.1007/978-3-322-90228-3works, namely, Convolutional Neural?Networks, Pretrained Unspervised Networks, and Recurrent/Recursive Neural Networks. Applications of each of these architectures?in selected areas such as pattern recognition and image detection are also discussed.作者: Adjourn 時間: 2025-3-24 03:02
https://doi.org/10.1007/978-3-322-90228-3omplexity and curvature. We also describe neural networks from the viewpoints of scattering transforms?and share some of the mathematical and intuitive justifications for those. We finally share a technique for visualizing and analyzing neural networks based on concept of Riemann?curvature.作者: 壓倒 時間: 2025-3-24 09:42
https://doi.org/10.1007/978-3-322-90228-3utput sentences is provided. Finally, the attention mechanism which is a technique to cope with long-term dependencies and to improve the encoder-decoder performance on sophisticated tasks is studied.作者: Gorilla 時間: 2025-3-24 13:54 作者: 得罪人 時間: 2025-3-24 17:21 作者: Flagging 時間: 2025-3-24 21:46 作者: guzzle 時間: 2025-3-25 01:44
1860-949X mplementations and case studies, identifying the best designThis book introduces readers to the fundamental concepts of deep learning and offers practical insights into how this learning paradigm supports automatic mechanisms of structural knowledge representation. It discusses a number of multilaye作者: CHOP 時間: 2025-3-25 03:39
Die kommunale Stadt des Mittelaltersnference. The investigation on efficient representation of a graph has profound theoretical significance and important realistic meaning, we therefore introduce some basic ideas in graph representation/network embedding as well as some representative models in this chapter.作者: 慎重 時間: 2025-3-25 07:37 作者: 聽覺 時間: 2025-3-25 13:49
https://doi.org/10.1007/978-3-322-97122-7ter describes and evaluates the proposed accelerator?for the main computational intensive components of a DNN: the fully connected layer, the convolution layer, the pooling layer, and the softmax layer.作者: 蒙太奇 時間: 2025-3-25 19:22
Heterogeneous Computing System for Deep Learning,ter describes and evaluates the proposed accelerator?for the main computational intensive components of a DNN: the fully connected layer, the convolution layer, the pooling layer, and the softmax layer.作者: neutralize 時間: 2025-3-25 21:06
Book 2020atic mechanisms of structural knowledge representation. It discusses a number of multilayer architectures giving rise to tangible and functionally meaningful pieces of knowledge, and shows how the structural developments have become essential to the successful delivery of competitive practical solut作者: gregarious 時間: 2025-3-26 02:32 作者: Delirium 時間: 2025-3-26 07:17 作者: Interim 時間: 2025-3-26 12:06 作者: 隼鷹 時間: 2025-3-26 14:13 作者: 喚醒 時間: 2025-3-26 18:16
978-3-030-31758-4Springer Nature Switzerland AG 2020作者: PURG 時間: 2025-3-26 23:18 作者: URN 時間: 2025-3-27 04:01
https://doi.org/10.1007/978-3-322-90228-3, 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作者: 泛濫 時間: 2025-3-27 08:07 作者: 粗魯?shù)娜?nbsp; 時間: 2025-3-27 11:31 作者: 1分開 時間: 2025-3-27 14:09 作者: 迎合 時間: 2025-3-27 18:52 作者: 敬禮 時間: 2025-3-27 23:37 作者: 玉米 時間: 2025-3-28 03:24 作者: 地名表 時間: 2025-3-28 09:27
Sch?ffensprüche und Ratsurteilediction and depth estimation, Convolutional Neural Networks (CNNs) still perform unsatisfactorily in some difficult tasks such as human parsing which is the focus of our research. The inappropriate capacity of a CNN model and insufficient training data both contribute to the failure in perceiving th作者: 暖昧關(guān)系 時間: 2025-3-28 13:41 作者: vitreous-humor 時間: 2025-3-28 17:19
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作者: CHIP 時間: 2025-3-28 21:38
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作者: Employee 時間: 2025-3-29 01:36
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作者: travail 時間: 2025-3-29 03:31 作者: Indicative 時間: 2025-3-29 10:31
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作者: Pander 時間: 2025-3-29 15:19
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作者: Chagrin 時間: 2025-3-29 17:33
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 作者: impale 時間: 2025-3-29 23:24
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作者: SPECT 時間: 2025-3-30 03:46 作者: Favorable 時間: 2025-3-30 05:58
