Deep learning: concepts and architectures:
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| Other Authors: | , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Cham
Springer
[2020]
|
| Series: | Studies in Computational Intelligence
volume 866 |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis Klappentext |
| Physical Description: | xii, 342 Seiten Illustrationen, Diagramme (teilweise farbig) |
| ISBN: | 9783030317553 |
| ISSN: | 1860-949X |
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| 245 | 1 | 0 | |a Deep learning: concepts and architectures |c Witold Pedrycz, Shyi-Ming Chen, editors |
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Contents Deep Learning Architectures. Mohammad-Parsa Hosseini, Senbao Lu, Kavin Kamaraj, Alexander Słowikowski and Haygreev C. Venkatesh 1 Background. 2 Training Procedure. 2.1 Supervised Learning. 2.2 Unsupervised Learning. 2.3 Semi-supervised Learning. 3 Deep Learning Categories. 3.1 Convolutional Neural Networks (CNNs). 3.2 Pretrained Unsupervised Networks. 3.3 Recurrent and Recursive Neural Networks. 4 Conclusions. References. Theoretical Characterization of Deep Neural Networks. Piyush Kaul and Brejesh Lall 1 Overview. 2 Neural Net Architecture. 3
Brief Mathematical Background. 3.1 Topology and Manifolds. 3.2 Riemannian Geometry and Curvature. 3.3 Signal Processing on Graphs. 4 Characterization by Homological Complexity. 4.1 Betti Numbers. 4.2 Architecture Selection from Homologyof Dataset. 4.3 Computational Homology. 4.4 Empirical Measurements. 1 2 2 2 3 3 4 4 10 13 22 23 25 25 26 31 31 33 37 40 40 42 42 43 vii
viii Contents 5 Characterization by Scattering Transform. 5.1 Overview. 5.2 Invariants and Symmetries. 5.3 Translation and Diffeomorphisms . 5.4 Contraction and Scale Separation by Wavelets. 5.5 Filter Bank, Phase Removal and Contractions. 5.6 Translation Groups. 5.7 Inverse Scattering and Sparsity. 6 Characterization by Curvature. 6.1 Mean Field Theory and Gaussian Curvature. 6.2 Riemannian and Ricci Curvature Measurement. References. Scaling Analysis of Specialized Tensor Processing Architectures for Deep Learning Models. Yuri Gordienko, Yuriy Kochura, Vlad Taran, Nikita Gordienko, Alexandr Rokovyi, Oleg Alienin and Sergii Stirenko 1 Introduction. 2 Background and Related
Work. 2.1 Tensor Cores. 2.2 Tensor ProcessingUnits. 2.3 Other DNNs Accelerators. 2.4 Parallel Algorithms and Tensor Processing Architectures. 2.5 Parallel Algorithms and Computing Complexity in DNNs. 3 Experimental and Computational Details. 3.1 Datasets, Equipment, Metrics, and Models. 3.2 Computing Complexity of DNNs. 3.3 Scaling Analysis. 4 Results. 4.1 Vggl6. 4.2 ResNet50. 4.3 CapsNet. 5 Discussion. 6 Conclusions.
References. Assessment of Autoencoder Architectures for Data Representation . Karishma Pawar and Vahida Z. Attar 1 Introduction. 2 General Architecture and Taxonomy of Autoencoders . 3 Variants of Autoencoders. 3.1 Application Specific Autoencoders . 3.2 Regularized Autoencoders. 3.3 Robust Autoencoders Tolerant to Noise. 3.4 Generative Autoencoders . 45 45 46 47 48 48 49 51 51 51 56 61 65 66 67 68 69 69 70 71 73 73 77 78 79 79 85 85 91 95 96 101 102 103 104 106 110 113 114
Contents ix 4 Factors Affecting Overall Performance of Autoencoders. 4.1 Training. 4.2 Objective Function. 4.3 Activation Functions. 4.4 Layer Size and Depth. 5 Applications of Autoencoders. 6 Conclusion. Appendix. References. 117 117 118 120 120 120 126 126 128 The Encoder-Decoder Framework and ItsApplications. Ahmad Asadi and Reza Safabakhsh 1 Introduction. 1.1 Machine Translation. 1.2 Image/Video Captioning. 1.3 Textual/Visual Question Answering. 1.4 Text
Summarization. 2 Baseline Encoder-Decoder Model. 2.1 Background. 2.2 The Encoder-Decoder Model forMachine Translation. 2.3 Formulation. 2.4 Encoders in Machine Translation(Feature Extraction) . 2.5 Decoders in Machine Translation(Language Modeling). 3 Encoder Structure Varieties. 3.1 Sentence as Input. 3.2 Image as Input. 3.3 Video as Input. 4 Decoder Structure Varieties. 4.1 Long-Term Dependencies. 4.2 LSTMs. 4.3 Stacked RNNs. 4.4 Vanishing Gradients in Stacked Decoders. 4.5 Reinforcement
Learning. 5 Attention Mechanism. 5.1 Basic Mechanism. 5.2 Extensions. 6 Future Work. 7 Conclusion. References. 133 Deep Learning for Learning Graph Representations. Wenwu Zhu, Xin Wang and Peng Cut 1 Introduction. 169 133 134 135 135 136 136 136 137 137 139 140 141 142 143 144 151 151 152 152 154 156 160 160 161 163 163 164 169
x Contents 2 High Order Proximity Preserving Network Embedding. 171 2.1 Problem Definition. 172 2.2 The SDNE Model . 173 2.3 Analysis and Discussions on SDNE. 178 3 Global Structure Preserving Network Embedding. 179 3.1 Preliminaries and Definitions. 180 3.2 The DRNE Model. 181 4 Structure Preserving Hyper Network Embedding. 185 4.1 Notations and Definitions. 187 4.2 The DHNE Model. 188 5 Uncertainty-Aware Network Embedding. 192 5.1 Notations. 193 5.2 The DVNE Model. 194 6 Dynamic-Aware Network Embedding. 197 6.1 The DepthLGP Model. 199 6.2 Extensions and Variants. 205 7 Conclusion
and Future Work. 206 References. 207 Deep Neural Networks forCorrupted Labels. Ishan Jindal, Matthew Nokleby, Daniel Pressel, Xuewen Chen and Harpreet Singh 1 Introduction. 2 Label Noise. 3 Relationship to Prior Work. 4 Proposed Approach . 4.1 Proposed Approach . 4.2 Justifying the Nonlinear Noise Model. 5 Experimental Results . 5.1 General Setting. 5.2 Artificial Label Noise. 5.3 Real Label Noise. 5.4 Effect of Batch Size. 5.5 Understanding Noise
Model. 6 Conclusion and Future Work. References. 211 212 214 215 217 218 221 223 223 224 228 230 231 233 233 Constructing a Convolutional Neural Network with a Suitable Capacity for a SemanticSegmentation Task. 237 Yalong Jiang and Zheru Chi 1 Introduction. 238 2 Techniques to Fully Explore the Potential of Low-Capacity Networks. 244 2.1 Methodology. 244
Contents 3 Estimation of Task Complexity. 3.1 Methodology. 3.2 Summary. 4 Optimization of Model Capacity. 4.1 Methodology. 4.2 Summary. 5 Conclusion and Future Work. References. Using Convolutional Neural Networks to Forecast Sporting Event Results. Mu-Yen Chen, Ting-Hsuan Chen and Shu-Hong Lin 1 Introduction. 2 Literature Review. 2.1 Convolutional Neural Network Architecture. 2.2 Related Research Regarding Sports Predictions. 3 Research Methods. 3.1 Development
Environment. 3.2 Research Process. 3.3 Experiment Design. 3.4 Performance Evaluation. 4 Experiment Results. 4.1 Dataset Description. 4.2 Results of Experiments 1and 2. 4.3 Results of Experiment 3. 4.4 Results of Experiment 4. 4.5 Discussion. 5 Conclusions. References. Heterogeneous Computing System for Deep Learning. Mihaela Maliţa, George Vlăduţ Popescu and Gheorghe M. Ştefan 1 Introduction. 2 The Computational Components of a DNN Involved in Deep Learning. 2.1 Fully Connected
Layers. 2.2 Convolution Layer. 2.3 Pooling Layer. 2.4 Softmax Layer. 2.5 Putting All Together. 3 The State of the Art. 3.1 Intel’s MIC. 3.2 Nvidia’s GPU as GPGPU. 3.3 Google’s TPUs. 3.4 Concluding About the State of the Art. xi 251 251 256 256 256 264 265 265 269 270 271 271 272 273 273 273 277 279 279 279 280 281 282 283 284 285 287 287 288 289 290 292 293 294 294 295 296 299 300
xii Contents 4 Map-Scan/Reduce Accelerator. 4.1 The Heterogeneous System. 4.2 The Accelerator’s Structure. 4.3 The Micro-architecture. 4.4 Hardware Parameters of MSRA. 4.5 NeuralKemel library. 5 Implementation and Evaluation. 5.1 Fully Connected NN. 5.2 Convolutional Layer. 5.3 Pooling Layer. 5.4 Softmax Layer. 6 Conclusions. References. 302 302 303 304 308 309 310 311 312 315 316 317 318 Progress in Neural Network Based Statistical Language Modeling . Anup Shrikant Kunte and Vahida Z. Attar 1 Introduction. 2 Statistical Language
Modeling. 2.1 N-Gram Language Model. 3 Extensions to N-Gram Language Model . 4 Neural Network Based Language Modeling. 4.1 Neural Network Language Model(NNLM). 4.2 Recurrent Neural Network LanguageModels (RNNLM). 4.3 Long Short Term Memory LanguageModels (LSTMLM). 4.4 Bidirectional RNN. 5 Milestones in NNLM Research. 6 Evaluation Metrics. 6.1 State of the Art PPL. 7 Conclusion. References. 321 Index 341 322 323 324 325 328 328 330 331 332 334 336 337 338 338
Studies in Computational Intelligence 866 Witold Pedrycz · Shyi-Ming Chen Editors Deep Learning: Concepts and Architectures This 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 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 solutions to real-world problems. The book also demonstrates how the architectural developments, which arise in the setting of deep learning, support detailed learning and refinements to the system design. Featuring detailed descriptions of the 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. |
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| spelling | Deep learning: concepts and architectures Witold Pedrycz, Shyi-Ming Chen, editors Cham Springer [2020] © 2020 xii, 342 Seiten Illustrationen, Diagramme (teilweise farbig) txt rdacontent n rdamedia nc rdacarrier Studies in Computational Intelligence volume 866 1860-949X Computational Intelligence Artificial Intelligence Computational intelligence Artificial intelligence Deep Learning (DE-588)1135597375 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf (DE-588)4143413-4 Aufsatzsammlung gnd-content Maschinelles Lernen (DE-588)4193754-5 s DE-604 Deep Learning (DE-588)1135597375 s Pedrycz, Witold 1953- (DE-588)122838203 edt Chen, Shyi-Ming (DE-588)1097439267 edt Erscheint auch als Online-Ausgabe 978-3-030-31756-0 Studies in Computational Intelligence volume 866 (DE-604)BV020822171 866 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=031682195&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=031682195&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext |
| spellingShingle | Deep learning: concepts and architectures Studies in Computational Intelligence Computational Intelligence Artificial Intelligence Computational intelligence Artificial intelligence Deep Learning (DE-588)1135597375 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)1135597375 (DE-588)4193754-5 (DE-588)4143413-4 |
| title | Deep learning: concepts and architectures |
| title_auth | Deep learning: concepts and architectures |
| title_exact_search | Deep learning: concepts and architectures |
| title_full | Deep learning: concepts and architectures Witold Pedrycz, Shyi-Ming Chen, editors |
| title_fullStr | Deep learning: concepts and architectures Witold Pedrycz, Shyi-Ming Chen, editors |
| title_full_unstemmed | Deep learning: concepts and architectures Witold Pedrycz, Shyi-Ming Chen, editors |
| title_short | Deep learning: concepts and architectures |
| title_sort | deep learning concepts and architectures |
| topic | Computational Intelligence Artificial Intelligence Computational intelligence Artificial intelligence Deep Learning (DE-588)1135597375 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Computational Intelligence Artificial Intelligence Computational intelligence Artificial intelligence Deep Learning Maschinelles Lernen Aufsatzsammlung |
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