Investigating a second-order optimization strategy for neural networks:
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Abschlussarbeit Buch |
| Sprache: | English |
| Veröffentlicht: |
Passau
2024
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| Online-Zugang: | kostenfrei kostenfrei Inhaltsverzeichnis |
| Beschreibung: | Zusammenfassung in deutscher Sprache |
| Beschreibung: | xv, 59 Seiten Diagramme |
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Datensatz im Suchindex
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Contents List of Publications iii Zusammenfassung v Summary ix List of Acronyms xv Representational Capacity of Deep NeuralNetworks: A Computing Study 1.1 Introduction. 1.2 Attainable Representational Capacity. 1.3 Test Problems with a Known Minimum. 1.4 Optimization Methods . 1.5 Computing Results . 1.5.1 Data Generation . 1.5.2 Optimization Results. 1.6 Discussion. References . 1 1 3 4 5 6 6 6 9 9 2 Singular Value Decomposition andNeural Networks 2.1 Motivation. 2.2 Singular Value Decomposition. 2.3 SVD and Linear Regression. 2.4 SVD and Mappings of a Given
Rank. 2.4.1 Over-determined Problems . 2.4.2 Under-determined Problems. 2.5 SVD and Linear Networks . 2.6 SVD and Initializing Nonlinear Neural Networks. 2.7 Computing Experiments . 2.8 Conclusion and Discussion. References . 11 11 12 13 14 14 15 16 17 18 19 20 1 3 Number of Attention Heads vs. Number of Transformer-encoders in Com puter Vision 23 3.1 Introduction. 3.2 Parameter structureof a multi-head transformer. 3.3 Measuring the degree of overdetermination. 3.4 Computing results. 3.4.1 Dataset MNIST. 3.4.2 Dataset CIFAR-100. 3.4.3 Dataset CUB-200-2011
. 3.4.4 Dataset places365 . 3.4.5 Dataset imagenet. 3.5 Conclusions. 23 24 24 26 27 28 28 28 29 30 xiii
Contents References. 31 4 Training Neural Networks in Single vs. Double Precision 4.1 Introduction. 4.2 Second-order optimization methods: factors depending on machine precision 4.3 Controlling the extent of nonlinearity. 4.4 Comparison with RMSprop. 4.5 Computing results. 4.5.1 Moderately nonlinear problems. 4.5.2 Strongly nonlinear problems. 4.6 Summary and discussion. References. 33 33 33 35 36 36 38 39 40 42 Make Deep Networks Shallow Again 5.1 Introduction. 5.2 Decomposition of stacked residual connections. 5.3 Setup of computing experiments. 5.4 Computing experiments
. 5.4.1 With a single filter. 5.4.2 With multiple filters. 5.4.3 Trade-off of the number of filters and the number of layers . 5.5 Statistics of experiments . 5.6 Conclusion. References. 45 45 46 49 50 50 51 53 53 55 55 5 Acknowledgments xiv 59 |
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| spelling | Bermeitinger, Bernhard Verfasser (DE-588)128624823X aut Investigating a second-order optimization strategy for neural networks Bernhard Bermeitinger Passau 2024 xv, 59 Seiten Diagramme txt rdacontent n rdamedia nc rdacarrier Zusammenfassung in deutscher Sprache Dissertation Universität Passau 2024 Konjugierte-Gradienten-Methode (DE-588)4255670-3 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf (DE-588)4113937-9 Hochschulschrift gnd-content Maschinelles Lernen (DE-588)4193754-5 s Konjugierte-Gradienten-Methode (DE-588)4255670-3 s DE-604 Erscheint auch als Online-Ausgabe urn:nbn:de:bvb:739-opus4-14087 https://opus4.kobv.de/opus4-uni-passau/frontdoor/index/index/docId/1408 Verlag kostenfrei Volltext https://nbn-resolving.de/urn:nbn:de:bvb:739-opus4-14087 Resolving-System kostenfrei Volltext Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=035038936&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Bermeitinger, Bernhard Investigating a second-order optimization strategy for neural networks Konjugierte-Gradienten-Methode (DE-588)4255670-3 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4255670-3 (DE-588)4193754-5 (DE-588)4113937-9 |
| title | Investigating a second-order optimization strategy for neural networks |
| title_auth | Investigating a second-order optimization strategy for neural networks |
| title_exact_search | Investigating a second-order optimization strategy for neural networks |
| title_full | Investigating a second-order optimization strategy for neural networks Bernhard Bermeitinger |
| title_fullStr | Investigating a second-order optimization strategy for neural networks Bernhard Bermeitinger |
| title_full_unstemmed | Investigating a second-order optimization strategy for neural networks Bernhard Bermeitinger |
| title_short | Investigating a second-order optimization strategy for neural networks |
| title_sort | investigating a second order optimization strategy for neural networks |
| topic | Konjugierte-Gradienten-Methode (DE-588)4255670-3 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Konjugierte-Gradienten-Methode Maschinelles Lernen Hochschulschrift |
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