Hierarchical representations in machine learning and many-body quantum physics
En col·laboració amb la Universitat Autònoma de Barcelona (UAB) i la Universitat de Barcelona (UB)
| Autor: | |
|---|---|
| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2017 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/117738 |
| Acceso en línea: | https://hdl.handle.net/2117/117738 |
| Access Level: | acceso abierto |
| Palabra clave: | Machine learning Image processing Hierarchical representations CNN Tree Tensor Networks(TTNs) Manifold Learning t-SNE. Aprenentatge automàtic Imatges -- Processament Àrees temàtiques de la UPC::Enginyeria de la telecomunicació |
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Hierarchical representations in machine learning and many-body quantum physicsDeep Learning in Quantum PhysicsBlázquez García, RaúlMachine learningImage processingHierarchical representationsCNNTree Tensor Networks(TTNs)Manifold Learningt-SNE.Aprenentatge automàticImatges -- ProcessamentÀrees temàtiques de la UPC::Enginyeria de la telecomunicacióEn col·laboració amb la Universitat Autònoma de Barcelona (UAB) i la Universitat de Barcelona (UB)Over the past few years a number of proofs have emerged revealing the many connections between the methods used in quantum-many body physics and those in machine learning. In particular, much attention has been given to tensor networks (TNs) and deep learning architectures which exhibit striking similarities. Finding those similarities has helped us gain a better understanding on why deep learning architectures have so much expressive efficiency. Recently, machine learning techniques have been used to approximate many-body physics problems. Conversely, TNs have been used for machine learning tasks. For example, state-of-the-art research has used one-dimensional TNs to solve image recognition problems with very limited scalability. The scalability problem, however, can be overcomed by using a two-dimensional hierarchical TNs and a training algorithm derived from the multipartite entanglement renormalization ansatz (MERA). Here we give further analysis of the inner structural resemblance between such hierarchical tensor networks (TNs) and pretrained convolutional neural networks (CNNs): this was achieved by analyzing their abstraction power layer by layer.Universitat Politècnica de CatalunyaWittek, PeterAcín dal Maschio, Antonio20172017-10-2720182018-06-01master thesishttp://purl.org/coar/resource_type/c_bdccNAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/masterThesisapplication/pdfhttps://hdl.handle.net/2117/117738reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2http://creativecommons.org/licenses/by-nc-nd/3.0/es/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/1177382026-05-27T15:37:01Z |
| dc.title.none.fl_str_mv |
Hierarchical representations in machine learning and many-body quantum physics Deep Learning in Quantum Physics |
| title |
Hierarchical representations in machine learning and many-body quantum physics |
| spellingShingle |
Hierarchical representations in machine learning and many-body quantum physics Blázquez García, Raúl Machine learning Image processing Hierarchical representations CNN Tree Tensor Networks(TTNs) Manifold Learning t-SNE. Aprenentatge automàtic Imatges -- Processament Àrees temàtiques de la UPC::Enginyeria de la telecomunicació |
| title_short |
Hierarchical representations in machine learning and many-body quantum physics |
| title_full |
Hierarchical representations in machine learning and many-body quantum physics |
| title_fullStr |
Hierarchical representations in machine learning and many-body quantum physics |
| title_full_unstemmed |
Hierarchical representations in machine learning and many-body quantum physics |
| title_sort |
Hierarchical representations in machine learning and many-body quantum physics |
| dc.creator.none.fl_str_mv |
Blázquez García, Raúl |
| author |
Blázquez García, Raúl |
| author_facet |
Blázquez García, Raúl |
| author_role |
author |
| dc.contributor.none.fl_str_mv |
Wittek, Peter Acín dal Maschio, Antonio |
| dc.subject.none.fl_str_mv |
Machine learning Image processing Hierarchical representations CNN Tree Tensor Networks(TTNs) Manifold Learning t-SNE. Aprenentatge automàtic Imatges -- Processament Àrees temàtiques de la UPC::Enginyeria de la telecomunicació |
| topic |
Machine learning Image processing Hierarchical representations CNN Tree Tensor Networks(TTNs) Manifold Learning t-SNE. Aprenentatge automàtic Imatges -- Processament Àrees temàtiques de la UPC::Enginyeria de la telecomunicació |
| description |
En col·laboració amb la Universitat Autònoma de Barcelona (UAB) i la Universitat de Barcelona (UB) |
| publishDate |
2017 |
| dc.date.none.fl_str_mv |
2017 2017-10-27 2018 2018-06-01 |
| dc.type.none.fl_str_mv |
master thesis http://purl.org/coar/resource_type/c_bdcc NA http://purl.org/coar/version/c_be7fb7dd8ff6fe43 |
| dc.type.openaire.fl_str_mv |
info:eu-repo/semantics/masterThesis |
| format |
masterThesis |
| dc.identifier.none.fl_str_mv |
https://hdl.handle.net/2117/117738 |
| url |
https://hdl.handle.net/2117/117738 |
| dc.language.none.fl_str_mv |
Inglés eng |
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Inglés |
| language |
eng |
| dc.rights.none.fl_str_mv |
open access http://purl.org/coar/access_right/c_abf2 http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
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info:eu-repo/semantics/openAccess |
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open access http://purl.org/coar/access_right/c_abf2 http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
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openAccess |
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application/pdf |
| dc.publisher.none.fl_str_mv |
Universitat Politècnica de Catalunya |
| publisher.none.fl_str_mv |
Universitat Politècnica de Catalunya |
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reponame:UPCommons. Portal del coneixement obert de la UPC instname:Universitat Politècnica de Catalunya (UPC) |
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Universitat Politècnica de Catalunya (UPC) |
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UPCommons. Portal del coneixement obert de la UPC |
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UPCommons. Portal del coneixement obert de la UPC |
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1869419933327163392 |
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15,300719 |