Essays about: "inbäddning"

Showing result 6 - 10 of 36 essays containing the word inbäddning.

  1. 6. Advancing Keyword Clustering Techniques: A Comparative Exploration of Supervised and Unsupervised Methods : Investigating the Effectiveness and Performance of Supervised and Unsupervised Methods with Sentence Embeddings

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Filippo Caliò; [2023]
    Keywords : Keyword Clustering; Supervised Learning; Unsupervised Learning; Cluster Labels; Natural Language Processing; Sentence Embeddings; Nyckelord Klustring; övervakad inlärning; oövervakad inlärning; klustermärkning; naturlig språkbehandling; Inbäddning av meningar;

    Abstract : Clustering keywords is an important Natural Language Processing task that can be adopted by several businesses since it helps to organize and group related keywords together. By clustering keywords, businesses can better understand the topics their customers are interested in. READ MORE

  3. 7. Towards topology-aware Variational Auto-Encoders : from InvMap-VAE to Witness Simplicial VAE

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Aniss Aiman Medbouhi; [2022]
    Keywords : Variational Auto-Encoder; Nonlinear dimensionality reduction; Generative model; Inverse projection; Computational topology; Algorithmic topology; Topological Data Analysis; Data visualisation; Unsupervised representation learning; Topological machine learning; Betti number; Simplicial complex; Witness complex; Simplicial map; Simplicial regularization.; Variations autokodare; Ickelinjär dimensionalitetsreducering; Generativ modell; Invers projektion; Beräkningstopologi; Algoritmisk topologi; Topologisk Data Analys; Datavisualisering; Oövervakat representationsinlärning; Topologisk maskininlärning; Betti-nummer; Simplicielt komplex; Vittneskomplex; Simpliciel avbildning; Simpliciel regularisering.;

    Abstract : Variational Auto-Encoders (VAEs) are one of the most famous deep generative models. After showing that standard VAEs may not preserve the topology, that is the shape of the data, between the input and the latent space, we tried to modify them so that the topology is preserved. READ MORE

  4. 8. Data Collection and Layout Analysis on Visually Rich Documents using Multi-Modular Deep Learning.

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Mattias Stahre; [2022]
    Keywords : DeepLearning; Machine Learning; Dataset Collection; Annotation; Labeling; Transformer Network; Multi-Modal; Computer Vision; Natural Language Processing; Embedding; LayoutLMv2; DocBank; Djupinlärning; Maskininlärning; Datasamling; Annotering; Märkning; Transformernätverk; Multi-modulär; Datorsyn; Naturlig Språkbehandling; Inbäddning; LayoutLMv2; DocBank;

    Abstract : The use of Deep Learning methods for Document Understanding has been embraced by the research community in recent years. A requirement for Deep Learning methods and especially Transformer Networks, is access to large datasets. READ MORE

  5. 9. Attribute Embedding for Variational Auto-Encoders : Regularization derived from triplet loss

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Anton E. L. Dahlin; [2022]
    Keywords : Variational Auto-Encoder; Triplet Loss; Contrastive Loss; Generative Models; Metric Learning; Latent Space; Attribute Manipulation; Variationsautokodare; Triplettförlust; Kontrastiv Förlust; Generativa Modeller; Metrisk Inlärning; Latent Utrymme; Attributmanipulation;

    Abstract : Techniques for imposing a structure on the latent space of neural networks have seen much development in recent years. Clustering techniques used for classification have been used to great success, and with this work we hope to bridge the gap between contrastive losses and Generative models. READ MORE

  6. 10. An unsupervised method for Graph Representation Learning

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Yi Ren; [2022]
    Keywords : Graph Representation Learning; unsupervised learning; machine learning;

    Abstract : Internet services, such as online shopping and chat apps, have been spreading significantly in recent years, generating substantial amounts of data. These data are precious for machine learning and consist of connections between different entities, such as users and items. READ MORE