Sökning: "Restricted Boltzmann Machine"

Visar resultat 1 - 5 av 11 uppsatser innehållade orden Restricted Boltzmann Machine.

  1. 1. Local Integrals of Motion from Neural Networks

    Master-uppsats, KTH/Fysik

    Författare :Hannes Karlsson; [2023]
    Nyckelord :many-body localization; integrals of motion; neural networks; machine learning; flerkroppslokalisering; rörelseintegraler; neurala nätverk; maskininlärning;

    Sammanfattning : Neural network quantum states (NNQS) is a novel machine learning method, based on restricted Boltzmann machines, previously used to represent the wave function in many-body quantum mechanics. In this thesis, we use NNQS to instead find integrals of motion, i.e., operators, commuting with the Hamiltonian, describing a system. LÄS MER

  3. 2. Group Invariant Convolutional Boltzmann Machines

    Master-uppsats, Göteborgs universitet/Institutionen för matematiska vetenskaper

    Författare :Maria Lindström; [2020-12-04]
    Nyckelord :Convolutional Boltzmann Machines; Convolutional neural networks; artificial neural networks; machine learning; group invariance; group equivariance;

    Sammanfattning : We investigate group invariance in unsupervised learning in the context of certain generative networks based on Boltzmann machines. Specifically, we introduce a generalization of restricted Boltzmann machines which is adapted to input data that is acted upon by any compact group G. LÄS MER

  4. 3. Restricted Boltzmann Machine as Recommendation Model for Venture Capital

    Master-uppsats, KTH/Matematisk statistik

    Författare :Gustav Fredriksson; Anton Hellström; [2019]
    Nyckelord :Machine learning; statistics; applied mathematics; venture capital; recommendation models; Maskininlärning; statistik; tillämpad matematik; riskkapital; rekommendationsmodeller;

    Sammanfattning : Denna studie introducerar restricted Boltzmann machines (RBMs) som rekommendationsmodell i kontexten av riskkapital. Ett nätverk av relationer används som proxy för att modellera investerares bolagspreferenser. LÄS MER

  5. 4. Unsupervised real-time anomaly detection on streaming data for large-scale application deployments

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Carl Jernbäcker; [2019]
    Nyckelord :;

    Sammanfattning : Anomaly detection is the classification of data points that do not adhere to the familiar pattern; in previous studies there existed a need for extensive human interactions with either labelling or sorting normal and abnormal data from one another. In this thesis, we want to go one step further and apply machine learning techniques on time-series data in order to have a deeper understanding of the properties of a given data point without any sorting and labelling. LÄS MER

  7. 5. Recommender Systems for Movies Using a Class of Neural Networks

    Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Sabeen Nawaz; Sophie Remstam; [2018]
    Nyckelord :;

    Sammanfattning : In this project a recommendation system for suggesting movies is implemented, in the field of Collaborative Filtering (CF). The system is created with a Restricted Boltzmann Machine (RBM), which is a two-layer neural network. The main tool used for programming the RBM is the TensorFlow library, imported to Python. LÄS MER