Essays about: "Generative Models"

Showing result 1 - 5 of 115 essays containing the words Generative Models.

  1. 1. Fast Simulations of Radio Neutrino Detectors : Using Generative Adversarial Networks and Artificial Neural Networks

    University essay from Uppsala universitet/Högenergifysik

    Author : Anton Holmberg; [2022]
    Keywords : Askaryan emission; Radio detection of neutrinos; in-ice propagation; neutrino; Generative adversarial networks; GAN; WGAN; Neural networks; NN; surrogate model; IceCube; deep learning; generative model;

    Abstract : Neutrino astronomy is expanding into the ultra-high energy (>1017eV) frontier with the use of in-ice detection of Askaryan radio emission from neutrino-induced particle showers. There are already pilot arrays for validating the technology and the next few years will see the planning and construction of IceCube-Gen2, an upgrade to the current neutrino telescope IceCube. READ MORE

  2. 2. Virtual Staining of Blood Cells using Point Light Source Illumination and Deep Learning

    University essay from Lunds universitet/Matematik LTH

    Author : Joel Wulff; [2022]
    Keywords : Deep learning; virtual staining; blood; cells; GANs; Generative adversarial networks; CNNs; convolutional neural networks; ESRGAN; Unet; digital microscopy; Mathematics and Statistics;

    Abstract : Blood tests are an important part of modern medicine, and are essentially always stained using chemical colorization methods before analysis by computational or manual methods. The staining process allows different parts of blood cells to be discerned that would be unnoticeable in unstained blood. READ MORE

  3. 3. Structural Comparison of Data Representations Obtained from Deep Learning Models

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

    Author : Tommy Wallin; [2022]
    Keywords : Representation Learning; Deep learning models; Image Representations.; Representationsinlärning; Djupinlärningsmodeller; Bildrepresentationer;

    Abstract : In representation learning we are interested in how data is represented by different models. Representations from different models are often compared by training a new model on a downstream task using the representations and testing their performance. READ MORE

  4. 4. 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

  5. 5. Generating Geospatial Trip DataUsing Deep Neural Networks

    University essay from Linköpings universitet/Statistik och maskininlärning

    Author : Ahmed Alhasan; [2022]
    Keywords : Deep Learning; Machine Learning; Statistics; Generative Adversarial Networks; Computer Science; Generative Models;

    Abstract : Synthetic data provides a good alternative to real data when the latter is not sufficientor limited by privacy requirements. In spatio-temporal applications, generating syntheticdata is generally more complex due to the existence of both spatial and temporal dependencies. READ MORE