Sökning: "generativa modeller"

Visar resultat 21 - 25 av 59 uppsatser innehållade orden generativa modeller.

1. 21. Basil-GAN

   Master-uppsats, KTH/Matematisk statistik

   Författare :Jonatan Risberg; [2022]
   Nyckelord :GAN; mathematical statistics; deep neural networks; generative models; latent space exploration; sequential data; GAN; matematisk statistik; djupa neurala nätverk; generativa modeller; utforskning av latenta rum; sekventiell data;

   Sammanfattning : Developments in computer vision has sought to design deep neural networks which trained on a large set of images are able to generate high quality artificial images which share semantic qualities with the original image set. A pivotal shift was made with the introduction of the generative adversarial network (GAN) by Goodfellow et al.. LÄS MER

3. 22. Evaluation of generative machine learning models : Judging the quality of generated data with the use of neural networks

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

   Författare :Sam Yousefzadegan Hedin; [2022]
   Nyckelord :Generative Modeling; MAUVE; Deep Learning; GPT-2; evaluation; Generativ modellering; MAUVE; Djupinlärning; GPT-2; evaluering;

   Sammanfattning : Generative machine learning models are capable of generating remarkably realistic samples. Some models generate images that look entirely natural, and others generate text that reads as if a human wrote it. However, judging the quality of these models is a major challenge. LÄS MER

4. 23. Generative Adversarial Networks for Vehicle Trajectory Generation

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

   Författare :Kristupas Bajarunas; [2022]
   Nyckelord :Data Generation; Generative Adversarial Networks; Vehicle Trajectories; Datagenerering; Generativa Motståndarnätverk; Fordonsbanor;

   Sammanfattning : Deep learning models heavily rely on an abundance of data, and their performance is directly affected by data availability. In mobility pattern modeling, problems, such as next location prediction or flow prediction, are commonly solved using deep learning approaches. LÄS MER

5. 24. Synthetic Graph Generation at Scale : A novel framework for generating large graphs using clustering, generative models and node embeddings

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

   Författare :Johan Hammarstedt; [2022]
   Nyckelord :Data Anonymization; Graph Learning; Generative Graph Modeling; Graph Clustering; Node Embedding; Synthetic Data; Dataanonymisering; Grafinlärning; Generativa graf-modeller; Graf klustring; Länk prediktion; Nodinbäddning; Syntetisk data;

   Sammanfattning : The field of generative graph models has seen increased popularity during recent years as it allows us to model the underlying distribution of a network and thus recreate it. From allowing anonymization of sensitive information in social networks to data augmentation of rare diseases in the brain, the ability to generate synthetic data has multiple applications in various domains. LÄS MER

7. 25. Believable and Manipulable Facial Behaviour in a Robotic Platform using Normalizing Flows

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

   Författare :Kildo Alias; [2021]
   Nyckelord :Nonverbal Behaviour; Machine Learning; Generative Models; Normalizing Flows; Human-Robot Interaction; Icke-verbalt beteende; Maskininlärning; Generativa modeller; Normaliserande Flöden; Människa-robot interaktion.;

   Sammanfattning : Implicit communication is important in interaction because it plays a role in conveying the internal mental states of an individual. For example, emotional expressions that are shown through unintended facial gestures can communicate underlying affective states. LÄS MER