Sökning: "deep generative model"

Visar resultat 16 - 20 av 96 uppsatser innehållade orden deep generative model.

1. 16. Synthetic data generation for domain adaptation of a retriever-reader Question Answering system for the Telecom domain : Comparing dense embeddings with BM25 for Open Domain Question Answering

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

   Författare :Filip Döringer Kana; [2023]
   Nyckelord :Natural Language Processing; Transformers; Deep Learning; Question Answering; Data Generation; Språkteknologi; Transformers; Djupinlärning; Frågebesvaring; Datagenerering;

   Sammanfattning : Having computer systems capable of answering questions has been a goal within Natural Language Processing research for many years. Machine Learning systems have recently become increasingly proficient at this task with large language models obtaining state-of-the-art performance. LÄS MER

3. 17. Deep Generative Modeling : An Overview of Recent Advances in Likelihood-based Models and an Application to 3D Point Cloud Generation

   Master-uppsats, Umeå universitet/Institutionen för matematik och matematisk statistik

   Författare :Shams Methnani; [2023]
   Nyckelord :;

   Sammanfattning : Deep generative modeling refers to the process of constructing a model, parameterized by a deep neural network, that learns the underlying patterns and structures of the data generating process which produced the samples in a given dataset, in order to generate novel samples that resemble those in the original dataset. Deep generative models for 3D shape generation hold significant importance to various fields including robotics, medical imaging, manufacturing, computer animation and more. LÄS MER

4. 18. Scenario Generation for Stress Testing Using Generative Adversarial Networks : Deep Learning Approach to Generate Extreme but Plausible Scenarios

   Uppsats för yrkesexamina på avancerad nivå, Umeå universitet/Institutionen för matematik och matematisk statistik

   Författare :Jonas Gustafsson; Conrad Jonsson; [2023]
   Nyckelord :Machine Learning; Generative Adversarial Network GAN ; Wasserstein Generative Adversarial Network WGAN ; Scenario Generation; Stress Testing; Central Counterparty Clearing;

   Sammanfattning : Central Clearing Counterparties play a crucial role in financial markets, requiring robust risk management practices to ensure operational stability. A growing emphasis on risk analysis and stress testing from regulators has led to the need for sophisticated tools that can model extreme but plausible market scenarios. LÄS MER

5. 19. Using a Deep Generative Model to Generate and Manipulate 3D Object Representation

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

   Författare :Yu Hu; [2023]
   Nyckelord :Neural networks; point cloud; 3D shape generation; 3D shape manipulation; classification; Neurala nätverk; punktmoln; generering av 3D-former; manipulation av 3Dformer; klassificering;

   Sammanfattning : The increasing importance of 3D data in various domains, such as computer vision, robotics, medical analysis, augmented reality, and virtual reality, has gained giant research interest in generating 3D data using deep generative models. The challenging problem is how to build generative models to synthesize diverse and realistic 3D objects representations, while having controllability for manipulating the shape attributes of 3D objects. LÄS MER

7. 20. Stable diffusion for HRIR extrapolation : A novel approach with deep learning

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

   Författare :Axel Rooth; [2023]
   Nyckelord :Head-related filter; Generative AI; Diffusion; Huvud-relaterat filter; Generative AI; Diffusion;

   Sammanfattning : Humans perceive and interact with their environment through a multitude of sensory channels. Among these, hearing plays a pivotal role, enabling humans to effectively navigate their surroundings. LÄS MER