Essays about: "Tabelldata"

Showing result 1 - 5 of 6 essays containing the word Tabelldata.

1. 1. Variational AutoEncoders and Differential Privacy : balancing data synthesis and privacy constraints

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

   Author : Baptiste Bremond; [2024]
   Keywords : TVAE; Differential privacy; Tabular data; Synthetic data; DP-SGD; TVAE; differentiell integritet; tabelldata; syntetiska data; DP-SGD;

   Abstract : This thesis investigates the effectiveness of Tabular Variational Auto Encoders (TVAEs) in generating high-quality synthetic tabular data and assesses their compliance with differential privacy principles. The study shows that while TVAEs are better than VAEs at generating synthetic data that faithfully reproduces the distribution of real data as measured by the Synthetic Data Vault (SDV) metrics, the latter does not guarantee that the synthetic data is up to the task in practical industrial applications. READ MORE

3. 2. Investigating the Use of Deep Learning Models for Transactional Underwriting

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

   Author : Samuel Tober; [2022]
   Keywords : Tabular Data; Deep Learning; Explainable Machine Learning; Underwriting; Tabelldata; Djupinlärning; Förklaringsbar maskininlärning; Underwriting;

   Abstract : Tabular data is the most common form of data, and is abundant throughout crucial industries, such as banks, hospitals and insurance companies. Albeit, deep learning research has largely been dominated by applications to homogeneous data, e.g. images or natural language. READ MORE

4. 3. Synthetic Data Generation Using Transformer Networks

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

   Author : Pedro Campos; [2021]
   Keywords : Transformer; Synthetic Data; Text Generation; Deep Learning; Tabular Data; Transformator; Syntetisk data; Textgenerering; Djupinlärning; Tabelldata;

   Abstract : One of the areas propelled by the advancements in Deep Learning is Natural Language Processing. These continuous advancements allowed the emergence of new language models such as the Transformer [1], a deep learning model based on attention mechanisms that takes a sequence of symbols as input and outputs another sequence, attending to the input during its generation. READ MORE

5. 4. Flight search engine CPU consumption prediction

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

   Author : Zhaopeng Tao; [2021]
   Keywords : Flight Search Engine; Deep Neural Networks; Tabular Data; Regression; Machine Learning; Flight Schedule Embedding; Node Embedding; Graph Embedding; Line Graph Embedding; Sökmotor för flygresor; Djupa neurala nätverk; Tabulära data; Regression; Maskininlärning; Inbäddning av flygschema; Inbäddning av noder; Inbäddning av grafer; Inbäddning av linjediagram;

   Abstract : The flight search engine is a technology used in the air travel industry. It allows the traveler to search and book for the best flight options, such as the combination of flights while keeping the best services, options, and price. The computation for a flight search query can be very intensive given its parameters and complexity. READ MORE

6. 5. Synthetic Data Generation for the Financial Industry Using Generative Adversarial Networks

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

   Author : Mikael Ljung; [2021]
   Keywords : Deep Learning; Generative Models; GAN; CTGAN; Synthetic Data; Financial Industry; Djupinlärning; generativ modellering; GAN; CTGAN; Syntetisk Data; Finansindustrin;

   Abstract : Following the introduction of new laws and regulations to ensure data protection in GDPR and PIPEDA, interests in technologies to protect data privacy have increased. A promising research trajectory in this area is found in Generative Adversarial Networks (GAN), an architecture trained to produce data that reflects the statistical properties of its underlying dataset without compromising the integrity of the data subjects. READ MORE