Essays about: "Generativa modeller"
Showing result 1 - 5 of 47 essays containing the words Generativa modeller.
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1. Generating Extreme Value Distributions in Finance using Generative Adversarial Networks
University essay from KTH/Matematik (Avd.)Abstract : This thesis aims to develop a new model for stress-testing financial portfolios using Extreme Value Theory (EVT) and General Adversarial Networks (GANs). The current practice of risk management relies on mathematical or historical models, such as Value-at-Risk and expected shortfall. READ MORE
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2. Keeping tabs on GPT-SWE : Classifying toxic output from generative language models for Swedish text generation
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Disclaimer: This paper contains content that can be perceived as offensive or upsetting. Considerable progress has been made in Artificial intelligence (AI) and Natural language processing (NLP) in the last years. READ MORE
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3. An empirical comparison of generative capabilities of GAN vs VAE
University essay from KTH/DatavetenskapAbstract : Generative models are a family of machine learning algorithms that are aspire to enable computers to understand the real world. Their capability to understand the underlying distribution of data enables them to generate synthetic data from the data they are trained on. READ MORE
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4. Distance preserving Fermat VAE
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Deep neural networks takes their strength in the representations, or features, that they internally build. While these internal encodings help networks performing classification or regression tasks on specific data types, it exists a branch of machine learning that has for only purpose to build these representations. READ MORE
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5. Structural Comparison of Data Representations Obtained from Deep Learning Models
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)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
