Essays about: "VAE"
Showing result 11 - 15 of 45 essays containing the word VAE.
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11. Causal Reinforcement Learning for Bandits with Unobserved Confounders
University essay from Uppsala universitet/Institutionen för informationsteknologiAbstract : Reinforcement Learning (RL) has been recognized as a valuable tool in various fields. However, its application is limited by its reliance on extensive data through a trial-and-error approach and challenges in generalizing learned policies. READ MORE
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12. Credit Card Transaction Fraud Detection Using Neural Network Classifiers
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : With increasing usage of credit card payments, credit card fraud has also been increasing. Therefore a fast and accurate fraud detection system is vital for the banks. To solve the problem of fraud detection, different machine learning classifiers have been designed and trained on a credit card transaction dataset. READ MORE
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13. Tackling Non-Stationarity in Reinforcement Learning via Latent Representation : An application to Intraday Foreign Exchange Trading
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Reinforcement Learning has applications in various domains, but the typical assumption is of a stationary process. Hence, when this hypothesis does not hold, performance may be sub-optimal. READ MORE
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14. Insurance Fraud Detection using Unsupervised Sequential Anomaly Detection
University essay from Linköpings universitet/Institutionen för datavetenskapAbstract : Fraud is a common crime within the insurance industry, and insurance companies want to quickly identify fraudulent claimants as they often result in higher premiums for honest customers. Due to the digital transformation where the sheer volume and complexity of available data has grown, manual fraud detection is no longer suitable. READ MORE
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15. 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