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Deep Learning For Portfolio Optimization With Delta Controlled
(2022)
Kandidaatintyö
Kandidaatintyö
The cryptocurrency market is considered high-risk compared to traditional investment channels such as stocks or bonds. The whole market trend is primarily affected by only a few top-of-the-market capitalization cryptocurrencies ...
Harnessing neural networks for predicting next actions of investors
(2023)
Kandidaatintyö
Kandidaatintyö
The goal of this study is to find out whether machine learning (ML) can be harnessed to predict next actions of active household investors. More precisely to compare the performance of three machine learning model structures. ...
Automatic Text Generation Using Supervised Pre-training and Reinforcement Learning Based Adaptation
(2021)
Kandidaatintyö
Kandidaatintyö
Text generation tasks are becoming more and more prominent in applications such as machine translation, image captioning, dialogue systems, etc. While text generation systems often require an extremely large amount of data, ...
On The Evaluation of Neural Network Deployment Options
(2023)
Kandidaatintyö
Kandidaatintyö
It is known that machine learning inference models are already transforming computing. However, it still requires massive power usage for training multiple gigantic datasets. Furthermore, inferences can be carried out in ...
Learning privacy-preserving representation of audio data with adversarial learning: The usage of adversarial learning to address privacy problems in smart audio processing devices
(2023)
Kandidaatintyö
Kandidaatintyö
Recently, the development of IoT leads to numerous automated machine listening systems be ing introduced. In the audio signals processed by these systems, human voice also exists, which poses a threat of leakage of privacy ...
Representation Learning for candlesticks time-series data: A contrastive learning approach
(2023)
Kandidaatintyö
Kandidaatintyö
In time series analysis, an important question is to distinguish between highly different samples. This question is even more crucial in the financial market, where time series data usually possess stochastic characteristics ...