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Learn Biologically Meaningful Representation With Transfer Learning, Di He
Learn Biologically Meaningful Representation With Transfer Learning, Di He
Dissertations, Theses, and Capstone Projects
Machine learning has made significant contributions to bioinformatics and computational biology. In particular, supervised learning approaches have been widely used in solving problems such as biomarker identification, drug response prediction, and so on. However, because of the limited availability of comprehensively labeled and clean data, constructing predictive models in super vised settings is not always desirable or possible, especially when using datahunger, redhot learning paradigms such as deep learning methods. Hence, there are urgent needs to develop new approaches that could leverage more readily available unlabeled data in driving successful machine learning ap plications in this area.
In my dissertation, …
Applying Deep Learning On Financial Sentiment Analysis, Cuiyuan Wang
Applying Deep Learning On Financial Sentiment Analysis, Cuiyuan Wang
Dissertations, Theses, and Capstone Projects
Portfolio Investment has always been appealing to investors and researchers. In the past, people tend to use historical trading information of the securities to predict the return or manage the portfolio. Nowadays, the literature has been proved that the market sentiment could predict asset prices. Specifically, it has been shown that the stock market movement is related to financial news and social media events. Thus, it becomes necessary to extract the sentiment of the financial news. We explicitly introduce the application of dictionary methods, traditional machine learning models and deep learning models on text classification. The experiment results show that …
Speech Enhancement Using Speech Synthesis Techniques, Soumi Maiti
Speech Enhancement Using Speech Synthesis Techniques, Soumi Maiti
Dissertations, Theses, and Capstone Projects
Traditional speech enhancement systems reduce noise by modifying the noisy signal to make it more like a clean signal, which suffers from two problems: under-suppression of noise and over-suppression of speech. These problems create distortions in enhanced speech and hurt the quality of the enhanced signal. We propose to utilize speech synthesis techniques for a higher quality speech enhancement system. Synthesizing clean speech based on the noisy signal could produce outputs that are both noise-free and high quality. We first show that we can replace the noisy speech with its clean resynthesis from a previously recorded clean speech dictionary from …