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EXPERIMENTS IN TEXT SUMMARIZATION USING DEEP LEARNING
Title
EXPERIMENTS IN TEXT SUMMARIZATION USING DEEP LEARNING
Author
Bulusu, Sai Amrit
Date Created
2018
Subjects--Topical
Computer science, Artificial intelligence, Information science
Description
Deep Learning has been the go-to tool for text summarization in the recent times.Traditional deep learning research focuses on performing abstractive text summarizationwithout considering the user’s interests to personalize the summaries.This prob...
Deep Learning for Sentiment and Emotion Detection in Multilingual Contexts
Title
Deep Learning for Sentiment and Emotion Detection in Multilingual Contexts
Author
Abdullah, Malak
Date Created
2018
Subjects--Topical
Computer science
Description
Social media is growing as a communication medium where people can express online their feelings and opinions on a variety of topics in ways they rarely do in person. Detecting sentiments and emotions in texts have gained a considerable amount of ...
Deep Neural Language Generation with Emotional Intelligence and External Feedback
Title
Deep Neural Language Generation with Emotional Intelligence and External Feedback
Author
Srinivasan, Vidhushini
Date Created
2018
Subjects--Topical
Computer science
Description
Neural seq2seq models are widely used to generate dialogue using variations of RNN Encoder-Decoder architecture with maximum likelihood estimation as objective function. Advances in this architecture include introducing reinforcement learning to t...
Interactive Mining for Large-scale Neuro-Morphological Datasets
Title
Interactive Mining for Large-scale Neuro-Morphological Datasets
Author
Li, Zhongyu
Date Created
2018
Subjects--Topical
Computer science, Neurosciences, Biomedical engineering
Description
In this dissertation, we aim to investigate advanced methods for the computational analytics of large-scale neuro-morphological datasets, which can help neuroscientists interactively explore neurons in real-time. Particularly, we tackle the neuro-...
Investigating the causal link between the stock market and Twitter sentiments using causality models and deep learning methods
Title
Investigating the causal link between the stock market and Twitter sentiments using causality models and deep learning methods
Author
Seyeditabari, Narjessadat
Date Created
2018
Subjects--Topical
Computer science, Artificial intelligence
Description
An investment theory called the efficient market hypothesis (EMH) claims that it is impossible to outperform the market, and therefore, stocks always trade at a fair value. An important assumption of EMH is that all investors make decisions ration...
High Frequency GaN Power Converters Digital Twin
Title
High Frequency GaN Power Converters Digital Twin
Author
Biglarbegian, Mehrdad
Date Created
2018
Subjects--Topical
Electrical engineering
Description
There is a need for a foundation of a research study aimed at investigations on near real-time reliability awareness of Gallium Nitride devices in high-frequency power converters for which we need advanced hardware and algorithms. This dissertatio...