End-to-End Learning of Interactive Video Game Scripts with Deep Recurrent Neural Networks – We show that, based on a deep neural network (DNN) model, the Atari 2600-inspired video game Atari 2600 can be learnt from non-linear video clips. This study shows that Atari 2600 can produce a video that is non-linear in time compared to a video that contains any video clip. The learner then selects the shortest path to the next block of video to the Atari 2600. The Atari 2600-produced video contains the longest path to the next block of video and thus this process has been learnt to be non-linear.
This thesis explores the use of word embeddings in machine learning to help identify the user’s emotional states (e.g. excitement or sadness) from the text of text. We demonstrate that this technique provides a powerful tool for identifying the emotional state that is associated with human emotional states in both text and visual data. Moreover, we argue that it leads to a significant gap between emotion-related content and the emotional state of a human being. We show how the use of emotion-related text can aid the identification of users’ emotional states in a variety of machine learning tasks such as sentiment analysis and emotion recognition. In particular, we illustrate how text-based emotion-related feature learning with the state-of-the-art neural network improves the robustness to human emotion detection and classification, and provides a new approach for generating emotions. We provide a comprehensive review of all previous work that has used emotion-related feature learning in emotion recognition.
A new type of syntactic constant applied to language structures
Design of Novel Inter-rater Agreement and Boundary Detection Using Nonmonotonic Constraints
End-to-End Learning of Interactive Video Game Scripts with Deep Recurrent Neural Networks
Learning User Preferences for Automated Question AnsweringThis thesis explores the use of word embeddings in machine learning to help identify the user’s emotional states (e.g. excitement or sadness) from the text of text. We demonstrate that this technique provides a powerful tool for identifying the emotional state that is associated with human emotional states in both text and visual data. Moreover, we argue that it leads to a significant gap between emotion-related content and the emotional state of a human being. We show how the use of emotion-related text can aid the identification of users’ emotional states in a variety of machine learning tasks such as sentiment analysis and emotion recognition. In particular, we illustrate how text-based emotion-related feature learning with the state-of-the-art neural network improves the robustness to human emotion detection and classification, and provides a new approach for generating emotions. We provide a comprehensive review of all previous work that has used emotion-related feature learning in emotion recognition.
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