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Skip to Search Results- 49Artificial Intelligence
- 34Deep Learning
- 33Machine Learning
- 19Planning
- 14Reinforcement Learning
- 11Heuristic Search
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Fall 2015
Brammadesam Manavalan, Yathirajan
Displaying believable emotional reactions in virtual characters is required in applications ranging from virtual-reality trainers to video games. Manual scripting is the most frequently used method and enables an arbitrarily high fidelity of the emotions displayed. However, scripting is labor...
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Spring 2022
Data augmentation is a strong tool for enhancing the performance of deep learning models using different techniques to increase both the quantity and diversity of training data. Cutout was previously proposed, in the context of image classification, as a simple regularization technique that...
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Spring 2021
Learning about many things can provide numerous benefits to a reinforcement learning system. For example, learning many auxiliary value functions, in addition to optimizing the environmental reward, appears to improve both exploration and representation learning. The question we tackle in this...
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Advancing Forest Health Monitoring: Harnessing the Power of Deep Learning Computer Vision for Remote Sensing Applications
DownloadFall 2023
Forests provide immense economic, ecological, and societal values, making forest health monitoring (FHM) a crucial task for guiding conservation and management of these essential ecosystems. Drones have seen increased popularity in this domain due to their ability to collect high-resolution,...
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Fall 2022
In this thesis, we investigate the empirical performance of several experience replay techniques. Efficient experience replay plays an important role in model-free reinforcement learning by improving sample efficiency through reusing past experience. However, replay-based methods were largely...
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Spring 2020
Reinforcement Learning is a formalism for learning by trial and error. Unfortunately, trial and error can take a long time to find a solution if the agent does not efficiently explore the behaviours available to it. Moreover, how an agent ought to explore depends on the task that the agent is...
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An Empirical Study on Learning and Improving the Search Objective for Unsupervised Paraphrasing
DownloadSpring 2022
Research in unsupervised text generation has been gaining attention over the years. One recent approach is local search towards a heuristically defined objective, which specifies language fluency, semantic meanings, and other task-specific attributes. Search in the sentence space is realized by...
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Spring 2021
Deep learning has revolutionized many fields that process large amounts of data such as images, video, audio, speech, and text. Anomaly detection, however, is among the areas that still require major advancements. Based on the key traits of deep learning, which are the need for very little hand...
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Fall 2022
With the rapid development of smart grids, the detection of anomalies is essential to improve the quality and security protection of the grid. The identification of anomalies not only saves valuable time but also reduces maintenance costs. Due to the increasing deployment of distributed energy...
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Application of Artificial Intelligence in Hip Ultrasound and its Performance in Detecting Developmental Dysplasia of the Hip
DownloadSpring 2022
Developmental Dysplasia of Hip (DDH) which represents a wide range of abnormalities from acetabular dysplasia to fixed dislocation, is mainly defined by a loss of conformity between the femoral head and the acetabulum and it can lead to structural instability and osteoarthritis. The diagnosis of...