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Skip to Search Results- 83Machine Learning
- 19Artificial Intelligence
- 17Reinforcement Learning
- 9Natural Language Processing
- 8Deep Learning
- 5Computer Vision
- 2Jacobsen, Andrew
- 2Wen, Junfeng
- 1Aghaei, Nikoo
- 1Alam Anik, Md Tanvir
- 1Ashley, Dylan R
- 1Ashrafi Asli, Seyed Arad
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A Framework for Associating Mobile Devices to Individuals Based on Identification of Motion Events
DownloadFall 2020
The ubiquity of the Internet-of-Things (IoT) devices in everyday life allows various sensors to be utilized in networked systems for solving a number of real-world problems. Models utilizing specific sensing modalities achieve impressive performance in understanding human activity and are used in...
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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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Fall 2024
Video game development is a highly technical practice that traditionally requires programming skills. This serves as a barrier to entry for would-be developers or those hoping to use games as part of their creative expression. While there have been prior game development tools focused on...
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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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Fall 2024
Over the last decade, machine learning (ML) has lead to advances in many fields, such as computer vision, online decision-making, robotics, natural language processing, and many others. The algorithms driving these successes typically have one or more user-specified free variables called...
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Spring 2016
Monte Carlo methods are a simple, effective, and widely deployed way of approximating integrals that prove too challenging for deterministic approaches. This thesis presents a number of contributions to the field of adaptive Monte Carlo methods. That is, approaches that automatically adjust the...
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Spring 2024
In model-based reinforcement learning, an agent can improve its policy by planning: learning from experience generated by a model. Search control is the problem of determining which starting state should be used to generate this experience. Given a limited planning budget, an agent should be...
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Addressing the Challenges of Applying Machine Learning for Predicting Mental Disorders and Their Prognosis Using Two Case Studies
DownloadSpring 2019
Ghoreishiamiri, Seyedehreyhaneh
One of the principal applications of machine learning in psychiatry is to build automated tools that can help clinicians predict the diagnosis and prognosis of mental disorders using available data from patients’ profiles. Here, in two different studies, we investigate ways to use machine learn-...
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Spring 2023
Wheelchair-mounted robotic manipulators have the potential to help the elderly and individuals living with disabilities carry out their activities of daily living independently. While robotics researchers focus on assistive tasks from the perspective of various control schemes and motion types, ...
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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...