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Skip to Search Results- 10Transfer Learning
- 4Machine Learning
- 4Reinforcement Learning
- 3Artificial Intelligence
- 2Deep Reinforcement Learning
- 2Domain Adaptation
- 1Dhankar, Abhishek
- 1Doosti Sanjani, Anahita
- 1Krishna Guruvayur Sasikumar, Aakash
- 1Miahi, Erfan
- 1Sahir
- 1Shiva Soleimany Dizicheh
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Fall 2019
Occupancy and thermal modelling is the foundation of several smart building applications, such as intelligent control of residential and commercial buildings' Heating, Ventilation and Air Conditioning (HVAC) system to improve energy efficiency and overall occupant experience in the built...
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Spring 2022
Reinforcement learning (RL) offers agents a framework for learning to perform hard-to-engineer behaviors that other machine learning (ML) approaches cannot due to the complex nature of these problems. However, it is impractical to learn a complex task from scratch due to reasons such as the huge...
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Feature Generalization in Deep Reinforcement Learning: An Investigation into Representation Properties
DownloadFall 2022
In this thesis, we investigate the connection between the properties and the generalization performance of representations learned by deep reinforcement learning algorithms. Much of the earlier work on representation learning for reinforcement learning focused on designing fixed-basis...
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Fall 2023
Deep learning has had much success on challenging problems with large datasets. However, it struggles in cases with limited training data. Transfer learning represents a class of approaches for transferring knowledge from large source datasets to smaller target datasets. But many transfer...
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Spring 2022
Reinforcement learning (RL) has shown great success in solving many challenging tasks via the use of deep neural networks. Although the use of deep learning for RL brings immense representational power to the arsenal, it also causes sample inefficiency. This means that the algorithms are...
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Fall 2023
Deep learning approaches have had success in many domains recently, particularly in domains with large amounts of training data. However, there are domains without a sufficient quantity of training data, or where the training data present is of insufficient quality. Transfer learning approaches...
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Fall 2023
Krishna Guruvayur Sasikumar, Aakash
The application of reinforcement learning (RL) to the optimal control of building systems has gained traction in recent years as it can reduce building energy consumption and improve human comfort, without requiring the knowledge of the building model. However, existing RL solutions for building...
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Fall 2022
Medical Fake News is a pervasive part of the information that people consume on the internet. It may lead people to take actions which may put the lives of their family and community in danger - such actions include vaccine hesitancy, administering unverified and harmful treatments, etc. First...
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Fall 2023
The increasing popularity of Deep Neural Networks (DNN) has led to their application to many domains, including Music Generation. However, these large DNN-based models are heavily dependent on their training dataset, which means they perform poorly on musical genres that are out-of-distribution...
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Spring 2021
This dissertation demonstrates how to utilize data collected previously from different sources to facilitate learning and inference for a target task. Learning from scratch for a target task or environment can be expensive and time-consuming. To address this problem, we make three contributions...