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Skip to Search Results- 15Transfer Learning
- 6Machine Learning
- 6Reinforcement Learning
- 4Domain Adaptation
- 3Artificial Intelligence
- 2Deep Learning
- 2Zhang, Tianyu
- 1Alikhasi, Mahdi
- 1Dhankar, Abhishek
- 1Doosti Sanjani, Anahita
- 1Imani, Ehsan
- 1Krishna Guruvayur Sasikumar, Aakash
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Spring 2024
In reinforcement learning, agents solve problems through interactions with the environment. However, when faced with intricate environmental dynamics, learning can become challenging, resulting in sub-optimal policies. A potential remedy to this situation lies in the transfer of knowledge from...
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Fall 2024
Numerical simulation is extensively used in advanced analysis of structures under seismic loading. Even though computational power and solution algorithms have advanced over the years, response evaluation of complex structures using numerical methods can still be challenging due to high...
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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...