This decommissioned ERA site remains active temporarily to support our final migration steps to https://ualberta.scholaris.ca, ERA's new home. All new collections and items, including Spring 2025 theses, are at that site. For assistance, please contact erahelp@ualberta.ca.
Search
Skip to Search Results- 4Continual Learning
- 3Machine Learning
- 2Lifelong Learning
- 1Artificial Intelligence
- 1Artificial Neural Networks
- 1Catastrophic Forgetting
-
Fall 2024
Operated under changing wind speed and harsh environment conditions, the rotating parts in wind turbine gearboxes, such as gears and bearings, will deteriorate and become faulty over time. By conducting real-time and accurate fault detection and diagnosis before significant failures occur, we can...
-
Spring 2023
Gradient Descent algorithms suffer many problems when learning representations using fixed neural network architectures, such as reduced plasticity on non-stationary continual tasks and difficulty training sparse architectures from scratch. A common workaround is continuously adapting the neural...
-
Fall 2024
If we aspire to design algorithms that can run for long periods, continually adapting to new, unexpected situations, then we must be willing to deploy our agents without tuning their hyperparameters over the agent’s entire lifetime. The standard practice in deep RL—and even continual RL—is to...
-
Fall 2020
This thesis is offered as a step forward in our understanding of forgetting in artificial neural networks. ANNs are a learning system loosely based on our understanding of the brain and are responsible for recent breakthroughs in artificial intelligence. However, they have been reported to be...