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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...
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Fall 2022
Modern representation learning methods perform well on offline tasks and primarily revolve around batch updates. However, batch updates preclude those methods from focusing on new experience, which is essential for fast online adaptation. In this thesis, we study an online and incremental...