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Xu, J. (author)
Deep Neural Networks (DNNs) have found extensive applications across diverse fields, such as image classification, speech recognition, and natural language processing. However, their susceptibility to various adversarial attacks, notably the backdoor attack, has repeatedly been demonstrated in recent years. <br/>The backdoor attack aims to...
doctoral thesis 2025
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Suau, M. (author)
Reinforcement learning techniques have demonstrated great promise in tackling sequential decision-making problems. However, the inherent complexity of real-world scenarios presents significant challenges for its application. This thesis takes a fresh approach that explores the untapped potential of factored state representations as a means to...
doctoral thesis 2024
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Ponnambalam, C.T. (author)
Reinforcement learning (RL) models the learning process of humans, but as exciting advances are made that use increasingly deep neural networks, some of the fundamental strengths of human learning are still underutilized by RL agents. One of the most exciting properties of RL is that it appears to be incredibly flexible, requiring no model or...
doctoral thesis 2023