Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/154676
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dc.contributor.authorWang, Maosenen_US
dc.date.accessioned2022-01-03T08:17:12Z-
dc.date.available2022-01-03T08:17:12Z-
dc.date.issued2021-
dc.identifier.citationWang, M. (2021). A simpler and faster biological learning framework for increasing robustness. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/154676en_US
dc.identifier.urihttps://hdl.handle.net/10356/154676-
dc.description.abstractHebbian Learning has been proposed for many years. The advantage of Hebbian learning is more plausible compared with Backpropagation in the aspect of biological learning. In this work, a new Hebbian Learning Framework (HLF) is designed. From the experiment results, the proposed HLF is much simpler and faster than the state-of-the-art Hebbian learning method. In this case, it can promote the usage scenarios of Hebbian Learning. Robustness of learning algorithms remains an important problem to be solved from both the perspective of adversarial attacks and improving generalization. Another work of this dissertation is that we investigate the robustness of the proposed HLF in depth. We find that Hebbian learning based algorithms outperform conventional learning algorithms like CNNs by a huge margin of upto 18% on the CIFAR-10 dataset under the addition of adversarial noise. We highlight that an important reason for this is the underlying representations that are being learned by the learning algorithms.en_US
dc.language.isoenen_US
dc.publisherNanyang Technological Universityen_US
dc.subjectEngineering::Electrical and electronic engineeringen_US
dc.titleA simpler and faster biological learning framework for increasing robustnessen_US
dc.typeThesis-Master by Courseworken_US
dc.contributor.supervisorPonnuthurai Nagaratnam Suganthanen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeMaster of Science (Computer Control and Automation)en_US
dc.contributor.supervisoremailEPNSugan@ntu.edu.sgen_US
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