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职业迁徙
个人简介
My research seeks to combine ideas from cognitive neuroscience and machine learning to develop new theories of how brain learns and develop improvements to machine learning models, especially artificial neural networks (ANNs). ANNs trained with the standard backpropagation algorithm struggle to learn well in online, continual, lifelong learning scenarios and are very energy inefficient. Animal brains, on the other hand, learn incredibly well in these scenarios and expend very little metabolic energy. Neuroscience may therefore provide ideas that could improve online, continual learning in ANNs and make their training more energy efficient. Furthermore, the brain was engineered via evolution to solve machine learning problems. This suggests that machine learning models engineered by people to solve similar problems may provide new ideas for neuroscience on how the brain may learn and store memories.
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论文共 5 篇作者统计合作学者相似作者
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AAAI 2024no. 10 (2024): 10812-10820
Nicholas Alonso, Jeffrey L Krichmar
Nature communicationsno. 1 (2024): 3722-3722
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D-Core
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