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Deep learning is increasingly being embedded in safety-critical systems such as self driving cars. It is essential that such systems are able to reason about what they do know, and what they do not (or cannot) know. As such, I use Bayesian inference, the mathematical theory of uncertainty, to build safe deep learning systems and data-analysis pipelines. I have published this work in major machine learning conferences including NeurIPS and ICLR. My PhD work focused on how neural systems solve these problems: prey animals must constantly reason about the possibility of predators in the immediate environment, and respond accordingly.
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论文共 80 篇作者统计合作学者相似作者
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arxiv(2024)
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Bobby Antonio,Andrew McRae,Dave MacLeod, Fenwick Cooper,John Marsham,Laurence Aitchison,Tim Palmer,Peter Watson
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CoRR (2024)
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CoRR (2024)
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arxiv(2023)
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