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Mathematical Challenges in Deep Learning

  • arXiv (Cornell University)
  • Cornell University
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Abstract

Deep models are dominating the artificial intelligence (AI) industry since the ImageNet challenge in 2012. The size of deep models is increasing ever since, which brings new challenges to this field with applications in cell phones, personal computers, autonomous cars, and wireless base stations. Here we list a set of problems, ranging from training, inference, generalization bound, and optimization with some formalism to communicate these challenges with mathematicians, statisticians, and theoretical computer scientists. This is a subjective view of the research questions in deep learning that benefits the tech industry in long run.

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Publication details

DOI
10.48550/arxiv.2303.15464
OpenAlex
W4361230453
Document type
preprint
Language
EN
Source
arXiv (Cornell University)
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