论文标题
Neurosambolic博士，或：我如何学会停止担心和接受统计
Dr. Neurosymbolic, or: How I Learned to Stop Worrying and Accept Statistics
论文作者
论文摘要
象征性的AI社区越来越多地试图在神经符号结构中拥抱机器学习，但由于文化障碍，仍在挣扎。为了打破障碍，这份相当有思想的个人备忘录试图解释和纠正统计，机器学习和深入学习的惯例，从局外人的角度来看。它提供了一个分步协议，用于设计一个机器学习系统，该系统满足符号AI社区认真对待所必需的最低理论保证，即，它讨论了“在哪些条件下我们可以停止担心和接受统计机器学习。”与大多数专为专业研究Stat/ml/dl并愿意接受术语的学生写的教科书不同，该备忘录是为经验丰富的象征研究人员编写的，这些研究人员听到了很多嗡嗡声，但仍然不确定和持怀疑态度。有关STAT/ML/DL的信息目前太分散或嘈杂而无法投资。此备忘录优先考虑紧凑性，对旧论文的引用（许多在20世纪初）以及与象征性范式相关的概念，以便节省时间。它优先考虑一般数学建模，并且没有讨论任何特定的函数近似器，例如神经网络（NNS），SVM，决策树等。最后，它可以对校正开放。将此备忘录视为与博客文章相似的内容，以arxiv的论文形式。
The symbolic AI community is increasingly trying to embrace machine learning in neuro-symbolic architectures, yet is still struggling due to cultural barriers. To break the barrier, this rather opinionated personal memo attempts to explain and rectify the conventions in Statistics, Machine Learning, and Deep Learning from the viewpoint of outsiders. It provides a step-by-step protocol for designing a machine learning system that satisfies a minimum theoretical guarantee necessary for being taken seriously by the symbolic AI community, i.e., it discusses "in what condition we can stop worrying and accept statistical machine learning." Unlike most textbooks which are written for students trying to specialize in Stat/ML/DL and willing to accept jargons, this memo is written for experienced symbolic researchers that hear a lot of buzz but are still uncertain and skeptical. Information on Stat/ML/DL is currently too scattered or too noisy to invest in. This memo prioritizes compactness, citations to old papers (many in early 20th century), and concepts that resonate well with symbolic paradigms in order to offer time savings. It prioritizes general mathematical modeling and does not discuss any specific function approximator, such as neural networks (NNs), SVMs, decision trees, etc. Finally, it is open to corrections. Consider this memo as something similar to a blog post taking the form of a paper on Arxiv.
