机器中的皮尔士：混合专家模型如何进行假设构造
Peirce in the Machine: How Mixture of Experts Models Perform Hypothesis Construction
AI总结：
本文探讨了混合专家模型在假设构造中的表现，指出其函数容量优势，并通过实验验证其在归纳推理中的有效性。
摘要：
混合专家是一种机器学习中的预测聚合方法，通过聚合专门专家的预测来进行预测。这种方法经常在贝叶斯方法之上表现更优，尽管贝叶斯方法具有更强的归纳保证。我们认为，这归因于混合专家更大的函数容量。我们证明，在混合专家的极限情况下，其容量将大于等效的贝叶斯方法，我们通过非极限情况的实验来证明这一点。最后，我们得出结论，混合专家是一种在皮尔士意义上的假设构造类型的归纳推理。
英文摘要：
Mixture of experts is a prediction aggregation method in machine learning that aggregates the predictions of specialized experts. This method often outperforms Bayesian methods despite the Bayesian having stronger inductive guarantees. We argue that this is due to the greater functional capacity of mixture of experts. We prove that in a limiting case of mixture of experts will have greater capacity than equivalent Bayesian methods, which we vouchsafe through experiments on non-limiting cases. Finally, we conclude that mixture of experts is a type of abductive reasoning in the Peircian sense of hypothesis construction.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
