序列模型的渐近行为

Flavio Chierichetti, Ravi Kumar, A. Tomkins
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引用次数: 2

摘要

本文研究了一类序列过程的极限动力学,该过程推广了Pólya定律。这一过程也在语言生成、离散选择、重复消费和网络图模型的背景下进行了研究。我们研究的过程是通过复制过去的项目来生成未来的项目。它是通过一系列权重来参数化的,这些权重描述了在多大程度上更喜欢从最近的位置复制而不是从更远的位置复制。我们证明,如果权重序列遵循指数α∈[0,1]的幂律,则模型生成的序列趋向于一种极限行为,即字母表中每个符号的最终频率达到一个极限。此外,在α > 2的情况下,我们证明了该序列收敛于一个令牌被无限次选择,而另一个令牌只被连续多次选择。
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Asymptotic Behavior of Sequence Models
In this paper we study the limiting dynamics of a sequential process that generalizes Pólya’s urn. This process has been studied also in the context of language generation, discrete choice, repeat consumption, and models for the web graph. The process we study generates future items by copying from past items. It is parameterized by a sequence of weights describing how much to prefer copying from recent versus more distant locations. We show that, if the weight sequence follows a power law with exponent α ∈ [0, 1), then the sequences generated by the model tend toward a limiting behavior in which the eventual frequency of each token in the alphabet attains a limit. Moreover, in the case α > 2, we show that the sequence converges to a token being chosen infinitely often, and each other token being chosen only constantly many times.
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