In this study, we propose a head-to-head type (H2H-type) inter-personal
multimodal Dirichlet mixture (Inter-MDM) by modifying the original Inter-MDM,
which is a probabilistic generative model that represents the symbol emergence
between two agents as multiagent multimodal categorization. A
Metropolis--Hastings method-based naming game based on the Inter-MDM enables
two agents to collaboratively perform multimodal categorization and share signs
with a solid mathematical foundation of convergence. However, the conventional
Inter-MDM presumes a tail-to-tail connection across a latent word variable,
causing inflexibility of the further extension of Inter-MDM for modeling a more
complex symbol emergence. Therefore, we propose herein a head-to-head type
(H2H-type) Inter-MDM that treats a latent word variable as a child node of an
internal variable of each agent in the same way as many prior studies of
multimodal categorization. On the basis of the H2H-type Inter-MDM, we propose a
naming game in the same way as the conventional Inter-MDM. The experimental
results show that the H2H-type Inter-MDM yields almost the same performance as
the conventional Inter-MDM from the viewpoint of multimodal categorization and
sign sharing.

本研究提出了一种头对头类型的跨人员多模式 Dirichlet 混合模型，用于符号出现和多模式分类，该模型基于 Metropolis-Hastings 方法的命名游戏使两个代理协作进行多模式分类和分享符号。

使用头对头潜在词进行人际分类的符号出现

Symbol Emergence as Inter-personal Categorization with Head-to-head Latent Word

We present a novel Metropolis-Hastings method for large datasets that uses
small expected-size minibatches of data. Previous work on reducing the cost of
Metropolis-Hastings tests yield variable data consumed per sample, with only
constant factor reductions versus using the full dataset for each sample. Here
we present a method that can be tuned to provide arbitrarily small batch sizes,
by adjusting either proposal step size or temperature. Our test uses the
noise-tolerant Barker acceptance test with a novel additive correction
variable. The resulting test has similar cost to a normal SGD update. Our
experiments demonstrate several order-of-magnitude speedups over previous work.

本文提出了一种新颖的 Metropolis-Hastings 方法，用于大型数据集，使用期望大小较小的数据小批量，其测试成本类似于普通 SGD 更新，并且可以比之前的方法实现多个量级的加速。