1 citations · 2 across the 2 of their papers we have counts for
2 papers
cs.CL2023★ 1 cited
Improving the Inference of Topic Models via Infinite Latent State Replications
Daniel Rugeles, Zhen Hai, Juan Felipe Carmona +2
In text mining, topic models are a type of probabilistic generative models for inferring latent semantic topics from text corpus. One of the most popular inference approaches to to…
cs.LG2018★ 1 cited
Heron Inference for Bayesian Graphical Models
Daniel Rugeles, Zhen Hai, Gao Cong +1
Bayesian graphical models have been shown to be a powerful tool for discovering uncertainty and causal structure from real-world data in many application fields. Current inference…