DF2M: An Explainable Deep Bayesian Nonparametric Model for High-Dimensional Functional Time Series

no code implementations • 23 May 2023 • Yirui Liu, Xinghao Qiao, Yulong Pei, Liying Wang

In this paper, we present Deep Functional Factor Model (DF2M), a Bayesian nonparametric model for analyzing high-dimensional functional time series.

Time Series Variational Inference

EEGNN: Edge Enhanced Graph Neural Network with a Bayesian Nonparametric Graph Model

1 code implementation • 12 Aug 2022 • Yirui Liu, Xinghao Qiao, Liying Wang, Jessica Lam

We show that such simplifying can reduce the potential of message-passing layers to capture the structural information of graphs.

CATVI: Conditional and Adaptively Truncated Variational Inference for Hierarchical Bayesian Nonparametric Models

1 code implementation • 13 Jan 2020 • Yirui Liu, Xinghao Qiao, Jessica Lam

Current variational inference methods for hierarchical Bayesian nonparametric models can neither characterize the correlation structure among latent variables due to the mean-field setting, nor infer the true posterior dimension because of the universal truncation.

Clustering Topic Models +1