13 citations · 17 across the 16 of their papers we have counts for
6 papers · 1 filter
Hierarchical-Graph-Structured Edge Partition Models for Learning Evolving Community Structure
Xincan Yu, Sikun Yang
We propose a novel dynamic network model to capture evolving latent communities within temporal networks. To achieve this, we decompose each observed dynamic edge between vertices…
On Your Mark, Get Set, Predict! Modeling Continuous-Time Dynamics of Cascades for Information Popularity Prediction
Xin Jing, Yichen Jing, Yuhuan Lu +3
Information popularity prediction is important yet challenging in various domains, including viral marketing and news recommendations. The key to accurately predicting information…
Adaptive Gradient Regularization: A Faster and Generalizable Optimization Technique for Deep Neural Networks
Huixiu Jiang, Ling Yang, Yu Bao +2
Stochastic optimization plays a crucial role in the advancement of deep learning technologies. Over the decades, significant effort has been dedicated to improving the training eff…
Scaling up Dynamic Edge Partition Models via Stochastic Gradient MCMC
Sikun Yang, Heinz Koeppl
The edge partition model (EPM) is a generative model for extracting an overlapping community structure from static graph-structured data. In the EPM, the gamma process (GaP) prior…
Negative-Binomial Randomized Gamma Markov Processes for Heterogeneous Overdispersed Count Time Series
Rui Huang, Sikun Yang, Heinz Koeppl
Modeling count-valued time series has been receiving increasing attention since count time series naturally arise in physical and social domains. Poisson gamma dynamical systems (P…
A Poisson-Gamma Dynamic Factor Model with Time-Varying Transition Dynamics
Jiahao Wang, Sikun Yang, Heinz Koeppl +3
Probabilistic approaches for handling count-valued time sequences have attracted amounts of research attentions because their ability to infer explainable latent structures and to…