56 citations · 79 across the 5 of their papers we have counts for
5 papers
Do We Really Need Complicated Model Architectures For Temporal Networks?
Weilin Cong, Si Zhang, Jian Kang +5
Recurrent neural network (RNN) and self-attention mechanism (SAM) are the de facto methods to extract spatial-temporal information for temporal graph learning. Interestingly, we fo…
Frequency-aware SGD for Efficient Embedding Learning with Provable Benefits
Yan Li, Dhruv Choudhary, Xiaohan Wei +4
Embedding learning has found widespread applications in recommendation systems and natural language modeling, among other domains. To learn quality embeddings efficiently, adaptive…
Multivariate Spatiotemporal Hawkes Processes and Network Reconstruction
Baichuan Yuan, Hao Li, Andrea L. Bertozzi +2
There is often latent network structure in spatial and temporal data and the tools of network analysis can yield fascinating insights into such data. In this paper, we develop a no…
Using Machine Learning to Discern Eruption in Noisy Environments: A Case Study using CO2-driven Cold-Water Geyser in Chimayo, New Mexico
B. Yuan, Y. J. Tan, M. K. Mudunuru +8
We present an approach based on machine learning (ML) to distinguish eruption and precursory signals of Chimayó geyser (New Mexico, USA) under noisy environments. This geyser can b…
Graph-Based Deep Modeling and Real Time Forecasting of Sparse Spatio-Temporal Data
Bao Wang, Xiyang Luo, Fangbo Zhang +3
We present a generic framework for spatio-temporal (ST) data modeling, analysis, and forecasting, with a special focus on data that is sparse in both space and time. Our multi-scal…