56 citations · 79 across the 5 of their papers we have counts for
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cs.LG2023★ 19 cited
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…
cs.LG2021
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…
cs.LG2018★ 56 cited
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…