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20162022
most citedTCL: Transformer-based Dynamic Graph Modelling via Contrastive Learning

24 citations · 33 across the 8 of their papers we have counts for

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12 papers · 1 filter

cs.LG2022

Learning Temporal Rules from Noisy Timeseries Data

Karan Samel, Zelin Zhao, Binghong Chen +4

Events across a timeline are a common data representation, seen in different temporal modalities. Individual atomic events can occur in a certain temporal ordering to compose highe…

cs.LG2021

Efficient Learning and Decoding of the Continuous-Time Hidden Markov Model for Disease Progression Modeling

Yu-Ying Liu, Alexander Moreno, Maxwell A. Xu +7

The Continuous-Time Hidden Markov Model (CT-HMM) is an attractive approach to modeling disease progression due to its ability to describe noisy observations arriving irregularly in…

cs.LG2021★ 2 cited

Understanding the Spread of COVID-19 Epidemic: A Spatio-Temporal Point Process View

Shuang Li, Lu Wang, Xinyun Chen +2

Since the first coronavirus case was identified in the U.S. on Jan. 21, more than 1 million people in the U.S. have confirmed cases of COVID-19. This infectious respiratory disease…

cs.LG2021★ 24 cited

TCL: Transformer-based Dynamic Graph Modelling via Contrastive Learning

Lu Wang, Xiaofu Chang, Shuang Li +7

Dynamic graph modeling has recently attracted much attention due to its extensive applications in many real-world scenarios, such as recommendation systems, financial transactions,…

cs.LG2021★ 3 cited

Digital Beamforming Robust to Time-Varying Carrier Frequency Offset

Shuang Li, Payam Nayeri, Michael B. Wakin

Adaptive interference cancellation is rapidly becoming a necessity for our modern wireless communication systems, due to the proliferation of wireless devices that interfere with e…

cs.LG2020

Energy-Based Models for Continual Learning

Shuang Li, Yilun Du, Gido M. van de Ven +1

We motivate Energy-Based Models (EBMs) as a promising model class for continual learning problems. Instead of tackling continual learning via the use of external memory, growing mo…