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Found 3 papers, 1 papers with code
Multi-perspective Feedback-attention Coupling Model for Continuous-time Dynamic Graphs
Recently, representation learning over graph networks has gained popularity, with various models showing promising results.
DF2M: An Explainable Deep Bayesian Nonparametric Model for High-Dimensional Functional Time Series
In this paper, we present Deep Functional Factor Model (DF2M), a Bayesian nonparametric model for analyzing high-dimensional functional time series.
EEGNN: Edge Enhanced Graph Neural Network with a Bayesian Nonparametric Graph Model
We show that such simplifying can reduce the potential of message-passing layers to capture the structural information of graphs.
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