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Found 4 papers, 3 papers with code
RGDAN: A random graph diffusion attention network for traffic prediction
RGDAN comprises a graph diffusion attention module and a temporal attention module.
Ranked #1 on
Traffic Prediction
on NE-BJ
Sepformer-based Models: More Efficient Models for Long Sequence Time-Series Forecasting
These include: 1) complexity - Informer has a relatively high computational complexity and a high memory overhead; 2) nuance - there is limited ability to capture the subtle features in a data stream; 3) interpretability - the inference procedure of Informer is not explainable; 4) extensibility - accuracy is poor with extra-long multivariate time series.
A robust feature reinforcement framework for heterogeneous graphs neural networks
Heterogeneous graph embedding is a crucial step in HGNNs.
CEKD:Cross Ensemble Knowledge Distillation for Augmented Fine-grained Data
We innovatively propose a cross distillation module to provide additional supervision to alleviate the noise problem, and propose a collaborative ensemble module to overcome the target conflict problem.
