Comments (1)
HI Xianglong,
Thanks for your interest in the paper. This part is for the approximation of the diffusion convolution using the Chebyshev polynomial basis which makes the gradient more stable. Similar results can be achieved even without the Cheyshev polynomial.
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Related Issues (20)
- About the graph of the paper
- About the graph of the paper
- Why are some speed data negative?
- scaler transform in load_dataset function Causing speed data negative
- sensors correlations or node interactions and how to interpret the model's output
- Input 'b' of 'SparseTensorDenseMatMul' Op has type float32 that does not match type float64 of argument 'a_values'. HOT 2
- Wrong sensor IDs for MetrLA? HOT 8
- Predictions near mean value
- 关于数据的输入问题
- train.py
- Sensor id and data series
- Result Charts - One Example Sensor or Mean of the entire dataset
- Tensorflow 2 for DCRNN models HOT 2
- reproduce results HOT 13
- nothing HOT 1
- A question about the code HOT 1
- Diffusion convolution is not found in code
- A question about changing predicting time interval
- How to train model use different dataset
- isolated nodes
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