Comments (3)
Thanks for your interest in our work!
I think our current model architecture can not adapt to a varying number of sensors at different time steps, but I think it is an interesting problem, and you could try to modify based on the current model or propose a new one.
For me, I think one reading resource is the papers included in https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#models. The code implementation insides can also be helpful for the understanding or the practical implementation. But I would say that is just my personal recommendation, as there are so many other helpful GNN related materials :D.
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Thank you for your quick response! The problem area I am working in is anomaly detection on highways, conditioned on vehicle trajectories on the road. Here, the number of vehicles near the human-controlled vehicle may change from time-to-time when cars move in and out of our sensor range. If interested, I'll be sure to share my work in anomaly detection with varying sensor numbers after I submit, if it would help with your work too :)
Also, thanks for the resource; this will help me out a lot!
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Sure, thanks :)
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Related Issues (20)
- No Results HOT 2
- how to implement models
- Question about MSL and SMAP dataset HOT 2
- Real time detection issue HOT 4
- about experiments hyperparameters HOT 1
- how to get SWaT dataset?
- Cuda issues even with only cpu
- CUDA is not available HOT 3
- Related issues in the result output section HOT 2
- current time tick? HOT 2
- multi-step prediction with iteration HOT 3
- question of fully connected graph HOT 1
- Weights initialization HOT 3
- Inquiry Regarding Suitability for Anomaly Detection HOT 2
- remove some of the 127 features from wadi dataset HOT 2
- some question about the auc score HOT 2
- RuntimeError: The expanded size of the tensor (1) must match the existing size (69120) at non-singleton dimension 1. Target sizes: [69120, 1, 64]. Tensor sizes: [1, 69120, 1] HOT 4
- GNN layers & topk relation HOT 2
- CUDA version and Torch Version HOT 1
- Figure 2: t-SNE plot HOT 2
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