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Source code for PairNorm (ICLR 2020)
I found there is another mode, scale-and-center, which hasn't been mentioned in the paper.
It looks a little bit weird to me that SCS mode first scales and then centers the representations, where the mean is computed based on the statistics before scaling. Could you explain the insight behind that?
Thank you for your great methodology!
Anyway, where is the code for actually optimizing equation (6)?
Thank you :)
I don’t use pairnorm, I use SGC to run 50 layers, and the effect can reach about 73%. Why is it a little bit different from the report in the paper?
Dear authors,
I'm confused with the split of CoauthorCS, "we randomly split all nodes into train/val/test as 3%/10%/87%".Dose it mean that we sample nodes consdering the label distribution, like the setting in cora, 20 nodes per class, or just totally randomly pick up the nodes in the whole set?I'm looking forward to your reply.It will help me a lot!
Hi Lingxiao,
Thank you for the great code. I am new to this area. So I would like to apologize first, considering that my questions might be trivial.
I wonder how to get the results shown in Table 2 of the paper. For example, for GCN-PN with 10 layers and 100% missing rate on Cora, I run the following command:
python main.py --data cora --model DeepGCN --nlayer 10 --missing_rate 100 --norm_mode PN-SI --residual 0
Instead of getting the acc of 0.731 shown in the paper, I obtained the following results:
Test set results: loss 1.084, acc 0.637.
I also found the same issue for other items in the table.
There might be something wrong in my experimental settings, and I would greatly appreciate it if you could help me. Thank you in advance.
Best,
Yongcheng
Hi,
Really nice work! After reading your paper, I have a question about the difference between Pairnorm and Batchnorm, especially under the inductive setting. Could you please provide some insights?
Thank you!
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