Comments (4)
The number of edges is a parameter that can be set to any number of edge types (of course not all numbers might work well). When we said "known" we didn't mean supervised manner, but that the number of edge types is known in advance as it is a simulation created by us.
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Thank you for the reply!
Now, I'm trying to reproduce the results, and generate a dataset of simulation that has 3 edge-types (weak spring, strong spring, no-interaction)
The number of data is 50000, and training, testing sequence number is 49, validation sequence number is 99, and skip-first is True, (same as 2 edge-types dataset) and I changed "edge-types" variable to 3.
When generating dataset, to create "edges" ground-truth, I set "no-relation" to 0, "weak spring" to 1, "strong spring" to 2.
When I trained NRI with 2 edge-types dataset (that I generate myself), It reaches to about 99% accuracy.
However, it reached only 40% accuracy when the number of edge-types are 3 with the settings described above.
I will try more, but could you give me some advice for better reproducing, if any?
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Thank you for your questions. I have not reproduced the results of this paper and I'm not very clear about the non-interaction edge type. Since there is no interaction between the non-interaction edge type should we directly ignore the non-interaction edge type in the decoder? E.g. If we use z_{ij}=[0,1] to denote interaction and z_{ij}=[1,0] to denote non-interaction, in the decoder should we write the edge embedding as: h^t_{ij} = z_{ij,0}fe([x^t_i, x^t_j]) ?
from nri.
Thank you for the reply!
Now, I'm trying to reproduce the results, and generate a dataset of simulation that has 3 edge-types (weak spring, strong spring, no-interaction)
The number of data is 50000, and training, testing sequence number is 49, validation sequence number is 99, and skip-first is True, (same as 2 edge-types dataset) and I changed "edge-types" variable to 3.
When generating dataset, to create "edges" ground-truth, I set "no-relation" to 0, "weak spring" to 1, "strong spring" to 2.When I trained NRI with 2 edge-types dataset (that I generate myself), It reaches to about 99% accuracy.
However, it reached only 40% accuracy when the number of edge-types are 3 with the settings described above.
I will try more, but could you give me some advice for better reproducing, if any?
I think in the paper they mean there are still 2 edge types(interaction or non-interaction) in the labels, but add an 3rd unknown(non-exist) edge in the encoder.
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Related Issues (20)
- Questions regarding the edges that are created in the latent space
- For the type of edge in the experimental setup. HOT 1
- Request for Kuramoto dataset HOT 5
- Error in running the simulation HOT 1
- my_softmax HOT 3
- Undirected latent graph
- Undirected latent graph
- How to reproduce some paper results
- Where can I find the code of Eq. 12 in the paper?? HOT 2
- Compatibility with Pytorch 1.xx ?
- Sport UV dataset HOT 4
- non-interaction edge type
- Error in class MLP def forward HOT 2
- About edge_accuracy() in utils.py HOT 2
- Unsupervised learning HOT 2
- what does mean edge_type? HOT 1
- what does mean logits shape?
- Results of charge experiment
- /
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