Comments (12)
@mnick I am also curious about visualization methods for these embeddings. Is there anything special to take into account when visualizing embeddings from hyperbolic space?
Seeing your visualization code would of course be helpful, but I'm assuming there is a consideration that keeps you from posting that in this repo.
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same question here...
what technique was used for mapping embeddings from, say, 5-dimensional Poincare ball (mammals) to 2-dimensional Euclidian ring for visualization? I tried SVD and while latent hierarchies survive to some extent after dimensionality reduction, the resulting plot is not that sharp as the one in the paper
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On the second thought, we all seem to be looking in a dark room for a black cat that is not there.
Taking a closer look at the paper, it says explicitly
"Figure 2 shows a visualization of a two-dimensional Poincaré embedding."
And again, in the Figure 2 capture:
"Figure 2: Two-dimensional Poincaré embeddings of transitive closure of the WORDNET mammals
subtree... A Poincaré embedding with d = 5 achieves mean rank 1.26 and MAP 0.927 on this subtree" - apparently, to make it clear that the Figure 2 does not represent the metrics obtained with the proper number of dimensions of the Poincaré ball
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Yes, sorry for the late response, but @alex-bloom, you are correct. The figure is of an embedding that is trained in only 2 dimensions (no dimensionality reduction is done).
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Yes, sorry for the late response, but @alex-bloom, you are correct. The figure is of an embedding that is trained in only 2 dimensions (no dimensionality reduction is done).
Hi,
Can you tell me after how many epochs did you achieve the above mentioned metrics?
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Which metrics are you referring to?
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Which metrics are you referring to?
A Poincaré embedding with d = 5 achieves mean rank 1.26 and MAP 0.927.
These evaluation metrics is what I was referring to.
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Please create a separate issue. This issue is regarding the visualization provided in the README
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Just wondering if there was any update on sharing the methods or code that generated the picture in README!
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+1
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Same here!
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Same here, would like to know how to generate the picture please. Thanks!
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Related Issues (20)
- The distance function of euclidean manifold is wrong.
- Error when running NIPS 2017 Release HOT 2
- What is the output after learning? HOT 2
- [Question] Running Inference on New Nodes HOT 1
- euclidean embedding HOT 3
- [Question] Predicting the parent of an unseen word in an existing hierarchy HOT 4
- Restore from checkpoints and train?
- NIPS results not reproducible with this code.
- ValueError: Buffer dtype mismatch, expected 'long_t' but got 'long' HOT 1
- Why the Euclidean gradient omit the first part ? HOT 2
- stop when eval HOT 2
- How to get embeddings? HOT 1
- Hyperparameters for reproduction HOT 1
- mammal_closure.csv not found HOT 1
- Entailment cones compute the wrong angle? HOT 3
- What should we do after training? HOT 4
- Is there any filter file for plants and vehicles subtrees like 'mammals_filter.txt'? Could you please share these files?
- how was mammal_closure.csv created HOT 1
- Large dataset that needs continuous training HOT 1
- Problems related to constrain the embeddings to remain within the Poincaré ball via the projection
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