You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Before doing more flexible learning, I'd like to learn hyperbolic embeddings for leaves of an existing tree (a tree from image region merging agglomerative clustering procedure). Such embeddings that when applying the tree decoding algorithm from HypHC, it would give back my original agglomerative clustering tree .
Can I do it within the framework / losses of HypHC?
I thought of using the tree shortest path for the similarity matrix.
The tricky part is that the similarity matrix obtained this way is extremely sparse, so randomly sampled triplets almost always have 0 similarities, so the model learns nothing.
Would you have any advices?
Thank you :)
The text was updated successfully, but these errors were encountered:
vadimkantorov
changed the title
How to compute hyperbolic embeddings for leaf of an existing tree
How to compute hyperbolic embeddings for leafs of an existing tree
Aug 6, 2023
Hi!
Before doing more flexible learning, I'd like to learn hyperbolic embeddings for leaves of an existing tree (a tree from image region merging agglomerative clustering procedure). Such embeddings that when applying the tree decoding algorithm from HypHC, it would give back my original agglomerative clustering tree .
Can I do it within the framework / losses of HypHC?
I thought of using the tree shortest path for the similarity matrix.
The tricky part is that the similarity matrix obtained this way is extremely sparse, so randomly sampled triplets almost always have 0 similarities, so the model learns nothing.
Would you have any advices?
Thank you :)
The text was updated successfully, but these errors were encountered: