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Improving Local Identifiability in Probabilistic Box Embeddings by u2203157 is a document available to read on EtoBox.

What is Improving Local Identifiability in Probabilistic Box Embeddings about?

This document discusses improvements to local identifiability in probabilistic box embeddings, which represent objects as n-dimensional hyperrectangles for modeling complex relationships. The authors propose a new approach using min and max Gumbel distributions to enhance the learning process by addressing issues of gradient sparsity and nonidentifiability in parameter spaces. Experimental results demonstrate that the proposed Gumbel-box process significantly improves model performance on various tasks comp

Author

u2203157

Language

EN