Figure 5 - uploaded by Cicero Nogueira Dos Santos
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Visualization of the word distributions of W. barycenter for different similarity matrices K based on GloVe (rows denote the distance of K from identity K − I F and corresponding ). Large entropic regularization generates K close to uninformative matrices of all 1's. This eventually leads to a barycenter which is close to a uniform distribution spreading the probability mass almost equally across all the words.
Source publication
In this paper we propose to perform model ensembling in a multiclass or a multilabel learning setting using Wasserstein (W.) barycenters. Optimal transport metrics, such as the Wasserstein distance, allow incorporating semantic side information such as word embeddings. Using W. barycenters to find the consensus between models allows us to balance c...
Contexts in source publication
Context 1
... eventually leads to a uniform distribution which spreads the probability mass equally across all the words. This can be also visualized with a histogram in Figure 5, where the histograms on the bottom represent distributions that are close to uniform, which can be considered as failure cases of W. barycenter, since the image captioner in this case can only generate meaningless, gibberish captions. ...
Context 2
... eventually leads to a uniform distribution which spreads the probability mass equally across all the words. This can be also visualized with a histogram in Figure 5, where the histograms on the bottom represent distributions that are close to uniform, which can be considered as failure cases of W. barycenter, since the image captioner in this case can only generate meaningless, gibberish captions. ...
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