Papers with Learning Representation
Domain Divergences: A Survey and Empirical Analysis (2021.naacl-main)
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| Challenge: | Existing literature on divergence measures is lacking in predicting performance of models in new domains. |
| Approach: | They propose a taxonomy of divergence measures consisting of three classes — Information-theoretic, Geometric, and Higher-order measures and identify the relationships between them. |
| Outcome: | The proposed measures are based on three novel use-cases and identify that they are prevalent in three domains and higher-order measures are more common in two. |