DIME: An Information-Theoretic Difficulty Measure for AI Datasets

By Peiliang Zhang, Huan Wang, Nikhil Vijay Naik, Caiming Xiong, Richard Socher. 2019

13 citations recorded by the source corpus.

Citations in the corpus

• Understanding Dataset Difficulty with V-Usable Information (2021, neutral): “As a notable exception, Zhang et al. (2020) proposed DIME, an information-theoretic measure to estimate a lower bound on the lowest possible (i.e., model-agnostic) 0-1 error.”