Feb. 9, 2024, 5:43 a.m. | Viktor Nilsson Anirban Samaddar Sandeep Madireddy Pierre Nyquist

cs.LG updates on arXiv.org arxiv.org

Information theoretic quantities play a central role in machine learning. The recent surge in the complexity of data and models has increased the demand for accurate estimation of these quantities. However, as the dimension grows the estimation presents significant challenges, with existing methods struggling already in relatively low dimensions. To address this issue, in this work, we introduce $\texttt{REMEDI}$ for efficient and accurate estimation of differential entropy, a fundamental information theoretic quantity. The approach combines the minimization of the cross-entropy …

challenges complexity cs.lg data demand dimensions entropy information low machine machine learning role stat.ml

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