most citedDealing with zero-inflated data: achieving SOTA with a two-fold machine learning approach

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cs.LG2026

On solving symmetric multi-type orthogonal non-negative matrix tri-factorization problem

Rok Hribar, Gregor Papa, Janez Povh +1

We study the symmetric multi-type orthogonal non-negative matrix tri-factorization problem, where several symmetric non-negative matrices are simultaneously approximated by factors…

cs.LG2023★ 1 cited

Dealing with zero-inflated data: achieving SOTA with a two-fold machine learning approach

Jože M. Rožanec, Gašper Petelin, João Costa +5

In many cases, a machine learning model must learn to correctly predict a few data points with particular values of interest in a broader range of data where many target values are…