14 citations · 15 across the 3 of their papers we have counts for
3 papers
cs.LG2021
Predicting Disease Progress with Imprecise Lab Test Results
Mei Wang, Jianwen Su, Zhihua Lin
In existing deep learning methods, almost all loss functions assume that sample data values used to be predicted are the only correct ones. This assumption does not hold for labora…
cs.LG2020★ 1 cited
Impact of Medical Data Imprecision on Learning Results
Mei Wang, Jianwen Su, Haiqin Lu
Test data measured by medical instruments often carry imprecise ranges that include the true values. The latter are not obtainable in virtually all cases. Most learning algorithms,…
cs.DB2017★ 14 cited
Research Directions for Principles of Data Management (Dagstuhl Perspectives Workshop 16151)
Serge Abiteboul, Marcelo Arenas, Pablo Barceló +18
In April 2016, a community of researchers working in the area of Principles of Data Management (PDM) joined in a workshop at the Dagstuhl Castle in Germany. The workshop was organi…