no code implementations • 28 Feb 2022 • Jamie Haddock, Lara Kassab, Sixian Li, Alona Kryshchenko, Rachel Grotheer, Elena Sizikova, Chuntian Wang, Thomas Merkh, RWMA Madushani, Miju Ahn, Deanna Needell, Kathryn Leonard
We propose new semi-supervised nonnegative matrix factorization (SSNMF) models for document classification and provide motivation for these models as maximum likelihood estimators.
no code implementations • 8 Apr 2021 • David Troxell, Miju Ahn, Harsha Gangammanavar
The model aims to minimize the operating cost of the system while simultaneously reducing the number of lines operating in emergency operating zones during contingency events.
1 code implementation • 15 Oct 2020 • Jamie Haddock, Lara Kassab, Sixian Li, Alona Kryshchenko, Rachel Grotheer, Elena Sizikova, Chuntian Wang, Thomas Merkh, R. W. M. A. Madushani, Miju Ahn, Deanna Needell, Kathryn Leonard
We propose several new models for semi-supervised nonnegative matrix factorization (SSNMF) and provide motivation for SSNMF models as maximum likelihood estimators given specific distributions of uncertainty.
Ranked #13 on Text Classification on 20NEWS
no code implementations • 2 Jan 2020 • Miju Ahn, Nicole Eikmeier, Jamie Haddock, Lara Kassab, Alona Kryshchenko, Kathryn Leonard, Deanna Needell, R. W. M. A. Madushani, Elena Sizikova, Chuntian Wang
There is currently an unprecedented demand for large-scale temporal data analysis due to the explosive growth of data.