1 code implementation • 28 Aug 2022 • Elena Sizikova, Joshua Vendrow, Xu Cao, Rachel Grotheer, Jamie Haddock, Lara Kassab, Alona Kryshchenko, Thomas Merkh, R. W. M. A. Madushani, Kenny Moise, Annie Ulichney, Huy V. Vo, Chuntian Wang, Megan Coffee, Kathryn Leonard, Deanna Needell
Automatic infectious disease classification from images can facilitate needed medical diagnoses.
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 • 3 Aug 2021 • Kathlén Kohn, Thomas Merkh, Guido Montúfar, Matthew Trager
We study the family of functions that are represented by a linear convolutional neural network (LCN).
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 • 22 Oct 2019 • Thomas Merkh, Guido Montúfar
We investigate different types of shallow and deep architectures, and the minimal number of layers and units per layer that are sufficient and necessary in order for the network to be able to approximate any given stochastic mapping from the set of inputs to the set of outputs arbitrarily well.