  Deep Learners (DLs) are the state-of-art predictive mechanism with
applications in many fields requiring complex high dimensional data processing.
Although conventional DLs get trained via gradient descent with
back-propagation, Kalman Filter (KF)-based techniques that do not need gradient
computation have been developed to approximate DLs. We propose a multi-arm
extension of a KF-based DL approximator that can mimic DL when the sample size
is too small to train a multi-arm DL. The proposed Matrix Ensemble Kalman
Filter-based multi-arm ANN (MEnKF-ANN) also performs explicit model stacking
that becomes relevant when the training sample has an unequal-size feature set.
Our proposed technique can approximate Long Short-term Memory (LSTM) Networks
and attach uncertainty to the predictions obtained from these LSTMs with
desirable coverage. We demonstrate how MEnKF-ANN can "adequately" approximate
an LSTM network trained to classify what carbohydrate substrates are digested
and utilized by a microbiome sample whose genomic sequences consist of
polysaccharide utilization loci (PULs) and their encoded genes.

Machine Learning;Machine Learning;Applications;Computation