A Matrix Ensemble Kalman Filter-based Multi-arm Neural Network to Adequately Approximate Deep Neural Networks

Autor:	Piyush, Ved, Yan, Yuchen, Zhou, Yuzhen, Yin, Yanbin, Ghosh, Souparno
Rok vydání:	2023
Předmět:	Statistics - Machine Learning Computer Science - Machine Learning Statistics - Applications Statistics - Computation
Druh dokumentu:	Working Paper
Popis:	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. Comment: 18 pages, 6 Figures, and 6 Tables
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2307.10436 Zobrazit plný text záznamu View this record from Arxiv