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.

翻译：深度学习器是当前最先进的预测机制，广泛应用于需要复杂高维数据处理的多领域。尽管传统深度学习器通过梯度下降与反向传播进行训练，但无需梯度计算的卡尔曼滤波近似技术已被开发用于逼近深度学习器。我们提出了一种基于卡尔曼滤波的深度学习近似器的多臂扩展，该扩展可在样本量过小无法训练多臂深度学习器时模拟其行为。所提出的基于矩阵集成卡尔曼滤波的多臂人工神经网络还执行显式模型堆叠，当训练样本具有不等尺寸特征集时该方法尤为重要。我们的技术能够近似长短期记忆网络，并为这些LSTM获得的预测附加具有理想覆盖率的置信度。我们展示了MEnKF-ANN如何"充分"近似一个训练用于分类微生物样本所消化利用的碳水化合物底物的LSTM网络，该样本的基因组序列包含多糖利用位点及其编码基因。