Therefore, heterogeneous data poses another challenge on deep learning models. Papa et al. Deep Boltzmann machines (DBM) (Srivastava and Salakhutdinov, 2014) and deep auto encoder (DAE) (Qiu and Cho, 2006a) are among some of the deep learning techniques used to carry out MMBD representation. Deep Belief networks are another type of Deep Learning Networks. A detailed comparison of different types of HDMs can be found in [84]. Besides directed HDMs, we can also construct undirected HDMs such as the, Fine-tuning restricted Boltzmann machines using quaternion-based flower pollination algorithm, Leandro Aparecido Passos, ... João Paulo Papa, in, Nature-Inspired Computation and Swarm Intelligence, Deep learning and its applications to machine health monitoring, Deterministic wind energy forecasting: A review of intelligent predictors and auxiliary methods, Multimedia big data computing and Internet of Things applications: A taxonomy and process model, Aparna Kumari, ... Kim-Kwang Raymond Choo, in, Journal of Network and Computer Applications. We apply K iterations of mean-field to obtain the mean-field parameters that will be used in the training update for DBM’s. Specially, they designed a tensor auto-encoder by extending the stacked auto-encoder model to the tensor space based on the tensor data representation. Amongst the machine learning subtopics on the rise, deep learning has obtained much recognition due to its capacity in solving several problems. Specifically, we can construct a deep regression BN [84] as shown in Fig. The first layer of the RBM is called … Maximum likelihood learning in DBMs, and other related models, is very difﬁcult because of the hard inference problem induced by the partition function Multiple layers of hidden units make learning in DBM’s far more difﬁcult [13]. Deep Learning algorithms are known for their capability to learn features more accurately than other machine learning algorithms, and are considered to be promising approaches for solving data analytics tasks with high degrees of accuracy. With the inclusion of the additional label layer, the energy of the state (v,h(1),h(2),o) in the discriminative DBM is given by, where U and o∈{0,1}C denote a connectivity between the top hidden layer and the label layer and a class-label indicator vector, respectively, C is the number of classes, and Θ={W(1),W(2),U}. (A) A conventional BN; (B) a hierarchical deep BN with multiple hidden layers. 12 shows the architecture of the multi-modal deep learning model. The key intuition for greedy layer wise training for DBM is that we double the input for the lower-level RBM and the top level RBM. Deep Boltzmann Machines (DBM) [computational graph] EM-like learning algorithm based on PCD and mean-field variational inference ; arbitrary number of layers of any types; initialize from greedy layer-wise pretrained RBMs (no random initialization for now); whether to sample or use probabilities for visible and hidden units; variable learning rate, momentum and number of … First, because of the two-way dependency in DBM, it is not tractable for the data-dependent statistics. It is observed from the DBM that time complexity constraints will occur when setting the parameters as optimal [4]. The main difference between DBN and DBM lies that DBM is fully undirected graphical model, while DBN is mixed directed/undirected one. Deep Boltzmann machine (DBM) can be regarded as a deep structured RMBs where hidden units are grouped into a hierarchy of layers instead of a single layer [28]. Finally, the joint representation is used as input of a logical regression layer or a deep learning model for the tasks of classification or recognition. Deep Boltzmann Machine consider hidden nodes in several layers, with a layer being units that have no direct connections. Thus, for the hidden layer l, its probability distribution is conditioned by its two neighboring layers l+1 and l−1. They are designed to learn high-level representations through low-level structures by means of non-linear conversions to accomplish a variety of tasks. The proposed model forecasted testing data through three convolutional layers, a fully connected layer and an SVM layer. (2017) employed the quaternion algebra to the FPA. They firstly trained a CNN model with two fully connected layers and three convolutional layers, and then utilized the output of the first fully connected layer to train the SVM model. 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