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restricted Boltzmann machine

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https://www.readbyqxmd.com/read/28726751/an-adaptive-feature-learning-model-for-sequential-radar-high-resolution-range-profile-recognition
#1
Xuan Peng, Xunzhang Gao, Yifan Zhang, Xiang Li
This paper proposes a new feature learning method for the recognition of radar high resolution range profile (HRRP) sequences. HRRPs from a period of continuous changing aspect angles are jointly modeled and discriminated by a single model named the discriminative infinite restricted Boltzmann machine (Dis-iRBM). Compared with the commonly used hidden Markov model (HMM)-based recognition method for HRRP sequences, which requires efficient preprocessing of the HRRP signal, the proposed method is an end-to-end method of which the input is the raw HRRP sequence, and the output is the label of the target...
July 20, 2017: Sensors
https://www.readbyqxmd.com/read/28646763/deep-neural-mapping-support-vector-machines
#2
Yujian Li, Ting Zhang
The choice of kernel has an important effect on the performance of a support vector machine (SVM). The effect could be reduced by NEUROSVM, an architecture using multilayer perceptron for feature extraction and SVM for classification. In binary classification, a general linear kernel NEUROSVM can be theoretically simplified as an input layer, many hidden layers, and an SVM output layer. As a feature extractor, the sub-network composed of the input and hidden layers is first trained together with a virtual ordinary output layer by backpropagation, then with the output of its last hidden layer taken as input of the SVM classifier for further training separately...
June 21, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28627811/energy-based-neural-networks-as-a-tool-for-harmony-based-virtual-screening
#3
Nelly I Zhokhova, Igor I Baskin
In Energy-Based Neural Networks (EBNNs), relationships between variables are captured by means of a scalar function conventionally called "energy". In this article, we introduce a procedure of "harmony search", which looks for compounds providing the lowest energies for the EBNNs trained on active compounds. It can be considered as a special kind of similarity search that takes into account regularities in the structures of active compounds. In this paper, we show that harmony search can be used for performing virtual screening...
June 19, 2017: Molecular Informatics
https://www.readbyqxmd.com/read/28618812/auditory-feature-representation-using-convolutional-restricted-boltzmann-machine-and-teager-energy-operator-for-speech-recognition
#4
Hardik B Sailor, Hemant A Patil
In this letter, authors propose an auditory feature representation technique with the filterbank learned using an annealing dropout convolutional restricted Boltzmann machine (ConvRBM) and noise-robust energy estimation using the Teager energy operator (TEO). TEO is applied on each subband of ConvRBM filterbank and pooled later to get the short-term spectral features. Experiments on AURORA 4 database show that the proposed features perform better than the Mel filterbank features. The relative improvement of 2...
June 2017: Journal of the Acoustical Society of America
https://www.readbyqxmd.com/read/28562219/determination-of-the-lowest-energy-states-for-the-model-distribution-of-trained-restricted-boltzmann-machines-using-a-1000-qubit-d-wave-2x-quantum-computer
#5
Yaroslav Koshka, Dilina Perera, Spencer Hall, M A Novotny
The possibility of using a quantum computer D-Wave 2X with more than 1000 qubits to determine the global minimum of the energy landscape of trained restricted Boltzmann machines is investigated. In order to overcome the problem of limited interconnectivity in the D-Wave architecture, the proposed RBM embedding combines multiple qubits to represent a particular RBM unit. The results for the lowest-energy (the ground state) and some of the higher-energy states found by the D-Wave 2X were compared with those of the classical simulated annealing (SA) algorithm...
July 2017: Neural Computation
https://www.readbyqxmd.com/read/28562217/deep-restricted-kernel-machines-using-conjugate-feature-duality
#6
Johan A K Suykens
The aim of this letter is to propose a theory of deep restricted kernel machines offering new foundations for deep learning with kernel machines. From the viewpoint of deep learning, it is partially related to restricted Boltzmann machines, which are characterized by visible and hidden units in a bipartite graph without hidden-to-hidden connections and deep learning extensions as deep belief networks and deep Boltzmann machines. From the viewpoint of kernel machines, it includes least squares support vector machines for classification and regression, kernel principal components analysis (PCA), matrix singular value decomposition, and Parzen-type models...
May 31, 2017: Neural Computation
https://www.readbyqxmd.com/read/28534790/neighborhood-based-stopping-criterion-for-contrastive-divergence
#7
Enrique Romero Merino, Ferran Mazzanti Castrillejo, Jordi Delgado Pin
Restricted Boltzmann Machines (RBMs) are general unsupervised learning devices to ascertain generative models of data distributions. RBMs are often trained using the Contrastive Divergence (CD) learning algorithm, an approximation to the gradient of the data log-likelihood (logL). A simple reconstruction error is often used as a stopping criterion for CD, although several authors have raised doubts concerning the feasibility of this procedure. In many cases, the evolution curve of the reconstruction error is monotonic, while the logL is not, thus indicating that the former is not a good estimator of the optimal stopping point for learning...
May 17, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28504950/graph-regularized-restricted-boltzmann-machine
#8
Dongdong Chen, Jiancheng Lv, Zhang Yi
The restricted Boltzmann machine (RBM) has received an increasing amount of interest in recent years. It determines good mapping weights that capture useful latent features in an unsupervised manner. The RBM and its generalizations have been successfully applied to a variety of image classification and speech recognition tasks. However, most of the existing RBM-based models disregard the preservation of the data manifold structure. In many real applications, the data generally reside on a low-dimensional manifold embedded in high-dimensional ambient space...
May 12, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28502383/rolling-bearing-fault-diagnosis-using-adaptive-deep-belief-network-with-dual-tree-complex-wavelet-packet
#9
Haidong Shao, Hongkai Jiang, Fuan Wang, Yanan Wang
Automatic and accurate identification of rolling bearing fault categories, especially for the fault severities and compound faults, is a challenge in rotating machinery fault diagnosis. For this purpose, a novel method called adaptive deep belief network (DBN) with dual-tree complex wavelet packet (DTCWPT) is developed in this paper. DTCWPT is used to preprocess the vibration signals to refine the fault characteristics information, and an original feature set is designed from each frequency-band signal of DTCWPT...
May 11, 2017: ISA Transactions
https://www.readbyqxmd.com/read/28409983/emergence-of-compositional-representations-in-restricted-boltzmann-machines
#10
J Tubiana, R Monasson
Extracting automatically the complex set of features composing real high-dimensional data is crucial for achieving high performance in machine-learning tasks. Restricted Boltzmann machines (RBM) are empirically known to be efficient for this purpose, and to be able to generate distributed and graded representations of the data. We characterize the structural conditions (sparsity of the weights, low effective temperature, nonlinearities in the activation functions of hidden units, and adaptation of fields maintaining the activity in the visible layer) allowing RBM to operate in such a compositional phase...
March 31, 2017: Physical Review Letters
https://www.readbyqxmd.com/read/28377709/the-role-of-architectural-and-learning-constraints-in-neural-network-models-a-case-study-on-visual-space-coding
#11
Alberto Testolin, Michele De Filippo De Grazia, Marco Zorzi
The recent "deep learning revolution" in artificial neural networks had strong impact and widespread deployment for engineering applications, but the use of deep learning for neurocomputational modeling has been so far limited. In this article we argue that unsupervised deep learning represents an important step forward for improving neurocomputational models of perception and cognition, because it emphasizes the role of generative learning as opposed to discriminative (supervised) learning. As a case study, we present a series of simulations investigating the emergence of neural coding of visual space for sensorimotor transformations...
2017: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/28297857/mean-field-message-passing-equations-in-the-hopfield-model-and-its-generalizations
#12
Marc M├ęzard
Motivated by recent progress in using restricted Boltzmann machines as preprocessing algorithms for deep neural network, we revisit the mean-field equations [belief-propagation and Thouless-Anderson Palmer (TAP) equations] in the best understood of such machines, namely the Hopfield model of neural networks, and we explicit how they can be used as iterative message-passing algorithms, providing a fast method to compute the local polarizations of neurons. In the "retrieval phase", where neurons polarize in the direction of one memorized pattern, we point out a major difference between the belief propagation and TAP equations: The set of belief propagation equations depends on the pattern which is retrieved, while one can use a unique set of TAP equations...
February 2017: Physical Review. E
https://www.readbyqxmd.com/read/28296961/correction-gaussian-binary-restricted-boltzmann-machines-for-modeling-natural-image-statistics
#13
Jan Melchior, Nan Wang, Laurenz Wiskott
[This corrects the article DOI: 10.1371/journal.pone.0171015.].
2017: PloS One
https://www.readbyqxmd.com/read/28287986/deepx-deep-learning-accelerator-for-restricted-boltzmann-machine-artificial-neural-networks
#14
Lok-Won Kim
Although there have been many decades of research and commercial presence on high performance general purpose processors, there are still many applications that require fully customized hardware architectures for further computational acceleration. Recently, deep learning has been successfully used to learn in a wide variety of applications, but their heavy computation demand has considerably limited their practical applications. This paper proposes a fully pipelined acceleration architecture to alleviate high computational demand of an artificial neural network (ANN) which is restricted Boltzmann machine (RBM) ANNs...
March 8, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28265122/ontology-based-deep-learning-for-human-behavior-prediction-with-explanations-in-health-social-networks
#15
Nhathai Phan, Dejing Dou, Hao Wang, David Kil, Brigitte Piniewski
Human behavior modeling is a key component in application domains such as healthcare and social behavior research. In addition to accurate prediction, having the capacity to understand the roles of human behavior determinants and to provide explanations for the predicted behaviors is also important. Having this capacity increases trust in the systems and the likelihood that the systems actually will be adopted, thus driving engagement and loyalty. However, most prediction models do not provide explanations for the behaviors they predict...
April 2017: Information Sciences
https://www.readbyqxmd.com/read/28152552/gaussian-binary-restricted-boltzmann-machines-for-modeling-natural-image-statistics
#16
Jan Melchior, Nan Wang, Laurenz Wiskott
We present a theoretical analysis of Gaussian-binary restricted Boltzmann machines (GRBMs) from the perspective of density models. The key aspect of this analysis is to show that GRBMs can be formulated as a constrained mixture of Gaussians, which gives a much better insight into the model's capabilities and limitations. We further show that GRBMs are capable of learning meaningful features without using a regularization term and that the results are comparable to those of independent component analysis. This is illustrated for both a two-dimensional blind source separation task and for modeling natural image patches...
2017: PloS One
https://www.readbyqxmd.com/read/28113522/mesh-convolutional-restricted-boltzmann-machines-for-unsupervised-learning-of-features-with-structure-preservation-on-3-d-meshes
#17
Zhizhong Han, Zhenbao Liu, Junwei Han, Chi-Man Vong, Shuhui Bu, Chun Long Philip Chen
Discriminative features of 3-D meshes are significant to many 3-D shape analysis tasks. However, handcrafted descriptors and traditional unsupervised 3-D feature learning methods suffer from several significant weaknesses: 1) the extensive human intervention is involved; 2) the local and global structure information of 3-D meshes cannot be preserved, which is in fact an important source of discriminability; 3) the irregular vertex topology and arbitrary resolution of 3-D meshes do not allow the direct application of the popular deep learning models; 4) the orientation is ambiguous on the mesh surface; and 5) the effect of rigid and nonrigid transformations on 3-D meshes cannot be eliminated...
June 30, 2016: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28113441/mapping-generative-models-onto-a-network-of-digital-spiking-neurons
#18
Bruno U Pedroni, Srinjoy Das, John V Arthur, Paul A Merolla, Bryan L Jackson, Dharmendra S Modha, Kenneth Kreutz-Delgado, Gert Cauwenberghs
Stochastic neural networks such as Restricted Boltzmann Machines (RBMs) have been successfully used in applications ranging from speech recognition to image classification, and are particularly interesting because of their potential for generative tasks. Inference and learning in these algorithms use a Markov Chain Monte Carlo procedure called Gibbs sampling, where a logistic function forms the kernel of this sampler. On the other side of the spectrum, neuromorphic systems have shown great promise for low-power and parallelized cognitive computing, but lack well-suited applications and automation procedures...
May 18, 2016: IEEE Transactions on Biomedical Circuits and Systems
https://www.readbyqxmd.com/read/28113374/unsupervised-3d-local-feature-learning-by-circle-convolutional-restricted-boltzmann-machine
#19
Zhizhong Han, Zhenbao Liu, Junwei Han, Chi-Man Vong, Shuhui Bu, Xuelong Li
Extracting local features from 3D shapes is an important and challenging task that usually requires carefully designed 3D shape descriptors. However, these descriptors are hand-crafted and require intensive human intervention with prior knowledge. To tackle this issue, we propose a novel deep learning model, namely Circle Convolutional Restricted Boltzmann Machine (CCRBM), for unsupervised 3D local feature learning. CCRBM is specially designed to learn from raw 3D representations. It effectively overcomes obstacles such as irregular vertex topology, orientation ambiguity on the 3D surface, and rigid or slightly non-rigid transformation invariance in the hierarchical learning of 3D data that cannot be resolved by the existing deep learning models...
September 2, 2016: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28095781/link-prediction-in-drug-target-interactions-network-using-similarity-indices
#20
Yiding Lu, Yufan Guo, Anna Korhonen
BACKGROUND: In silico drug-target interaction (DTI) prediction plays an integral role in drug repositioning: the discovery of new uses for existing drugs. One popular method of drug repositioning is network-based DTI prediction, which uses complex network theory to predict DTIs from a drug-target network. Currently, most network-based DTI prediction is based on machine learning - methods such as Restricted Boltzmann Machines (RBM) or Support Vector Machines (SVM). These methods require additional information about the characteristics of drugs, targets and DTIs, such as chemical structure, genome sequence, binding types, causes of interactions, etc...
January 17, 2017: BMC Bioinformatics
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