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restricted Boltzmann machine

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https://www.readbyqxmd.com/read/29060298/informative-sensor-selection-and-learning-for-prediction-of-lower-limb-kinematics-using-generative-stochastic-neural-networks
#1
Eunsuk Chong, Taejin Choi, Hyungmin Kim, Seung-Jong Kim, Yoha Hwang, Jong Min Lee
We propose a novel approach of selecting useful input sensors as well as learning a mathematical model for predicting lower limb joint kinematics. We applied a feature selection method based on the mutual information called the variational information maximization, which has been reported as the state-of-the-art work among information based feature selection methods. The main difficulty in applying the method is estimating reliable probability density of input and output data, especially when the data are high dimensional and real-valued...
July 2017: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/29059905/classification-of-respiratory-disturbances-in-rett-syndrome-patients-using-restricted-boltzmann-machine
#2
Heather M O'Leary, Juan Manuel Mayor, Chi-Sang Poon, Walter E Kaufmann, Mustafa Sahin
Rett syndrome (RTT) is a severe neurodevelopmental disorder that can cause pervasive wakeful respiratory disturbances that include tachypnea, breath-holding, and central apnea. Quantitative analysis of these respiratory disturbances in RTT is considered a promising outcome measure for clinical trials. Currently, machine learning methodologies have not been employed to automate the classification of RTT respiratory disturbances. In this paper, we propose using temporal, flow, and autocorrelation features taken from the respiratory inductance plethsymography chest signal...
July 2017: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/29020921/identify-huntington-s-disease-associated-genes-based-on-restricted-boltzmann-machine-with-rna-seq-data
#3
Xue Jiang, Han Zhang, Feng Duan, Xiongwen Quan
BACKGROUND: Predicting disease-associated genes is helpful for understanding the molecular mechanisms during the disease progression. Since the pathological mechanisms of neurodegenerative diseases are very complex, traditional statistic-based methods are not suitable for identifying key genes related to the disease development. Recent studies have shown that the computational models with deep structure can learn automatically the features of biological data, which is useful for exploring the characteristics of gene expression during the disease progression...
October 11, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/28964083/unsupervised-modulation-filter-learning-for-noise-robust-speech-recognition
#4
Purvi Agrawal, Sriram Ganapathy
The modulation filtering approach to robust automatic speech recognition (ASR) is based on enhancing perceptually relevant regions of the modulation spectrum while suppressing the regions susceptible to noise. In this paper, a data-driven unsupervised modulation filter learning scheme is proposed using convolutional restricted Boltzmann machine. The initial filter is learned using the speech spectrogram while subsequent filters are learned using residual spectrograms. The modulation filtered spectrograms are used for ASR experiments on noisy and reverberant speech where these features provide significant improvements over other robust features...
September 2017: Journal of the Acoustical Society of America
https://www.readbyqxmd.com/read/28950614/nonequilibrium-thermodynamics-of-restricted-boltzmann-machines
#5
Domingos S P Salazar
In this work, we analyze the nonequilibrium thermodynamics of a class of neural networks known as restricted Boltzmann machines (RBMs) in the context of unsupervised learning. We show how the network is described as a discrete Markov process and how the detailed balance condition and the Maxwell-Boltzmann equilibrium distribution are sufficient conditions for a complete thermodynamics description, including nonequilibrium fluctuation theorems. Numerical simulations in a fully trained RBM are performed and the heat exchange fluctuation theorem is verified with excellent agreement to the theory...
August 2017: Physical Review. E
https://www.readbyqxmd.com/read/28939812/efficient-representation-of-quantum-many-body-states-with-deep-neural-networks
#6
Xun Gao, Lu-Ming Duan
Part of the challenge for quantum many-body problems comes from the difficulty of representing large-scale quantum states, which in general requires an exponentially large number of parameters. Neural networks provide a powerful tool to represent quantum many-body states. An important open question is what characterizes the representational power of deep and shallow neural networks, which is of fundamental interest due to the popularity of deep learning methods. Here, we give a proof that, assuming a widely believed computational complexity conjecture, a deep neural network can efficiently represent most physical states, including the ground states of many-body Hamiltonians and states generated by quantum dynamics, while a shallow network representation with a restricted Boltzmann machine cannot efficiently represent some of those states...
September 22, 2017: Nature Communications
https://www.readbyqxmd.com/read/28923002/deep-learning-methods-for-protein-torsion-angle-prediction
#7
Haiou Li, Jie Hou, Badri Adhikari, Qiang Lyu, Jianlin Cheng
BACKGROUND: Deep learning is one of the most powerful machine learning methods that has achieved the state-of-the-art performance in many domains. Since deep learning was introduced to the field of bioinformatics in 2012, it has achieved success in a number of areas such as protein residue-residue contact prediction, secondary structure prediction, and fold recognition. In this work, we developed deep learning methods to improve the prediction of torsion (dihedral) angles of proteins...
September 18, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/28922128/deep-manifold-learning-combined-with-convolutional-neural-networks-for-action-recognition
#8
Xin Chen, Jian Weng, Wei Lu, Jiaming Xu, Jiasi Weng
Learning deep representations have been applied in action recognition widely. However, there have been a few investigations on how to utilize the structural manifold information among different action videos to enhance the recognition accuracy and efficiency. In this paper, we propose to incorporate the manifold of training samples into deep learning, which is defined as deep manifold learning (DML). The proposed DML framework can be adapted to most existing deep networks to learn more discriminative features for action recognition...
September 15, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28868149/speech-reconstruction-using-a-deep-partially-supervised-neural-network
#9
Ian McLoughlin, Jingjie Li, Yan Song, Hamid R Sharifzadeh
Statistical speech reconstruction for larynx-related dysphonia has achieved good performance using Gaussian mixture models and, more recently, restricted Boltzmann machine arrays; however, deep neural network (DNN)-based systems have been hampered by the limited amount of training data available from individual voice-loss patients. The authors propose a novel DNN structure that allows a partially supervised training approach on spectral features from smaller data sets, yielding very good results compared with the current state-of-the-art...
August 2017: Healthcare Technology Letters
https://www.readbyqxmd.com/read/28845892/multiple-functional-networks-modeling-for-autism-spectrum-disorder-diagnosis
#10
Tae-Eui Kam, Heung-Il Suk, Seong-Whan Lee
Despite countless studies on autism spectrum disorder (ASD), diagnosis relies on specific behavioral criteria and neuroimaging biomarkers for the disorder are still relatively scarce and irrelevant for diagnostic workup. Many researchers have focused on functional networks of brain activities using resting-state functional magnetic resonance imaging (rsfMRI) to diagnose brain diseases, including ASD. Although some existing methods are able to reveal the abnormalities in functional networks, they are either highly dependent on prior assumptions for modeling these networks or do not focus on latent functional connectivities (FCs) by considering discriminative relations among FCs in a nonlinear way...
August 28, 2017: Human Brain Mapping
https://www.readbyqxmd.com/read/28726751/an-adaptive-feature-learning-model-for-sequential-radar-high-resolution-range-profile-recognition
#11
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
#12
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...
September 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
#13
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
#14
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
#15
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
#16
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
#17
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
#18
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
#19
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
#20
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
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