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Journal articles on the topic 'Restricted Boltzmann Machine (RBM)'

Author: Grafiati

Published: 6 September 2023

Last updated: 25 July 2025

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1

Côté, Marc-Alexandre, and Hugo Larochelle. "An Infinite Restricted Boltzmann Machine." Neural Computation 28, no. 7 (2016): 1265–88. http://dx.doi.org/10.1162/neco_a_00848.

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We present a mathematical construction for the restricted Boltzmann machine (RBM) that does not require specifying the number of hidden units. In fact, the hidden layer size is adaptive and can grow during training. This is obtained by first extending the RBM to be sensitive to the ordering of its hidden units. Then, with a carefully chosen definition of the energy function, we show that the limit of infinitely many hidden units is well defined. As with RBM, approximate maximum likelihood training can be performed, resulting in an algorithm that naturally and adaptively adds trained hidden uni

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Li, Yu, Yuan Zhang, and Yue Ji. "Privacy-Preserving Restricted Boltzmann Machine." Computational and Mathematical Methods in Medicine 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/138498.

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With the arrival of the big data era, it is predicted that distributed data mining will lead to an information technology revolution. To motivate different institutes to collaborate with each other, the crucial issue is to eliminate their concerns regarding data privacy. In this paper, we propose a privacy-preserving method for training a restricted boltzmann machine (RBM). The RBM can be got without revealing their private data to each other when using our privacy-preserving method. We provide a correctness and efficiency analysis of our algorithms. The comparative experiment shows that the a

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Zhang, Jingshuai, Yuanxin Ouyang, Weizhu Xie, Wenge Rong, and Zhang Xiong. "Context-aware restricted Boltzmann machine meets collaborative filtering." Online Information Review 44, no. 2 (2018): 455–76. http://dx.doi.org/10.1108/oir-02-2017-0069.

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Purpose The purpose of this paper is to propose an approach to incorporate contextual information into collaborative filtering (CF) based on the restricted Boltzmann machine (RBM) and deep belief networks (DBNs). Traditionally, neither the RBM nor its derivative model has been applied to modeling contextual information. In this work, the authors analyze the RBM and explore how to utilize a user’s occupation information to enhance recommendation accuracy. Design/methodology/approach The proposed approach is based on the RBM. The authors employ user occupation information as a context to design

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Aoki, Ken-Ichi, and Tamao Kobayashi. "Restricted Boltzmann machines for the long range Ising models." Modern Physics Letters B 30, no. 34 (2016): 1650401. http://dx.doi.org/10.1142/s0217984916504017.

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We set up restricted Boltzmann machines (RBM) to reproduce the long range Ising (LRI) models of the Ohmic type in one dimension. The RBM parameters are tuned by using the standard machine learning procedure with an additional method of configuration with probability (CwP). The quality of resultant RBM is evaluated through the susceptibility with respect to the magnetic external field. We compare the results with those by block decimation renormalization group (BDRG) method, and our RBM clear the test with satisfactory precision.

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Wei, Jiangshu, Jiancheng Lv, and Zhang Yi. "A New Sparse Restricted Boltzmann Machine." International Journal of Pattern Recognition and Artificial Intelligence 33, no. 10 (2019): 1951004. http://dx.doi.org/10.1142/s0218001419510042.

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Although existing sparse restricted Boltzmann machine (SRBM) can make some hidden units activated, the major disadvantage is that the sparseness of data distribution is usually overlooked and the reconstruction error becomes very large after the hidden unit variables become sparse. Different from the SRBMs which only incorporate a sparse constraint term in the energy function formula from the original restricted Boltzmann machine (RBM), an energy function constraint SRBM (ESRBM) is proposed in this paper. The proposed ESRBM takes into account the sparseness of the data distribution so that the

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Lan, Jiabao, and Xiaodong Qian. "Research on Improved RBM Recommendation Algorithm Based on Gibbs Sampling." Scalable Computing: Practice and Experience 26, no. 3 (2025): 1017–34. https://doi.org/10.12694/scpe.v26i3.4166.

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Restricted Boltzmann Machine (RBM) is an important tool for personalized recommendation prediction, but it ignores the power-law distribution of the Restricted Boltzmann Machine data set, the RBM algorithm can not focus on the tail data sampling of the recommended data set. Therefore, firstly, the recommended data are obtained and the data characteristics are analyzed, then the random Gibbs Sampling initial value of RBM is changed to random selection in the early iteration and the last sampling value in the later iteration, the fixed Gibbs sampling steps were replaced by single-step sampling (

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Dewi, Christine, Rung-Ching Chen, Hendry, and Hsiu-Te Hung. "Experiment Improvement of Restricted Boltzmann Machine Methods for Image Classification." Vietnam Journal of Computer Science 08, no. 03 (2021): 417–32. http://dx.doi.org/10.1142/s2196888821500184.

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Restricted Boltzmann machine (RBM) plays an important role in current deep learning techniques, as most of the existing deep networks are based on or related to generative models and image classification. Many applications for RBMs have been developed for a large variety of learning problems. Recent developments have demonstrated the capacity of RBM to be powerful generative models, able to extract useful features from input data or construct deep artificial neural networks. In this work, we propose a learning algorithm to find the optimal model complexity for the RBM by improving the hidden l

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Wang, Qianglong, Xiaoguang Gao, Kaifang Wan, Fei Li, and Zijian Hu. "A Novel Restricted Boltzmann Machine Training Algorithm with Fast Gibbs Sampling Policy." Mathematical Problems in Engineering 2020 (March 20, 2020): 1–19. http://dx.doi.org/10.1155/2020/4206457.

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The restricted Boltzmann machine (RBM) is one of the widely used basic models in the field of deep learning. Although many indexes are available for evaluating the advantages of RBM training algorithms, the classification accuracy is the most convincing index that can most effectively reflect its advantages. RBM training algorithms are sampling algorithms essentially based on Gibbs sampling. Studies focused on algorithmic improvements have mainly faced challenges in improving the classification accuracy of the RBM training algorithms. To address the above problem, in this paper, we propose a f

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Song, Haifeng, Guangsheng Chen, and Weiwei Yang. "An Image Classification Algorithm and its Parallel Implementation Based on ANL-RBM." Journal of Information Technology Research 11, no. 3 (2018): 29–46. http://dx.doi.org/10.4018/jitr.2018070103.

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This article describes how when using Restricted Boltzmann Machine (RBM) algorithm to design the image classification network. The node number in each hidden layer, and the layer number of the entire network are designed by experiments, it increases the complexity for the RBM design. In order to solve the problem, this article proposes an image classification algorithm based on ANL-RBM (Adaptive Nodes and Layers Restricted Boltzmann Machine). The algorithm can automatically calculate the node number in each hidden layer and the layer number of the entire network. It can reduce the complexity f

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Gu, Jing, and Kai Zhang. "Thermodynamics of the Ising Model Encoded in Restricted Boltzmann Machines." Entropy 24, no. 12 (2022): 1701. http://dx.doi.org/10.3390/e24121701.

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The restricted Boltzmann machine (RBM) is a two-layer energy-based model that uses its hidden–visible connections to learn the underlying distribution of visible units, whose interactions are often complicated by high-order correlations. Previous studies on the Ising model of small system sizes have shown that RBMs are able to accurately learn the Boltzmann distribution and reconstruct thermal quantities at temperatures away from the critical point Tc. How the RBM encodes the Boltzmann distribution and captures the phase transition are, however, not well explained. In this work, we perform RBM

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Liu, Junhui, Yajuan Jia, Yaya Wang, and Petr Dolezel. "Development of Driver-Behavior Model Based onWOA-RBM Deep Learning Network." Journal of Advanced Transportation 2020 (September 29, 2020): 1–11. http://dx.doi.org/10.1155/2020/8859891.

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Human drivers’ behavior, which is very difficult to model, is a very complicated stochastic system. To characterize a high-accuracy driver behavior model under different roadway geometries, the paper proposes a new algorithm of driver behavior model based on the whale optimization algorithm-restricted Boltzmann machine (WOA-RBM) method. This method establishes an objective optimization function first, which contains the training of RBM deep learning network based on the real driver behavior data. Second, the optimal training parameters of the restricted Boltzmann machine (RBM) can be obtained

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Hoyle, David C. "Replica analysis of the lattice-gas restricted Boltzmann machine partition function." Journal of Statistical Mechanics: Theory and Experiment 2023, no. 1 (2023): 013301. http://dx.doi.org/10.1088/1742-5468/acaf83.

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Abstract We study the expectation value of the logarithm of the partition function of large binary-to-binary lattice-gas restricted Boltzmann machines (RBMs) within a replica-symmetric ansatz, averaging over the disorder represented by the parameters of the RBM Hamiltonian. Averaging over the Hamiltonian parameters is done with a diagonal covariance matrix. Due to the diagonal form of the parameter covariance matrix not being preserved under the isomorphism between the Ising and lattice-gas forms of the RBM, we find differences in the behaviour of the quenched log partition function of the lat

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Rully Widiastutik, Lukman Zaman P. C. S. W, and Joan Santoso. "Peringkasan Teks Ekstraktif pada Dokumen Tunggal Menggunakan Metode Restricted Boltzmann Machine." Journal of Intelligent System and Computation 1, no. 2 (2019): 58–64. http://dx.doi.org/10.52985/insyst.v1i2.84.

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Penelitian yang dilakukan yaitu menghasilkan peringkasan teks ekstratif secara otomatis yang dapat membantu menghasilkan dokumen yang lebih pendek dari dokumen aslinya dengan cara mengambil kalimat penting dari dokumen sehingga pembaca dapat memahami isi dokumen dengan cepat tanpa membaca secara keseluruhan. Dataset yang digunakan sebanyak 30 dokumen tunggal teks berita berbahasa Indonesia yang diperoleh dari www.kompas.com pada kategori tekno. Dalam penelitian ini, digunakan sepuluh fitur yaitu posisi kalimat, panjang kalimat, data numerik, bobot kalimat, kesamaan antara kalimat dan centroid,

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Bao, Lin, Xiaoyan Sun, Yang Chen, Guangyi Man, and Hui Shao. "Restricted Boltzmann Machine-Assisted Estimation of Distribution Algorithm for Complex Problems." Complexity 2018 (November 1, 2018): 1–13. http://dx.doi.org/10.1155/2018/2609014.

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A novel algorithm, called restricted Boltzmann machine-assisted estimation of distribution algorithm, is proposed for solving computationally expensive optimization problems with discrete variables. First, the individuals are evaluated using expensive fitness functions of the complex problems, and some dominant solutions are selected to construct the surrogate model. The restricted Boltzmann machine (RBM) is built and trained with the dominant solutions to implicitly extract the distributed representative information of the decision variables in the promising subset. The visible layer’s probab

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Prima, Indiko, and Defri Ahmad. "ANALISIS CONDITIONAL RESTRICTED BOLTZMAN MACHINE UNTUK MEMPREDIKSI HARGA SAHAM BANK SYARIAH INDONESIA." Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika 4, no. 1 (2023): 409–16. http://dx.doi.org/10.46306/lb.v4i1.266.

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This study aims to predict the movement of Bank Syariah Indonesia shares (BRIS.JK) prices using the Conditional Restricted Boltzmann Machine (CRBM) method. Prediction is needed in conducting share transactions, because the increase or decrease in share price movements is very difficult to predict. The CRBM method is a machine learning algorithm used to model the probability distribution of data associated with variables and inputs. CRBM is a type of Restricted Boltzman Machine (RBM) that consists of two layers, namely the input layer and the hidden layer. CRBM is a type of Boltzmann machine mo

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He, Xiao-hui, Dong Wang, Yan-feng Li, and Chun-hua Zhou. "A Novel Bearing Fault Diagnosis Method Based on Gaussian Restricted Boltzmann Machine." Mathematical Problems in Engineering 2016 (2016): 1–8. http://dx.doi.org/10.1155/2016/2957083.

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To realize the fault diagnosis of bearing effectively, this paper presents a novel bearing fault diagnosis method based on Gaussian restricted Boltzmann machine (Gaussian RBM). Vibration signals are firstly resampled to the same equivalent speed. Subsequently, the envelope spectrums of the resampled data are used directly as the feature vectors to represent the fault types of bearing. Finally, in order to deal with the high-dimensional feature vectors based on envelope spectrum, a classifier model based on Gaussian RBM is applied. Gaussian RBM has the ability to provide a closed-form represent

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Assis, Carlos A. S., Eduardo J. Machado, Adriano C. M. Pereira, and Eduardo G. Carrano. "Hybrid deep learning approach for financial time series classification." Revista Brasileira de Computação Aplicada 10, no. 2 (2018): 54–63. http://dx.doi.org/10.5335/rbca.v10i2.7904.

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This paper proposes a combined approach of two machine learning techniques for financial time series classification. Boltzmann Restricted Machines (RBM) were used as the latent features extractor and Support Vector Machines (SVM) as the classifier. Tests were performed with real data of five assets from Brazilian Stock Market. The results of the combined RBM + SVM techniques showed better performance when compared to the isolated SVM, which suggests that the proposed approach can be suitable for the considered application.

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Jiang, Yun, Junyu Zhuo, Juan Zhang, and Xiao Xiao. "The optimization of parallel convolutional RBM based on Spark." International Journal of Wavelets, Multiresolution and Information Processing 17, no. 02 (2019): 1940011. http://dx.doi.org/10.1142/s0219691319400113.

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With the extensive attention and research of the scholars in deep learning, the convolutional restricted Boltzmann machine (CRBM) model based on restricted Boltzmann machine (RBM) is widely used in image recognition, speech recognition, etc. However, time consuming training still seems to be an unneglectable issue. To solve this problem, this paper mainly uses optimized parallel CRBM based on Spark, and proposes a parallel comparison divergence algorithm based on Spark and uses it to train the CRBM model to improve the training speed. The experiments show that the method is faster than traditi

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Wahid, Fazli, Sania Azhar, Sikandar Ali, Muhammad Sultan Zia, Faisal Abdulaziz Almisned, and Abdu Gumaei. "Pneumonia Detection in Chest X-Ray Images Using Enhanced Restricted Boltzmann Machine." Journal of Healthcare Engineering 2022 (August 12, 2022): 1–17. http://dx.doi.org/10.1155/2022/1678000.

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The process of pneumonia detection has been the focus of researchers as it has proved itself to be one of the most dangerous and life-threatening disorders. In recent years, many machine learning and deep learning algorithms have been applied in an attempt to automate this process but none of them has been successful significantly to achieve the highest possible accuracy. In a similar attempt, we propose an enhanced approach of a deep learning model called restricted Boltzmann machine (RBM) which is named enhanced RBM (ERBM). One of the major drawbacks associated with the standard format of RB

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Mahmoud, Abeer M., and Hanen Karamti. "Classifying a type of brain disorder in children: an effective fMRI based deep attempt." Indonesian Journal of Electrical Engineering and Computer Science 22, no. 1 (2021): 260–69. https://doi.org/10.11591/ijeecs.v22.i1.pp260-269.

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Recent advanced intelligent learning approaches that are based on using neural networks in medical diagnosing increased researcher expectations. In fact, the literature proved a straight-line relation of the exact needs and the achieved results. Accordingly, it encouraged promising directions of applying these approaches toward saving time and efforts. This paper proposes a novel hybrid deep learning framework that is based on the restricted boltzmann machines (RBM) and the contractive autoencoder (CA) to classify the brain disorder and healthy control cases in children less than 12 years. The

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Wu, Jue, Lei Yang, Fujun Yang, Peihong Zhang, and Keqiang Bai. "Hybrid recommendation algorithm based on real-valued RBM and CNN." Mathematical Biosciences and Engineering 19, no. 10 (2022): 10673–86. http://dx.doi.org/10.3934/mbe.2022499.

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<abstract> <p>With the unprecedented development of big data, it is becoming hard to get the valuable information hence, the recommendation system is becoming more and more popular. When the limited Boltzmann machine is used for collaborative filtering, only the scoring matrix is considered, and the influence of the item content, the user characteristics and the user evaluation content on the predicted score is not considered. To solve this problem, the modified hybrid recommendation algorithm based on Gaussian restricted Boltzmann machine is proposed in the paper. The user text in

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Larochelle, Hugo, Yoshua Bengio, and Joseph Turian. "Tractable Multivariate Binary Density Estimation and the Restricted Boltzmann Forest." Neural Computation 22, no. 9 (2010): 2285–307. http://dx.doi.org/10.1162/neco_a_00014.

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We investigate the problem of estimating the density function of multivariate binary data. In particular, we focus on models for which computing the estimated probability of any data point is tractable. In such a setting, previous work has mostly concentrated on mixture modeling approaches. We argue that for the problem of tractable density estimation, the restricted Boltzmann machine (RBM) provides a competitive framework for multivariate binary density modeling. With this in mind, we also generalize the RBM framework and present the restricted Boltzmann forest (RBForest), which replaces the

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Yao, Yunkai. "Quantum computation of Restricted Boltzmann Machines by Monte Carlo Methods." Highlights in Science, Engineering and Technology 9 (September 30, 2022): 227–32. http://dx.doi.org/10.54097/hset.v9i.1780.

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In recent years, the diversification of problems that require computers to solve has attracted attention to the construction of meta-heuristics that can be applied to a wide range of problems, and to specialized computers that implement these meta-heuristics in their devices. The representative meta-heuristics are Simulated Annealing (SA) and its extension to quantum computation, Quantum Annealing (QA), and its path-integral Monte Carlo method for classical simulation Crosson and Harrow showed that for certain problems where QA outperformed SA, SQA achieved performance close to that of QA, and

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Pratama, Yohanssen, Riyanthi Angrainy Sianturi, Dedi Chandra, Kristopel Lumbantoruan, and Indah Trivena Tampubolon. "Restricted Boltzmann Machine and Matrix Factorization-Alternating Square Algorithm for Development Tourist Recommendation System." Journal of Physics: Conference Series 2394, no. 1 (2022): 012004. http://dx.doi.org/10.1088/1742-6596/2394/1/012004.

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Abstract The rapid development of technology affects the growth of tourist attraction information in Indonesia. Therefore, an accurate recommendation system is needed in recommending tourist attractions. In this final project, we use the Collaborative Filtering method, namely the Restricted Boltzmann Machine (RBM) algorithm and the Matrix Factorization-Alternating Least Squares (MF-ALS) algorithm in recommending tourist attractions. Attraction recommendations will be generated from the type of tourist attraction available on the website and the rating that has been given by previous users who

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Rastgoo, Razieh, Kourosh Kiani, and Sergio Escalera. "Multi-Modal Deep Hand Sign Language Recognition in Still Images Using Restricted Boltzmann Machine." Entropy 20, no. 11 (2018): 809. http://dx.doi.org/10.3390/e20110809.

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In this paper, a deep learning approach, Restricted Boltzmann Machine (RBM), is used to perform automatic hand sign language recognition from visual data. We evaluate how RBM, as a deep generative model, is capable of generating the distribution of the input data for an enhanced recognition of unseen data. Two modalities, RGB and Depth, are considered in the model input in three forms: original image, cropped image, and noisy cropped image. Five crops of the input image are used and the hand of these cropped images are detected using Convolutional Neural Network (CNN). After that, three types

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Khan, Umair, Pooyan Safari, and Javier Hernando. "Restricted Boltzmann Machine Vectors for Speaker Clustering and Tracking Tasks in TV Broadcast Shows." Applied Sciences 9, no. 13 (2019): 2761. http://dx.doi.org/10.3390/app9132761.

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Restricted Boltzmann Machines (RBMs) have shown success in both the front-end and backend of speaker verification systems. In this paper, we propose applying RBMs to the front-end for the tasks of speaker clustering and speaker tracking in TV broadcast shows. RBMs are trained to transform utterances into a vector based representation. Because of the lack of data for a test speaker, we propose RBM adaptation to a global model. First, the global model—which is referred to as universal RBM—is trained with all the available background data. Then an adapted RBM model is trained with the data of eac

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P. Deepa. "Conditional Mutual Information Maximization and Restricted Boltzmann Machine for Stock Price Prediction." Journal of Information Systems Engineering and Management 10, no. 8s (2025): 421–38. https://doi.org/10.52783/jisem.v10i8s.1083.

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Using various machine learning (ML) methodologies in forecasting the stock prices has lately been successful. Nonetheless, most of these models depend on the narrow set of attributes as input and lacks the sufficient information to offer the estimates of stock market. To enhance the stock price prediction models, conditional mutual information maximizing (CMIM) method is used for data preprocessing and feature selection based on restricted boltzmann machine (RBM). By optimizing their conditional mutual information with the target variable, CMIM assists in selecting the most significant charact

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Cheng, Song, Jing Chen, and Lei Wang. "Information Perspective to Probabilistic Modeling: Boltzmann Machines versus Born Machines." Entropy 20, no. 8 (2018): 583. http://dx.doi.org/10.3390/e20080583.

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We compare and contrast the statistical physics and quantum physics inspired approaches for unsupervised generative modeling of classical data. The two approaches represent probabilities of observed data using energy-based models and quantum states, respectively. Classical and quantum information patterns of the target datasets therefore provide principled guidelines for structural design and learning in these two approaches. Taking the Restricted Boltzmann Machines (RBM) as an example, we analyze the information theoretical bounds of the two approaches. We also estimate the classical mutual i

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Nastiti, Vinna Rahmayanti Setyaning, Zamah Sari, and Bella Chintia Eka Merita. "The Implementation of Restricted Boltzmann Machine in Choosing a Specialization for Informatics Students." Jurnal Online Informatika 8, no. 1 (2023): 36–43. http://dx.doi.org/10.15575/join.v8i1.917.

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Choosing a specialization was not an easy task for some students, especially for those who lacked confidence in their skill and ability. Specialization in tertiary education became the benchmark and key to success for students’ future careers. This study was conducted to provide the learning outcomes record, which showed the specialization classification for the Informatics students by using the data from the students of 2013-2015 who had graduated. The total data was 319 students. The classification method used for this study was the Restricted Boltzmann Machine (RBM). However, the data showe

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Le Roux, Nicolas, and Yoshua Bengio. "Representational Power of Restricted Boltzmann Machines and Deep Belief Networks." Neural Computation 20, no. 6 (2008): 1631–49. http://dx.doi.org/10.1162/neco.2008.04-07-510.

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Deep belief networks (DBN) are generative neural network models with many layers of hidden explanatory factors, recently introduced by Hinton, Osindero, and Teh (2006) along with a greedy layer-wise unsupervised learning algorithm. The building block of a DBN is a probabilistic model called a restricted Boltzmann machine (RBM), used to represent one layer of the model. Restricted Boltzmann machines are interesting because inference is easy in them and because they have been successfully used as building blocks for training deeper models. We first prove that adding hidden units yields strictly

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Dixit, Vivek, Yaroslav Koshka, Tamer Aldwairi, and M. A. Novotny. "Comparison of quantum and classical methods for labels and patterns in Restricted Boltzmann Machines." Journal of Physics: Conference Series 2122, no. 1 (2021): 012007. http://dx.doi.org/10.1088/1742-6596/2122/1/012007.

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Abstract Classification and data reconstruction using a restricted Boltzmann machine (RBM) is presented. RBM is an energy-based model which assigns low energy values to the configurations of interest. It is a generative model, once trained it can be used to produce samples from the target distribution. The D-Wave 2000Q is a quantum computer which has been used to exploit its quantum effect for machine learning. Bars-and-stripes (BAS) and cybersecurity (ISCX) datasets were used to train RBMs. The weights and biases of trained RBMs were used to map onto the D-Wave. Classification as well as imag

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Savitha, Ramasamy, ArulMurugan Ambikapathi, and Kanagasabai Rajaraman. "Online RBM: Growing Restricted Boltzmann Machine on the fly for unsupervised representation." Applied Soft Computing 92 (July 2020): 106278. http://dx.doi.org/10.1016/j.asoc.2020.106278.

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Bulso, Nicola, and Yasser Roudi. "Restricted Boltzmann Machines as Models of Interacting Variables." Neural Computation 33, no. 10 (2021): 2646–81. http://dx.doi.org/10.1162/neco_a_01420.

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Abstract We study the type of distributions that restricted Boltzmann machines (RBMs) with different activation functions can express by investigating the effect of the activation function of the hidden nodes on the marginal distribution they impose on observed binary nodes. We report an exact expression for these marginals in the form of a model of interacting binary variables with the explicit form of the interactions depending on the hidden node activation function. We study the properties of these interactions in detail and evaluate how the accuracy with which the RBM approximates distribu

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Crawford, Daniel, Anna Levit, Navid Ghadermarzy, Jaspreet S. Oberoi, and Pooya Ronagh. "Reinforcement learning using quantum Boltzmann machines." Quantum Information and Computation 18, no. 1&2 (2018): 51–74. http://dx.doi.org/10.26421/qic18.1-2-3.

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We investigate whether quantum annealers with select chip layouts can outperform classical computers in reinforcement learning tasks. We associate a transverse field Ising spin Hamiltonian with a layout of qubits similar to that of a deep Boltzmann machine (DBM) and use simulated quantum annealing (SQA) to numerically simulate quantum sampling from this system. We design a reinforcement learning algorithm in which the set of visible nodes representing the states and actions of an optimal policy are the first and last layers of the deep network. In absence of a transverse field, our simulations

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Qu, Jia, Zihao Song, Xiaolong Cheng, Zhibin Jiang, and Jie Zhou. "Neighborhood-based inference and restricted Boltzmann machine for small molecule-miRNA associations prediction." PeerJ 11 (August 24, 2023): e15889. http://dx.doi.org/10.7717/peerj.15889.

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Background A growing number of experiments have shown that microRNAs (miRNAs) can be used as target of small molecules (SMs) to regulate gene expression for treating diseases. Therefore, identifying SM-related miRNAs is helpful for the treatment of diseases in the domain of medical investigation. Methods This article presents a new computational model, called NIRBMSMMA (neighborhood-based inference (NI) and restricted Boltzmann machine (RBM)), which we developed to identify potential small molecule-miRNA associations (NIRBMSMMA). First, grounded on known SM-miRNAs associations, SM similarity a

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Bader Alazzam, Malik, Fawaz Alassery, and Ahmed Almulihi. "Identification of Diabetic Retinopathy through Machine Learning." Mobile Information Systems 2021 (November 26, 2021): 1–8. http://dx.doi.org/10.1155/2021/1155116.

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A cross-sectional study of patients with suspected diabetic retinopathy (DR) who had an ophthalmological examination and a retinal scan is the focus of this research. Specialized retinal images were analyzed and classified using OPF and RBM models (restricted Boltzmann machines). Classification of retinographs was based on the presence or absence of disease-related retinopathy (DR). The RBM and OPF models extracted 500 and 1000 characteristics from the images for disease classification after the system training phase for the recognition of retinopathy and normality patterns. There were a total

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Tubiana, Jérôme, Simona Cocco, and Rémi Monasson. "Learning Compositional Representations of Interacting Systems with Restricted Boltzmann Machines: Comparative Study of Lattice Proteins." Neural Computation 31, no. 8 (2019): 1671–717. http://dx.doi.org/10.1162/neco_a_01210.

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A restricted Boltzmann machine (RBM) is an unsupervised machine learning bipartite graphical model that jointly learns a probability distribution over data and extracts their relevant statistical features. RBMs were recently proposed for characterizing the patterns of coevolution between amino acids in protein sequences and for designing new sequences. Here, we study how the nature of the features learned by RBM changes with its defining parameters, such as the dimensionality of the representations (size of the hidden layer) and the sparsity of the features. We show that for adequate values of

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Noormandipour, Mohammadreza, Sun Youran, and Babak Haghighat. "Restricted Boltzmann machine representation for the groundstate and excited states of Kitaev Honeycomb model." Machine Learning: Science and Technology 3, no. 1 (2021): 015010. http://dx.doi.org/10.1088/2632-2153/ac3ddf.

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Abstract In this work, the capability of restricted Boltzmann machines (RBMs) to find solutions for the Kitaev honeycomb model with periodic boundary conditions is investigated. The measured groundstate energy of the system is compared and, for small lattice sizes (e.g. 3 × 3 with 18 spinors), shown to agree with the analytically derived value of the energy up to a deviation of 0.09 % . Moreover, the wave-functions we find have 99.89 % overlap with the exact ground state wave-functions. Furthermore, the possibility of realizing anyons in the RBM is discussed and an algorithm is given to build

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Koshka, Yaroslav, Dilina Perera, Spencer Hall, and M. A. Novotny. "Determination of the Lowest-Energy States for the Model Distribution of Trained Restricted Boltzmann Machines Using a 1000 Qubit D-Wave 2X Quantum Computer." Neural Computation 29, no. 7 (2017): 1815–37. http://dx.doi.org/10.1162/neco_a_00974.

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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. In many cases, the D-Wave machine su

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Siddula, Sundeep, G. K. Prashanth, Praful Nandankar, et al. "Optimal Placement of Hybrid Wind-Solar System Using Deep Learning Model." International Journal of Photoenergy 2022 (May 25, 2022): 1–7. http://dx.doi.org/10.1155/2022/2881603.

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In this paper, we develop an optimal placement of solar-wind energy systems using restricted Boltzmann machine (RBM). The RBM considers various factors to scale the process of optimal placement and enables proper sizing and placement for attaining increased electricity production from both wind and solar systems. The multiobjective criterion from both solar and wind energy farms simulated on MATLAB simulator shows an increased number of accuracies with reduced mean average error and computation time during training and testing. The results show that the RBM achieves improved rate of finding th

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Cheng, Xiaolong, Jia Qu, Shuangbao Song, and Zekang Bian. "Neighborhood-based inference and restricted Boltzmann machine for microbe and drug associations prediction." PeerJ 10 (August 15, 2022): e13848. http://dx.doi.org/10.7717/peerj.13848.

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Background Efficient identification of microbe-drug associations is critical for drug development and solving problem of antimicrobial resistance. Traditional wet-lab method requires a lot of money and labor in identifying potential microbe-drug associations. With development of machine learning and publication of large amounts of biological data, computational methods become feasible. Methods In this article, we proposed a computational model of neighborhood-based inference (NI) and restricted Boltzmann machine (RBM) to predict potential microbe-drug association (NIRBMMDA) by using integrated

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Guo, Xian, Zhang, Li, and Ren. "Hybrid IRBM-BPNN Approach for Error Parameter Estimation of SINS on Aircraft." Sensors 19, no. 17 (2019): 3682. http://dx.doi.org/10.3390/s19173682.

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To realize the error parameter estimation of strap-down inertial navigation system (SINS) and improve the navigation accuracy for aircraft, a hybrid improved restricted Boltzmann machine BP neural network (IRBM-BPNN) approach, which combines restricted Boltzmann machine (RBM) and BP neural network (BPNN), is proposed to forecast the inertial measurement unit (IMU) instrument errors and initial alignment errors of SINS. Firstly, the error generation mechanism of SINS is analyzed, and initial alignment error model and IMU instrument error model are established. Secondly, an unsupervised RBM meth

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Wu, Yangjun, Xiansong Xu, Dario Poletti, Yi Fan, Chu Guo, and Honghui Shang. "A Real Neural Network State for Quantum Chemistry." Mathematics 11, no. 6 (2023): 1417. http://dx.doi.org/10.3390/math11061417.

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The restricted Boltzmann machine (RBM) has recently been demonstrated as a useful tool to solve the quantum many-body problems. In this work we propose tanh-FCN, which is a single-layer fully connected neural network adapted from RBM, to study ab initio quantum chemistry problems. Our contribution is two-fold: (1) our neural network only uses real numbers to represent the real electronic wave function, while we obtain comparable precision to RBM for various prototypical molecules; (2) we show that the knowledge of the Hartree-Fock reference state can be used to systematically accelerate the co

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Behera, Dayal Kumar, Madhabananda Das, Subhra Swetanisha, and Prabira Kumar Sethy. "Hybrid model for movie recommendation system using content K-nearest neighbors and restricted Boltzmann machine." Indonesian Journal of Electrical Engineering and Computer Science 23, no. 1 (2021): 445–52. https://doi.org/10.11591/ijeecs.v23.i1.pp445-452.

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One of the most commonly used techniques in the recommendation framework is collaborative filtering (CF). It performs better with sufficient records of user rating but is not good in sparse data. Content-based filtering works well in the sparse dataset as it finds the similarity between movies by using attributes of the movies. RBM is an energy-based model serving as a backbone of deep learning and performs well in rating prediction. However, the rating prediction is not preferable by a single model. The hybrid model achieves better results by integrating the results of more than one model. Th

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Kondhalkar, Vrushali. "Image Restoration Using RBM." International Journal for Research in Applied Science and Engineering Technology 13, no. 5 (2025): 5968–71. https://doi.org/10.22214/ijraset.2025.71087.

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Image restoration is a critical task in computer vision, aiming to recover degraded images caused by noise, missing pixels, or corruption. Restricted Boltzmann Machines (RBMs), a type of unsupervised neural network, have gained popularity for their ability to learn hidden representations and restore images effectively. This paper provides a review of existing research and projects related to image restoration using RBMs and other deep learning techniques. It highlights the key approaches, algorithms, and outcomes in this field, providing a comparative perspective on their effectiveness

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Liu, Qinghua, Lu Sun, Alain Kornhauser, Jiahui Sun, and Nick Sangwa. "Road roughness acquisition and classification using improved restricted Boltzmann machine deep learning algorithm." Sensor Review 39, no. 6 (2019): 733–42. http://dx.doi.org/10.1108/sr-05-2018-0132.

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Purpose To realize classification of different pavements, a road roughness acquisition system design and an improved restricted Boltzmann machine deep neural network algorithm based on Adaboost Backward Propagation algorithm for road roughness detection is presented in this paper. The developed measurement system, including hardware designs and algorithm for software, constitutes an independent system which is low-cost, convenient for installation and small. Design/methodology/approach The inputs of restricted Boltzmann machine deep neural network are the vehicle vertical acceleration power sp

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Behera, Dayal Kumar, Madhabananda Das, Subhra Swetanisha, and Prabira Kumar Sethy. "Hybrid model for movie recommendation system using content K-nearest neighbors and restricted Boltzmann machine." Indonesian Journal of Electrical Engineering and Computer Science 23, no. 1 (2021): 445. http://dx.doi.org/10.11591/ijeecs.v23.i1.pp445-452.

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<span>One of the most commonly used techniques in the recommendation framework is collaborative filtering (CF). It performs better with sufficient records of user rating but is not good in sparse data. Content-based filtering works well in the sparse dataset as it finds the similarity between movies by using attributes of the movies. RBM is an energy-based model serving as a backbone of deep learning and performs well in rating prediction. However, the rating prediction is not preferable by a single model. The hybrid model achieves better results by integrating the results of more than o

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Cho, KyungHyun, Tapani Raiko, and Alexander Ilin. "Enhanced Gradient for Training Restricted Boltzmann Machines." Neural Computation 25, no. 3 (2013): 805–31. http://dx.doi.org/10.1162/neco_a_00397.

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Restricted Boltzmann machines (RBMs) are often used as building blocks in greedy learning of deep networks. However, training this simple model can be laborious. Traditional learning algorithms often converge only with the right choice of metaparameters that specify, for example, learning rate scheduling and the scale of the initial weights. They are also sensitive to specific data representation. An equivalent RBM can be obtained by flipping some bits and changing the weights and biases accordingly, but traditional learning rules are not invariant to such transformations. Without careful tuni

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Valle, Mauricio A. "The Capabilities of Boltzmann Machines to Detect and Reconstruct Ising System’s Configurations from a Given Temperature." Entropy 25, no. 12 (2023): 1649. http://dx.doi.org/10.3390/e25121649.

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The restricted Boltzmann machine (RBM) is a generative neural network that can learn in an unsupervised way. This machine has been proven to help understand complex systems, using its ability to generate samples of the system with the same observed distribution. In this work, an Ising system is simulated, creating configurations via Monte Carlo sampling and then using them to train RBMs at different temperatures. Then, 1. the ability of the machine to reconstruct system configurations and 2. its ability to be used as a detector of configurations at specific temperatures are evaluated. The resu

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Xu, Aoqi, Man-Wen Tian, Behnam Firouzi, Khalid A. Alattas, Ardashir Mohammadzadeh, and Ebrahim Ghaderpour. "A New Deep Learning Restricted Boltzmann Machine for Energy Consumption Forecasting." Sustainability 14, no. 16 (2022): 10081. http://dx.doi.org/10.3390/su141610081.

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A key issue in the desired operation and development of power networks is the knowledge of load growth and electricity demand in the coming years. Mid-term load forecasting (MTLF) has an important rule in planning and optimal use of power systems. However, MTLF is a complicated problem, and a lot of uncertain factors and variables disturb the load consumption pattern. This paper presents a practical approach for MTLF. A new deep learning restricted Boltzmann machine (RBM) is proposed for modelling and forecasting energy consumption. The contrastive divergence algorithm is presented for tuning

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