Gotowe bibliografie tematyczne / Restricted Boltzmann Machine (RBM)

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Gotowa bibliografia na temat „Restricted Boltzmann Machine (RBM)”

Autor: Grafiati

Data publikacji: 6 września 2023

Data aktualizacji: 25 lipca 2025

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Artykuły w czasopismach na temat "Restricted Boltzmann Machine (RBM)"

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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Rozprawy doktorskie na temat "Restricted Boltzmann Machine (RBM)"

Bertholds, Alexander, and Emil Larsson. "An intelligent search for feature interactions using Restricted Boltzmann Machines." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-202208.

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Klarna uses a logistic regression to estimate the probability that an e-store customer will default on its given credit. The logistic regression is a linear statistical model which cannot detect non-linearities in the data. The aim of this project has been to develop a program which can be used to find suitable non-linear interaction-variables. This can be achieved using a Restricted Boltzmann Machine, an unsupervised neural network, whose hidden nodes can be used to model the distribution of the data. By using the hidden nodes as new variables in the logistic regression it is possible to see

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Moody, John Matali. "Process monitoring with restricted Boltzmann machines." Thesis, Stellenbosch : Stellenbosch University, 2014. http://hdl.handle.net/10019.1/86467.

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Thesis (MScEng)--Stellenbosch University, 2014.<br>ENGLISH ABSTRACT: Process monitoring and fault diagnosis are used to detect abnormal events in processes. The early detection of such events or faults is crucial to continuous process improvement. Although principal component analysis and partial least squares are widely used for process monitoring and fault diagnosis in the metallurgical industries, these models are linear in principle; nonlinear approaches should provide more compact and informative models. The use of auto associative neural networks or auto encoders provide a principled app

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McCoppin, Ryan R. "An Evolutionary Approximation to Contrastive Divergence in Convolutional Restricted Boltzmann Machines." Wright State University / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=wright1418750414.

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Vrábel, Jakub. "Popis Restricted Boltzmann machine metody ve vztahu se statistickou fyzikou a jeho následné využití ve zpracování spektroskopických dat." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2019. http://www.nusl.cz/ntk/nusl-402522.

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Práca sa zaoberá spojeniami medzi štatistickou fyzikou a strojovým učením s dôrazom na základné princípy a ich dôsledky. Ďalej sa venuje obecným vlastnostiam spektroskopických dát a ich zohľadnení pri pokročilom spracovaní dát. Začiatok práce je venovaný odvodeniu partičnej sumy štatistického systému a štúdiu Isingovho modelu pomocou "mean field" prístupu. Následne, popri základnom úvode do strojového učenia, je ukázaná ekvivalencia medzi Isingovým modelom a Hopfieldovou sieťou - modelom strojového učenia. Na konci teoretickej časti je z Hopfieldovej siete odvodený model Restricted Boltzmann M

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Svoboda, Jiří. "Multi-modální "Restricted Boltzmann Machines"." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2013. http://www.nusl.cz/ntk/nusl-236426.

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This thesis explores how multi-modal Restricted Boltzmann Machines (RBM) can be used in content-based image tagging. This work also cointains brief analysis of modalities that can be used for multi-modal classification. There are also described various RBMs, that are suitable for different kinds of input data. A design and implementation of multimodal RBM is described together with results of preliminary experiments.

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Fredriksson, Gustav, and Anton Hellström. "Restricted Boltzmann Machine as Recommendation Model for Venture Capital." Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-252703.

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Denna studie introducerar restricted Boltzmann machines (RBMs) som rekommendationsmodell i kontexten av riskkapital. Ett nätverk av relationer används som proxy för att modellera investerares bolagspreferenser. Studiens huvudfokus är att undersöka hur RBMs kan implementeras för ett dataset bestående av relationer mellan personer och bolag, samt att undersöka om modellen går att förbättra genom att tillföra av ytterligare information. Nätverket skapas från styrelsesammansättningar för svenska bolag. För nätverket implementeras RBMs både med och utan den extra informationen om bolagens ursprungs

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Juel, Bjørn Erik. "Investigating the Consistency and Convexity of Restricted Boltzmann Machine Learning." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for nevromedisin, 2013. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-25696.

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In this thesis we asses the consistency and convexity of the parameter inference in Boltzmann machine learning algorithms based on gradient ascent on the likelihood surface. We do this by rst developing standard tools for generating equillibrium data drawn from a Boltzmann distribution, as well as analytically exact algorithms for inferring the parameters of restricted and semi-restricted Boltzmann machine architctures. After testing, and showing, the functionality of our algorithms, we assess how dierent network properties eect the inferrence quality of restricted Boltzmann machines. Subseque

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Tubiana, Jérôme. "Restricted Boltzmann machines : from compositional representations to protein sequence analysis." Thesis, Paris Sciences et Lettres (ComUE), 2018. http://www.theses.fr/2018PSLEE039/document.

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Les Machines de Boltzmann restreintes (RBM) sont des modèles graphiques capables d’apprendre simultanément une distribution de probabilité et une représentation des données. Malgré leur architecture relativement simple, les RBM peuvent reproduire très fidèlement des données complexes telles que la base de données de chiffres écrits à la main MNIST. Il a par ailleurs été montré empiriquement qu’elles peuvent produire des représentations compositionnelles des données, i.e. qui décomposent les configurations en leurs différentes parties constitutives. Cependant, toutes les variantes de ce modèle

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Spiliopoulou, Athina. "Probabilistic models for melodic sequences." Thesis, University of Edinburgh, 2013. http://hdl.handle.net/1842/8876.

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Structure is one of the fundamentals of music, yet the complexity arising from the vast number of possible variations of musical elements such as rhythm, melody, harmony, key, texture and form, along with their combinations, makes music modelling a particularly challenging task for machine learning. The research presented in this thesis focuses on the problem of learning a generative model for melody directly from musical sequences belonging to the same genre. Our goal is to develop probabilistic models that can automatically capture the complex statistical dependencies evident in music withou

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de, Giorgio Andrea. "A study on the similarities of Deep Belief Networks and Stacked Autoencoders." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-174341.

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Restricted Boltzmann Machines (RBMs) and autoencoders have been used - in several variants - for similar tasks, such as reducing dimensionality or extracting features from signals. Even though their structures are quite similar, they rely on different training theories. Lately, they have been largely used as building blocks in deep learning architectures that are called deep belief networks (instead of stacked RBMs) and stacked autoencoders. In light of this, the student has worked on this thesis with the aim to understand the extent of the similarities and the overall pros and cons of using e

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Części książek na temat "Restricted Boltzmann Machine (RBM)"

Wicht, Baptiste, Andreas Fischer, and Jean Hennebert. "On CPU Performance Optimization of Restricted Boltzmann Machine and Convolutional RBM." In Artificial Neural Networks in Pattern Recognition. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46182-3_14.

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Zięba, Maciej, Jakub M. Tomczak, and Adam Gonczarek. "RBM-SMOTE: Restricted Boltzmann Machines for Synthetic Minority Oversampling Technique." In Intelligent Information and Database Systems. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15702-3_37.

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Rani, J. Lece Elizabeth, M. P. Ramkumar, and G. S. R. Emil Selvan. "Detection of Ductal Carcinoma Using Restricted Boltzmann Machine and Autoencoder (RBM-AE) in PET Scan." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-5994-5_18.

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Jo, Taeho. "Restricted Boltzmann Machine." In Deep Learning Foundations. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-32879-4_11.

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Wang, Hao, Dejing Dou, and Daniel Lowd. "Ontology-Based Deep Restricted Boltzmann Machine." In Lecture Notes in Computer Science. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-44403-1_27.

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Rani, Velpula Sandhya, Havalath Balaji, Vishal Goar, and N. Ch Sriman Narayana Iyengar. "Nipah Virus Using Restricted Boltzmann Machine." In Advances in Information Communication Technology and Computing. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5421-6_47.

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Huang, Haiping. "Statistical Mechanics of Restricted Boltzmann Machine." In Statistical Mechanics of Neural Networks. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-7570-6_10.

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Cherla, Srikanth, Son N. Tran, Artur d’Avila Garcez, and Tillman Weyde. "Generalising the Discriminative Restricted Boltzmann Machines." In Artificial Neural Networks and Machine Learning – ICANN 2017. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68612-7_13.

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Liu, Yongqi, Qiuli Tong, Zhao Du, and Lantao Hu. "Content-Boosted Restricted Boltzmann Machine for Recommendation." In Artificial Neural Networks and Machine Learning – ICANN 2014. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11179-7_97.

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Li, Jinghua, Pengyu Tian, Dehui Kong, Lichun Wang, Shaofan Wang, and Baocai Yin. "Matrix-Variate Restricted Boltzmann Machine Classification Model." In Simulation Tools and Techniques. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32216-8_47.

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Streszczenia konferencji na temat "Restricted Boltzmann Machine (RBM)"

Babu, Viswaprakash, Banothu Raju, Saidulu Valampatla, M. Venkateswarao, Alagu Sundara Pandian, and Vishal Ratansing Patil. "An Innovative Method for Electricity Load Forecasting in the Agriculture Sector Using a Restricted Boltzmann Machine (RBM) Model." In 2024 International Conference on IoT Based Control Networks and Intelligent Systems (ICICNIS). IEEE, 2024. https://doi.org/10.1109/icicnis64247.2024.10823265.

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Fadhillah, Nurul, Ingrid Nurtanio, and Syafaruddin. "A Combined ResNet50 – Restricted Boltzmann Machine for Multilabel Eye Diseases Classification." In 2025 International Conference on Advancement in Data Science, E-learning and Information System (ICADEIS). IEEE, 2025. https://doi.org/10.1109/icadeis65852.2025.10933334.

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Broelemann, Klaus, Thomas Gottron, and Gjergji Kasneci. "LTD-RBM: Robust and Fast Latent Truth Discovery Using Restricted Boltzmann Machines." In 2017 IEEE 33rd International Conference on Data Engineering (ICDE). IEEE, 2017. http://dx.doi.org/10.1109/icde.2017.60.

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Passos, Leandro Aparecido, and João Paulo Papa. "On the Training Algorithms for Restricted Boltzmann Machines." In XXXII Conference on Graphics, Patterns and Images. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/sibgrapi.est.2019.8294.

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Deep learning techniques have been studied extensively in the last years due to their good results related to essential tasks on a large range of applications, such as speech and face recognition, as well as object classification. Restrict Boltzmann Machines (RBMs) are among the most employed techniques, which are energy-based stochastic neural networks composed of two layers of neurons whose objective is to estimate the connection weights between them. Recently, the scientific community spent much effort on sampling methods since the effectiveness of RBMs is directly related to the success of

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Hu, Di, Gang Chen, Tao Yang, et al. "An Artificial Neural Network Model for Monitoring Real-Time Parameters and Detecting Early Warnings in Induced Draft Fan." In ASME 2018 13th International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/msec2018-6370.

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This paper describes a method to monitor real time parameters and detect early warnings in induced draft fan (ID FAN). An artificial neural network (ANN) model based on cross-relationships among operating parameters was established. In particular, this paper adopted the pre-training of Restricted Boltzmann machines (RBM) and analyzed the training errors of model. A new approach was proposed to monitor parameters by predicted value of model and distribution law of training error, and the reasonable range of each parameter was defined to detect the early warnings in real time. Combining the hist

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Phan, NhatHai, Dejing Dou, Brigitte Piniewski, and David Kil. "Social Restricted Boltzmann Machine." In ASONAM '15: Advances in Social Networks Analysis and Mining 2015. ACM, 2015. http://dx.doi.org/10.1145/2808797.2809307.

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Kuchhold, Markus, Maik Simon, and Thomas Sikora. "Restricted Boltzmann Machine Image Compression." In 2018 Picture Coding Symposium (PCS). IEEE, 2018. http://dx.doi.org/10.1109/pcs.2018.8456279.

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Guanglei Qi, Yanfeng Sun, Junbin Gao, Yongli Hu, and Jinghua Li. "Matrix Variate Restricted Boltzmann Machine." In 2016 International Joint Conference on Neural Networks (IJCNN). IEEE, 2016. http://dx.doi.org/10.1109/ijcnn.2016.7727225.

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Nagatani, Koki, and Masafumi Hagiwara. "Restricted Boltzmann machine associative memory." In 2014 International Joint Conference on Neural Networks (IJCNN). IEEE, 2014. http://dx.doi.org/10.1109/ijcnn.2014.6889573.

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Shijing Dong and Jinqing Qi. "Restricted Boltzmann Machine for saliency detection." In 2015 IEEE 7th International Conference on Awareness Science and Technology (iCAST). IEEE, 2015. http://dx.doi.org/10.1109/icawst.2015.7314014.

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