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Статті в журналах з теми "Restricted Boltzmann Machine (RBM)"
Côté, Marc-Alexandre, and Hugo Larochelle. "An Infinite Restricted Boltzmann Machine." Neural Computation 28, no. 7 (July 2016): 1265–88. http://dx.doi.org/10.1162/neco_a_00848.
Повний текст джерелаLi, 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.
Повний текст джерелаZhang, Jingshuai, Yuanxin Ouyang, Weizhu Xie, Wenge Rong, and Zhang Xiong. "Context-aware restricted Boltzmann machine meets collaborative filtering." Online Information Review 44, no. 2 (November 13, 2018): 455–76. http://dx.doi.org/10.1108/oir-02-2017-0069.
Повний текст джерелаWei, Jiangshu, Jiancheng Lv, and Zhang Yi. "A New Sparse Restricted Boltzmann Machine." International Journal of Pattern Recognition and Artificial Intelligence 33, no. 10 (September 2019): 1951004. http://dx.doi.org/10.1142/s0218001419510042.
Повний текст джерелаAoki, Ken-Ichi, and Tamao Kobayashi. "Restricted Boltzmann machines for the long range Ising models." Modern Physics Letters B 30, no. 34 (December 8, 2016): 1650401. http://dx.doi.org/10.1142/s0217984916504017.
Повний текст джерелаDewi, 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 (January 19, 2021): 417–32. http://dx.doi.org/10.1142/s2196888821500184.
Повний текст джерелаWang, 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.
Повний текст джерелаHoyle, David C. "Replica analysis of the lattice-gas restricted Boltzmann machine partition function." Journal of Statistical Mechanics: Theory and Experiment 2023, no. 1 (January 1, 2023): 013301. http://dx.doi.org/10.1088/1742-5468/acaf83.
Повний текст джерелаRully 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 (December 5, 2019): 58–64. http://dx.doi.org/10.52985/insyst.v1i2.84.
Повний текст джерелаBao, 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.
Повний текст джерелаДисертації з теми "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.
Повний текст джерелаKlarna använder en logistisk regression för att estimera sannolikheten att en e-handelskund inte kommer att betala sina fakturor efter att ha givits kredit. Den logistiska regressionen är en linjär modell och kan därför inte upptäcka icke-linjäriteter i datan. Målet med detta projekt har varit att utveckla ett program som kan användas för att hitta lämpliga icke-linjära interaktionsvariabler. Genom att införa dessa i den logistiska regressionen blir det möjligt att upptäcka icke-linjäriteter i datan och därmed förbättra sannolikhetsestimaten. Det utvecklade programmet använder Restricted Boltzmann Machines, en typ av oövervakat neuralt nätverk, vars dolda noder kan användas för att hitta datans distribution. Genom att använda de dolda noderna i den logistiska regressionen är det möjligt att se vilka delar av distributionen som är viktigast i sannolikhetsestimaten. Innehållet i de dolda noderna, som motsvarar olika delar av datadistributionen, kan användas för att hitta lämpliga interaktionsvariabler. Det var möjligt att hitta datans distribution genom att använda en Restricted Boltzmann Machine och dess dolda noder förbättrade sannolikhetsestimaten från den logistiska regressionen. De dolda noderna kunde användas för att skapa interaktionsvariabler som förbättrar Klarnas interna kreditriskmodeller.
Moody, John Matali. "Process monitoring with restricted Boltzmann machines." Thesis, Stellenbosch : Stellenbosch University, 2014. http://hdl.handle.net/10019.1/86467.
Повний текст джерелаENGLISH 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 approach for process monitoring. However, until very recently, these multiple layer neural networks have been difficult to train and have therefore not been used to any significant extent in process monitoring. With newly proposed algorithms based on the pre-training of the layers of the neural networks, it is now possible to train neural networks with very complex structures, i.e. deep neural networks. These neural networks can be used as auto encoders to extract features from high dimensional data. In this study, the application of deep auto encoders in the form of Restricted Boltzmann machines (RBM) to the extraction of features from process data is considered. These networks have mostly been used for data visualization to date and have not been applied in the context of fault diagnosis or process monitoring as yet. The objective of this investigation is therefore to assess the feasibility of using Restricted Boltzmann machines in various fault detection schemes. The use of RBM in process monitoring schemes will be discussed, together with the application of these models in automated control frameworks.
AFRIKAANSE OPSOMMING: Prosesmonitering en fout diagnose word gebruik om abnormale gebeure in prosesse op te spoor. Die vroeë opsporing van sulke gebeure of foute is noodsaaklik vir deurlopende verbetering van prosesse. Alhoewel hoofkomponent-analise en parsiële kleinste kwadrate wyd gebruik word vir prosesmonitering en fout diagnose in die metallurgiese industrieë, is hierdie modelle lineêr in beginsel; nie-lineêre benaderings behoort meer kompakte en insiggewende modelle te voorsien. Die gebruik van outo-assosiatiewe neurale netwerke of outokodeerders bied 'n beginsel gebaseerder benadering om dit te bereik. Hierdie veelvoudige laag neurale netwerke was egter tot onlangs moeilik om op te lei en is dus nie tot ʼn beduidende mate in die prosesmonitering gebruik nie. Nuwe, voorgestelde algoritmes, gebaseer op voorafopleiding van die lae van die neurale netwerke, maak dit nou moontlik om neurale netwerke met baie ingewikkelde strukture, d.w.s. diep neurale netwerke, op te lei. Hierdie neurale netwerke kan gebruik word as outokodeerders om kenmerke van hoë-dimensionele data te onttrek. In hierdie studie word die toepassing van diep outokodeerders in die vorm van Beperkte Boltzmann Masjiene vir die onttrekking van kenmerke van proses data oorweeg. Tot dusver is hierdie netwerke meestal vir data visualisering gebruik en dit is nog nie toegepas in die konteks van fout diagnose of prosesmonitering nie. Die doel van hierdie ondersoek is dus om die haalbaarheid van die gebruik van Beperkte Boltzmann Masjiene in verskeie foutopsporingskemas te assesseer. Die gebruik van Beperkte Boltzmann Masjiene se eienskappe in prosesmoniteringskemas sal bespreek word, tesame met die toepassing van hierdie modelle in outomatiese beheer raamwerke.
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.
Повний текст джерелаVrábel, 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.
Повний текст джерелаSvoboda, 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.
Повний текст джерелаFredriksson, 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.
Повний текст джерелаIn this thesis, we introduce restricted Boltzmann machines (RBMs) as a recommendation model in the context of venture capital. A network of connections is used as a proxy for investors’ preferences of companies. The main focus of the thesis is to investigate how RBMs can be implemented on a network of connections and investigate if conditional information can be used to boost RBMs. The network of connections is created by using board composition data of Swedish companies. For the network, RBMs are implemented with and without companies’ place of origin as conditional data, respectively. The RBMs are evaluated by their learning abilities and their ability to recreate withheld connections. The findings show that RBMs perform poorly when used to recreate withheld connections but can be tuned to acquire good learning abilities. Adding place of origin as conditional information improves the model significantly and show potential as a recommendation model, both with respect to learning abilities and the ability to recreate withheld connections.
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.
Повний текст джерелаTubiana, Jérôme. "Restricted Boltzmann machines : from compositional representations to protein sequence analysis." Thesis, Paris Sciences et Lettres (ComUE), 2018. http://www.theses.fr/2018PSLEE039/document.
Повний текст джерелаRestricted Boltzmann machines (RBM) are graphical models that learn jointly a probability distribution and a representation of data. Despite their simple architecture, they can learn very well complex data distributions such the handwritten digits data base MNIST. Moreover, they are empirically known to learn compositional representations of data, i.e. representations that effectively decompose configurations into their constitutive parts. However, not all variants of RBM perform equally well, and little theoretical arguments exist for these empirical observations. In the first part of this thesis, we ask how come such a simple model can learn such complex probability distributions and representations. By analyzing an ensemble of RBM with random weights using the replica method, we have characterised a compositional regime for RBM, and shown under which conditions (statistics of weights, choice of transfer function) it can and cannot arise. Both qualitative and quantitative predictions obtained with our theoretical analysis are in agreement with observations from RBM trained on real data. In a second part, we present an application of RBM to protein sequence analysis and design. Owe to their large size, it is very difficult to run physical simulations of proteins, and to predict their structure and function. It is however possible to infer information about a protein structure from the way its sequence varies across organisms. For instance, Boltzmann Machines can leverage correlations of mutations to predict spatial proximity of the sequence amino-acids. Here, we have shown on several synthetic and real protein families that provided a compositional regime is enforced, RBM can go beyond structure and extract extended motifs of coevolving amino-acids that reflect phylogenic, structural and functional constraints within proteins. Moreover, RBM can be used to design new protein sequences with putative functional properties by recombining these motifs at will. Lastly, we have designed new training algorithms and model parametrizations that significantly improve RBM generative performance, to the point where it can compete with state-of-the-art generative models such as Generative Adversarial Networks or Variational Autoencoders on medium-scale data
Spiliopoulou, Athina. "Probabilistic models for melodic sequences." Thesis, University of Edinburgh, 2013. http://hdl.handle.net/1842/8876.
Повний текст джерелаde, 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.
Повний текст джерелаЧастини книг з теми "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, 163–74. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46182-3_14.
Повний текст джерелаZięba, Maciej, Jakub M. Tomczak, and Adam Gonczarek. "RBM-SMOTE: Restricted Boltzmann Machines for Synthetic Minority Oversampling Technique." In Intelligent Information and Database Systems, 377–86. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15702-3_37.
Повний текст джерелаJo, Taeho. "Restricted Boltzmann Machine." In Deep Learning Foundations, 277–302. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-32879-4_11.
Повний текст джерелаWang, Hao, Dejing Dou, and Daniel Lowd. "Ontology-Based Deep Restricted Boltzmann Machine." In Lecture Notes in Computer Science, 431–45. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-44403-1_27.
Повний текст джерелаRani, 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, 477–89. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5421-6_47.
Повний текст джерелаHuang, Haiping. "Statistical Mechanics of Restricted Boltzmann Machine." In Statistical Mechanics of Neural Networks, 111–32. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-7570-6_10.
Повний текст джерелаCherla, 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, 111–19. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68612-7_13.
Повний текст джерелаLiu, Yongqi, Qiuli Tong, Zhao Du, and Lantao Hu. "Content-Boosted Restricted Boltzmann Machine for Recommendation." In Artificial Neural Networks and Machine Learning – ICANN 2014, 773–80. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11179-7_97.
Повний текст джерелаLi, Jinghua, Pengyu Tian, Dehui Kong, Lichun Wang, Shaofan Wang, and Baocai Yin. "Matrix-Variate Restricted Boltzmann Machine Classification Model." In Simulation Tools and Techniques, 486–97. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32216-8_47.
Повний текст джерелаKuremoto, Takashi, Shinsuke Kimura, Kunikazu Kobayashi, and Masanao Obayashi. "Time Series Forecasting Using Restricted Boltzmann Machine." In Communications in Computer and Information Science, 17–22. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31837-5_3.
Повний текст джерелаТези доповідей конференцій з теми "Restricted Boltzmann Machine (RBM)"
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.
Повний текст джерелаPassos, 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.
Повний текст джерелаHu, Di, Gang Chen, Tao Yang, Cheng Zhang, Ziwen Wang, Qianming Chen, and Bing Li. "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.
Повний текст джерелаPhan, NhatHai, Dejing Dou, Brigitte Piniewski, and David Kil. "Social Restricted Boltzmann Machine." In ASONAM '15: Advances in Social Networks Analysis and Mining 2015. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2808797.2809307.
Повний текст джерелаKuchhold, 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.
Повний текст джерелаGuanglei 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.
Повний текст джерелаNagatani, 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.
Повний текст джерелаShijing 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.
Повний текст джерелаJiang, Yun, Jize Xiao, Xi Liu, and Jinquan Hou. "A removing redundancy Restricted Boltzmann Machine." In 2018 Tenth International Conference on Advanced Computational Intelligence (ICACI ). IEEE, 2018. http://dx.doi.org/10.1109/icaci.2018.8377580.
Повний текст джерелаKhan, Umair, Pooyan Safari, and Javier Hernando. "Restricted Boltzmann Machine Vectors for Speaker Clustering." In IberSPEECH 2018. ISCA: ISCA, 2018. http://dx.doi.org/10.21437/iberspeech.2018-3.
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