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Автор: Grafiati
Опубліковано: 6 вересня 2023

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Статті в журналах з теми "Restricted Boltzmann Machine (RBM)"

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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.

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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 units during learning. We empirically study the behavior of this infinite RBM, showing that its performance is competitive to that of the RBM, while not requiring the tuning of a hidden layer size.
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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 accuracy is very close to the original RBM model.
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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 (November 13, 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 a context-aware RBM and stack the context-aware RBM to construct DBNs for recommendations. Findings The experiments on the MovieLens data sets show that the user occupation-aware RBM outperforms other CF models, and combinations of different context-aware models by mutual information can obtain better accuracy. Moreover, the context-aware DBNs model is superior to baseline methods, indicating that deep networks have more qualifications for extracting preference features. Originality/value To improve recommendation accuracy through modeling contextual information, the authors propose context-aware CF approaches based on the RBM. Additionally, the authors attempt to introduce hybrid weights based on information entropy to combine context-aware models. Furthermore, the authors stack the RBM to construct a context-aware multilayer network model. The results of the experiments not only convey that the context-aware RBM has potential in terms of contextual information but also demonstrate that the combination method, the hybrid recommendation and the multilayer neural network extension have significant benefits for the recommendation quality.
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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 (September 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 learned features can better reflect the intrinsic features of data. Simulations show that compared with SRBM, ESRBM has smaller reconstruction error and lower computational complexity, and that for supervised learning classification, ESRBM obtains higher accuracy rates than SRBM, classification RBM, and Softmax classifier.
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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 (December 8, 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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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 (January 19, 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 layer (50–750 layers). Then, we compare and analyze the classification performance in depth of regular RBM use RBM () function, classification RBM use stackRBM() function, and Deep Belief Network (DBN) use DBN() function with the different hidden layer. As a result, Stacking RBM and DBN could improve our classification performance compared to regular RBM.
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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 fast Gibbs sampling (FGS) algorithm to learn the RBM by adding accelerated weights and adjustment coefficient. An important link based on Gibbs sampling theory was established between the update of the network weights and mixing rate of Gibbs sampling chain. The proposed FGS method was used to accelerate the mixing rate of Gibbs sampling chain by adding accelerated weights and adjustment coefficients. To further validate the FGS method, numerous experiments were performed to facilitate comparisons with the classical RBM algorithm. The experiments involved learning the RBM based on standard data. The results showed that the proposed FGS method outperformed the CD, PCD, PT5, PT10, and DGS algorithms, particularly with respect to the handwriting database. The findings of our study suggest the potential applications of FGS to real-world problems and demonstrate that the proposed method can build an improved RBM for classification.
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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 (January 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 lattice-gas RBM compared to that of the Ising RBM form usually studied. We obtain explicit expressions for the expectation and variance of the lattice-gas RBM log partition function per node in the thermodynamic limit. We also obtain explicit expressions for the leading order finite size correction to the expected log partition function per node, and the threshold for the stability of the replica-symmetric approximation. We show that the stability threshold of the replica-symmetric approximation is equivalent, in the thermodynamic limit, to the stability threshold of a recent message-passing algorithm used to construct a mean-field Bethe approximation to the RBM free energy. Given the replica-symmetry assumption breaks down as the level of disorder in the spin-spin couplings increases, we obtain asymptotic expansions, in terms of the variance controlling this disorder, for the replica-symmetric log partition function and the replica-symmetric stability threshold. We confirm the various results derived using simulation.
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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 (December 5, 2019): 58–64. http://dx.doi.org/10.52985/insyst.v1i2.84.

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Анотація:
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, bi-gram, tri-gram, kata benda yang tepat, kemiripan antar kalimat, huruf besar. Nilai fitur setiap kalimat dihitung. Nilai fitur yang dihasilkan ditingkatkan dengan menggunakan metode Restricted Boltzmann Machine (RBM) agar ringkasan yang dihasilkan lebih akurat. Untuk proses pengujian dalam penelitian ini menggunakan ROUGE-1. Hasil yang diperoleh dalam penelitian yaitu dengan menggunakan learning rate 0.06 menghasilkan recall, precision dan f-measure tertinggi yakni 0.744, 0.611 dan 0.669. Selain itu, semakin besar nilai compression rate yang digunakan maka hasil recall, precision dan f-measure yang dihasilkan akan semakin tinggi. Hasil peringkasan teks dengan menggunakan RBM memiliki nilai recall lebih tinggi 2.1%, precision lebih tinggi 1.6% dan f-measure lebih tinggi 1.8% daripada hasil peringkasan teks tanpa RBM. Hal ini menunjukkan bahwa peringkasan teks dengan menggunakan RBM hasilnya lebih baik daripada peringkasan teks tanpa RBM.
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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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Анотація:
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 probability of the RBM is designed as the sampling probability model of the estimation of distribution algorithm (EDA) and is updated dynamically along with the update of the dominant subsets. Second, according to the energy function of the RBM, a fitness surrogate is developed to approximate the expensive individual fitness evaluations and participates in the evolutionary process to reduce the computational cost. Finally, model management is developed to train and update the RBM model with newly dominant solutions. A comparison of the proposed algorithm with several state-of-the-art surrogate-assisted evolutionary algorithms demonstrates that the proposed algorithm effectively and efficiently solves complex optimization problems with smaller computational cost.
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Більше джерел

Дисертації з теми "Restricted Boltzmann Machine (RBM)"

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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 which nodes that have the greatest impact on the probability of default estimates. The contents of the hidden nodes, corresponding to different parts of the data distribution, can be used to find suitable interaction-variables which will allow the modelling of non-linearities. It was possible to find the data distribution using the Restricted Boltzmann Machine and adding its hidden nodes to the logistic regression improved the model's ability to predict the probability of default. The hidden nodes could be used to create interaction-variables which improve Klarna's internal models used for credit risk estimates.
Klarna 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.
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2

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.
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 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.
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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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4

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 Machine (RBM). Vhodnosť použitia RBM na spracovanie spektroskopických dát je diskutovaná a preukázaná na znížení dimenzie týchto dát. Výsledky sú porovnané s bežne používanou Metódou Hlavných Komponent (PCA), spolu so zhodnotením prístupu a možnosťami ďalšieho zlepšovania.
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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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6

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 ursprungsort. Vardera RBM-modell undersöks genom att utvärdera dess inlärningsförmåga samt förmåga att återskapa manuellt gömda relationer. Resultatet påvisar att RBM-modellerna har en bristfällig förmåga att återskapa borttagna relationer, dock noteras god inlärningsförmåga. Genom att addera ursprungsort som extra information förbättras modellerna markant och god potential som rekommendationsmodell går att urskilja, både med avseende på inlärningsförmåga samt förmåga att återskapa gömda relationer.
In 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.
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7

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. Subsequently, we look closer at the likelihood function itself, in an attempt to uncover more rigid details about its curvature, and the nature of its convexity. As we present results of our investigation, we discuss the ndings, before suggesting possible future directions to take, improvements to make and aspects to further investigate. We conclude that the standard, analytically exact restricted Boltzmann machine algorithm is convex up to certain permutations of the parameters, when initialized within reasonable ranges of parameter values, and given that the strength of connectivity in the underlying model is within a specied range. Additionaly, for strengths of connectivity, the distribution of Hessian eigenvalues of the likelihood function, as a funtion of the distance to a peak, may be stable both within and across network sizes.
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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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Анотація:
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 ne sont pas aussi performantes les unes que les autres, et il n’y a pas d’explication théorique justifiant ces observations empiriques. Dans la première partie de ma thèse, nous avons cherché à comprendre comment un modèle si simple peut produire des distributions de probabilité si complexes. Pour cela, nous avons analysé un modèle simplifié de RBM à poids aléatoires à l’aide de la méthode des répliques. Nous avons pu caractériser théoriquement un régime compositionnel pour les RBM, et montré sous quelles conditions (statistique des poids, choix de la fonction de transfert) ce régime peut ou ne peut pas émerger. Les prédictions qualitatives et quantitatives de cette analyse théorique sont en accord avec les observations réalisées sur des RBM entraînées sur des données réelles. Nous avons ensuite appliqué les RBM à l’analyse et à la conception de séquences de protéines. De part leur grande taille, il est en effet très difficile de simuler physiquement les protéines, et donc de prédire leur structure et leur fonction. Il est cependant possible d’obtenir des informations sur la structure d’une protéine en étudiant la façon dont sa séquence varie selon les organismes. Par exemple, deux sites présentant des corrélations de mutations importantes sont souvent physiquement proches sur la structure. A l’aide de modèles graphiques tels que les Machine de Boltzmann, on peut exploiter ces signaux pour prédire la proximité spatiale des acides-aminés d’une séquence. Dans le même esprit, nous avons montré sur plusieurs familles de protéines que les RBM peuvent aller au-delà de la structure, et extraire des motifs étendus d’acides aminés en coévolution qui reflètent les contraintes phylogénétiques, structurelles et fonctionnelles des protéines. De plus, on peut utiliser les RBM pour concevoir de nouvelles séquences avec des propriétés fonctionnelles putatives par recombinaison de ces motifs. Enfin, nous avons développé de nouveaux algorithmes d’entraînement et des nouvelles formes paramétriques qui améliorent significativement la performance générative des RBM. Ces améliorations les rendent compétitives avec l’état de l’art des modèles génératifs tels que les réseaux génératifs adversariaux ou les auto-encodeurs variationnels pour des données de taille intermédiaires
Restricted 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
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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 without the need to incorporate significant domain-specifc knowledge. At all stages we avoid making assumptions explicit to music and consider models that can can be readily applied in different music genres and can easily be adapted for other sequential data domains. We develop the Dirichlet Variable-Length Markov Model (Dirichlet-VMM), a Bayesian formulation of the Variable-Length Markov Model (VMM), where smoothing is performed in a systematic probabilistic manner. The model is a general-purpose, dictionary-based predictor with a formal smoothing technique and is shown to perform significantly better than the standard VMM in melody modelling. Motivated by the ability of the Restricted Boltzmann Machine (RBM) to extract high quality latent features in an unsupervised manner, we next develop the Time-Convolutional Restricted Boltzmann Machine (TC-RBM), a novel adaptation of the Convolutional RBM for modelling sequential data. We show that the TC-RBM learns descriptive musical features such as chords, octaves and typical melody movement patterns. To deal with the non-stationarity of music, we develop the Variable-gram Topic model, which employs the Dirichlet-VMM for the parametrisation of the topic distributions. The Dirichlet-VMM models the local temporal structure, while the latent topics represent di erent music regimes. The model does not make any assumptions explicit to music, but it is particularly suitable in this context, as it couples the latent topic formalism with an expressive model of contextual information.
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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 either RBMs, autoencoders or denoising autoencoders in deep networks. Important characteristics are tested, such as the robustness to noise, the influence on training of the availability of data and the tendency to overtrain. The author has then dedicated part of the thesis to study how the three deep networks in exam form their deep internal representations and how similar these can be to each other. In result of this, a novel approach for the evaluation of internal representations is presented with the name of F-Mapping. Results are reported and discussed.
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Частини книг з теми "Restricted Boltzmann Machine (RBM)"

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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.

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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, 377–86. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15702-3_37.

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3

Jo, 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.

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Wang, 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.

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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, 477–89. Singapore: 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, 111–32. Singapore: 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, 111–19. Cham: 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, 773–80. Cham: 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, 486–97. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32216-8_47.

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Kuremoto, 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.

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Тези доповідей конференцій з теми "Restricted Boltzmann Machine (RBM)"

1

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 such a process. Thereby, this work contributes to studies concerning different training algorithms for RBMs, as well as its variants Deep Belief Networks and Deep Boltzmann Machines. Further, the work covers the application of meta-heuristic methods concerning a proper fine-tune of these techniques. Moreover, the validation of the model is presented in the context of image reconstruction and unsupervised feature learning. In general, we present different approaches to training these techniques, as well as the evaluation of meta-heuristic methods for fine-tuning parameters, and its main contributions are: (i) temperature parameter introduction in DBM formulation, (ii) DBM using adaptive temperature, (iii) DBM meta-parameter optimization through meta-heuristic techniques, and (iv) infinity Restricted Boltzmann Machine (iRBM) meta-parameters optimization through meta-heuristic techniques.
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Hu, 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.

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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 historical operational data of the No. 1 induced draft fan of No. 3 generating unit in Shajiao C Power Plant in China, this work used MATLAB to verify and analyze the proposed method. The numerical examples shown that the proposed method has better detection performance than the fixed upper and lower limits in the safety instrumented system (SIS). Moreover, this work can expand to other machinery that could be used in manufacturing easily.
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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. New York, NY, USA: 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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Jiang, 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.

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Khan, 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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