Bibliografías temáticas / Machines de Boltzmann restreintes

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Literatura académica sobre el tema "Machines de Boltzmann restreintes"

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Publicado: 25 de mayo de 2024

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Artículos de revistas sobre el tema "Machines de Boltzmann restreintes"

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Apolloni, B., A. Bertoni, P. Campadelli, and D. de Falco. "Asymmetric Boltzmann machines." Biological Cybernetics 66, no. 1 (1991): 61–70. http://dx.doi.org/10.1007/bf00196453.

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Lu, Wenhao, Chi-Sing Leung, and John Sum. "Analysis on Noisy Boltzmann Machines and Noisy Restricted Boltzmann Machines." IEEE Access 9 (2021): 112955–65. http://dx.doi.org/10.1109/access.2021.3102275.

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Livesey, M. "Clamping in Boltzmann machines." IEEE Transactions on Neural Networks 2, no. 1 (1991): 143–48. http://dx.doi.org/10.1109/72.80301.

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Fischer, Asja. "Training Restricted Boltzmann Machines." KI - Künstliche Intelligenz 29, no. 4 (2015): 441–44. http://dx.doi.org/10.1007/s13218-015-0371-2.

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Bojnordi, Mahdi Nazm, and Engin Ipek. "The Memristive Boltzmann Machines." IEEE Micro 37, no. 3 (2017): 22–29. http://dx.doi.org/10.1109/mm.2017.53.

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Liu, Jeremy, Ke-Thia Yao, and Federico Spedalieri. "Dynamic Topology Reconfiguration of Boltzmann Machines on Quantum Annealers." Entropy 22, no. 11 (2020): 1202. http://dx.doi.org/10.3390/e22111202.

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Boltzmann machines have useful roles in deep learning applications, such as generative data modeling, initializing weights for other types of networks, or extracting efficient representations from high-dimensional data. Most Boltzmann machines use restricted topologies that exclude looping connectivity, as such connectivity creates complex distributions that are difficult to sample. We have used an open-system quantum annealer to sample from complex distributions and implement Boltzmann machines with looping connectivity. Further, we have created policies mapping Boltzmann machine variables to
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Luo, Heng, Ruimin Shen, Changyong Niu, and Carsten Ullrich. "Sparse Group Restricted Boltzmann Machines." Proceedings of the AAAI Conference on Artificial Intelligence 25, no. 1 (2011): 429–34. http://dx.doi.org/10.1609/aaai.v25i1.7923.

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Since learning in Boltzmann machines is typically quite slow, there is a need to restrict connections within hidden layers. However, theresulting states of hidden units exhibit statistical dependencies. Based on this observation, we propose using l1/l2 regularization upon the activation probabilities of hidden units in restricted Boltzmann machines to capture the local dependencies among hidden units. This regularization not only encourages hidden units of many groups to be inactive given observed data but also makes hidden units within a group compete with each other for modeling observed dat
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Decelle, Aurélien, and Cyril Furtlehner. "Gaussian-spherical restricted Boltzmann machines." Journal of Physics A: Mathematical and Theoretical 53, no. 18 (2020): 184002. http://dx.doi.org/10.1088/1751-8121/ab79f3.

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Apolloni, B., and D. de Falco. "Learning by parallel Boltzmann machines." IEEE Transactions on Information Theory 37, no. 4 (1991): 1162–65. http://dx.doi.org/10.1109/18.87009.

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d'Anjou, A., M. Grana, F. J. Torrealdea, and M. C. Hernandez. "Solving satisfiability via Boltzmann machines." IEEE Transactions on Pattern Analysis and Machine Intelligence 15, no. 5 (1993): 514–21. http://dx.doi.org/10.1109/34.211473.

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Tesis sobre el tema "Machines de Boltzmann restreintes"

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Fissore, Giancarlo. "Generative modeling : statistical physics of Restricted Boltzmann Machines, learning with missing information and scalable training of Linear Flows." Electronic Thesis or Diss., université Paris-Saclay, 2022. http://www.theses.fr/2022UPASG028.

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Les modèles de réseaux neuronaux capables d'approximer et d'échantillonner des distributions de probabilité à haute dimension sont connus sous le nom de modèles génératifs. Ces dernières années, cette classe de modèles a fait l'objet d'une attention particulière en raison de son potentiel à apprendre automatiquement des représentations significatives de la grande quantité de données que nous produisons et consommons quotidiennement. Cette thèse présente des résultats théoriques et algorithmiques relatifs aux modèles génératifs et elle est divisée en deux parties. Dans la première partie, nous
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Hasasneh, Ahmad. "Robot semantic place recognition based on deep belief networks and a direct use of tiny images." Phd thesis, Université Paris Sud - Paris XI, 2012. http://tel.archives-ouvertes.fr/tel-00960289.

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Usually, human beings are able to quickly distinguish between different places, solely from their visual appearance. This is due to the fact that they can organize their space as composed of discrete units. These units, called ''semantic places'', are characterized by their spatial extend and their functional unity. Such a semantic category can thus be used as contextual information which fosters object detection and recognition. Recent works in semantic place recognition seek to endow the robot with similar capabilities. Contrary to classical localization and mapping works, this problem is us
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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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TICKNOR, ANTHONY JAMES. "OPTICAL COMPUTING IN BOLTZMANN MACHINES." Diss., The University of Arizona, 1987. http://hdl.handle.net/10150/184169.

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This dissertation covers theoretical and experimental work on applying optical processing techniques ot the operation of a Boltzmann machine. A Boltzmann machine is a processor that solves a problem by iteratively optimizing an estimate of the solution. The optimization is done by finding a minimum of an energy surface over the solution space. The energy function is designed to consider not only data but also a priori information about the problem to assist the optimization. The dissertation first establishes a generic line-of-approach for designing an algorithmic optical computer that might s
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Camilli, Francesco. "Statistical mechanics perspectives on Boltzmann machines." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/19302/.

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La tesi contiene un approccio rigoroso alla meccanica statistica dei sistemi disordinati con particolare attenzione a modelli di campo medio. Il punto di partenza è un’introduzione al modello Curie-Weiss in cui si presentano sia risultati classici sia una nuova proprietà di stabilità per cambio di normalizzazione. Il modello è stato risolto per interazioni ferromagnetiche ed antiferromagnetiche. Una volta introdotti gli strumenti fondamentali, si passa al modello di Sherringtone Kirkpatrick, in cui le interazioni sono estratte da una gaussiana standard ed indipendenti. Si provano l’esis
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CRUZ, FELIPE JOAO PONTES DA. "RECOMMENDER SYSTEMS USING RESTRICTED BOLTZMANN MACHINES." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2016. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=30285@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO<br>COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>PROGRAMA DE EXCELENCIA ACADEMICA<br>Sistemas de recomendação aparecem em diversos domínios do mundo real. Vários modelos foram propostos para o problema de predição de entradas faltantes em um conjunto de dados. Duas das abordagens mais comuns são filtragem colaborativa baseada em similaridade e modelos de fatores latentes. Uma alternativa, mais recente, foi proposta por Salakhutdinov em 2007, usando máquinas de Boltzmann restritas, ou RBMs. Esse modelo se encaixa na família de
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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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LACAILLE, JEROME. "Machines de boltzmann. Theorie et applications." Paris 11, 1992. http://www.theses.fr/1992PA112213.

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Cette these presente les machines de boltzmann en trois grandes parties. On detaille tout d'abord le formalisme et l'algorithmique de ces machines d'un point de vue theorique. L'accent sera mis sur les architectures paralleles et asymetriques. Une application de ce type de reseau est presentee dans un second temps. Il s'agit d'un detecteur de contours sur des photographies en niveau de gris. La derniere partie decrit une implementation logicielle des machines de boltzmann synchrones asymetriques. On propose un simulateur et un interpreteur muni d'une notice d'utilisation. Cette derniere partie
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Swersky, Kevin. "Inductive principles for learning Restricted Boltzmann Machines." Thesis, University of British Columbia, 2010. http://hdl.handle.net/2429/27816.

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We explore the training and usage of the Restricted Boltzmann Machine for unsupervised feature extraction. We investigate the many different aspects involved in their training, and by applying the concept of iterate averaging we show that it is possible to greatly improve on state of the art algorithms. We also derive estimators based on the principles of pseudo-likelihood, ratio matching, and score matching, and we test them empirically against contrastive divergence, and stochastic maximum likelihood (also known as persistent contrastive divergence). Our results show that ratio matching and
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Farguell, Matesanz Enric. "A new approach to Decimation in High Order Boltzmann Machines." Doctoral thesis, Universitat Ramon Llull, 2011. http://hdl.handle.net/10803/9155.

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La Màquina de Boltzmann (MB) és una xarxa neuronal estocàstica amb l'habilitat tant d'aprendre com d'extrapolar distribucions de probabilitat. Malgrat això, mai ha arribat a ser tant emprada com d'altres models de xarxa neuronal, com ara el perceptró, degut a la complexitat tan del procés de simulació com d'aprenentatge: les quantitats que es necessiten al llarg del procés d'aprenentatge són normalment estimades mitjançant tècniques Monte Carlo (MC), a través de l'algorisme del Temprat Simulat (SA). Això ha portat a una situació on la MB és més ben aviat considerada o bé com una extensió de la
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Libros sobre el tema "Machines de Boltzmann restreintes"

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P, Coughlin James. Neural computation in Hopfield networks and Boltzmann machines. University of Delaware Press, 1995.

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Williams, Chris. Using deterministic Boltzmann machines to discriminate temporally distorted strings. National Library of Canada, 1990.

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Williams, Chris. Using deterministic Boltzmann machines to discriminate temporally distorted strings. University, Dept. of Computer Science, 1990.

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Luttrell, Stephen P. The implications of Boltzmann-type machines for SAR data processing: A preliminary survey. Procurement Executive, Ministry of Defence, RSRE, 1985.

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Jan, Korst, ed. Simulated annealing and Boltzmann machines: A stochastic approach to combinatorial optimization and neural computing. Wiley, 1989.

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Azencott, Robert. Boltzmann Machines, Gibbs Fields, and Artificial Vision. Perseus Books (Sd), 1999.

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P, Coughlin James, and Robert H. Baran. Neural Computation in Hopfield Networks and Boltzmann Machines. Rowman & Littlefield Publishers, Incorporated, 1995.

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Masters, Timothy. Deep Belief Nets in C++ and CUDA C: Volume 1: Restricted Boltzmann Machines and Supervised Feedforward Networks. Apress, 2018.

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Tiwari, Sandip. Information mechanics. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198759874.003.0001.

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Information is physical, so its manipulation through devices is subject to its own mechanics: the science and engineering of behavioral description, which is intermingled with classical, quantum and statistical mechanics principles. This chapter is a unification of these principles and physical laws with their implications for nanoscale. Ideas of state machines, Church-Turing thesis and its embodiment in various state machines, probabilities, Bayesian principles and entropy in its various forms (Shannon, Boltzmann, von Neumann, algorithmic) with an eye on the principle of maximum entropy as an
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Capítulos de libros sobre el tema "Machines de Boltzmann restreintes"

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Du, Ke-Lin, and M. N. S. Swamy. "Boltzmann Machines." In Neural Networks and Statistical Learning. Springer London, 2019. http://dx.doi.org/10.1007/978-1-4471-7452-3_23.

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Alla, Sridhar, and Suman Kalyan Adari. "Boltzmann Machines." In Beginning Anomaly Detection Using Python-Based Deep Learning. Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-5177-5_5.

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Spieksma, F. C. R. "Boltzmann Machines." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/bfb0027026.

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Munro, Paul, Hannu Toivonen, Geoffrey I. Webb, et al. "Boltzmann Machines." In Encyclopedia of Machine Learning. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_83.

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Yan, Wei Qi. "Boltzmann Machines." In Texts in Computer Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61081-4_7.

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Hinton, Geoffrey. "Boltzmann Machines." In Encyclopedia of Machine Learning and Data Mining. Springer US, 2014. http://dx.doi.org/10.1007/978-1-4899-7502-7_31-1.

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Hinton, Geoffrey. "Boltzmann Machines." In Encyclopedia of Machine Learning and Data Mining. Springer US, 2017. http://dx.doi.org/10.1007/978-1-4899-7687-1_31.

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Masters, Timothy. "Restricted Boltzmann Machines." In Deep Belief Nets in C++ and CUDA C: Volume 1. Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3591-1_3.

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Poole, William, Andrés Ortiz-Muñoz, Abhishek Behera, et al. "Chemical Boltzmann Machines." In Lecture Notes in Computer Science. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66799-7_14.

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Aggarwal, Charu C. "Restricted Boltzmann Machines." In Neural Networks and Deep Learning. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-94463-0_6.

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Actas de conferencias sobre el tema "Machines de Boltzmann restreintes"

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Ticknor, Anthony J., and Harrison H. Barrett. "Optical Boltzmann machines." In OSA Annual Meeting. Optica Publishing Group, 1985. http://dx.doi.org/10.1364/oam.1985.tuy3.

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An optical Boltzmann machine is a highly parallel computing module for inverting matrix equations expressed g = Af. A Monte Carlo procedure is used, being more efficient with large matrices and because the transform matrix A often has no true inverse. Since the operation is not determinstic, the resultant data f ^ will only be an estimate if the data f, although confidence that it is the best possible estimate of f can be made very high. An energy function E of the estimate f ^ is defined with minimum at the best estimate; then the starting estimate is iteratively perturbed by adding or subtra
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Ticknor, Anthony J., Harrison H. Barrett, and Roger L. Easton. "Optical Boltzmann Machines." In Optical Computing. Optica Publishing Group, 1985. http://dx.doi.org/10.1364/optcomp.1985.pd3.

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Sejnowski, Terrence J. "Higher-order Boltzmann machines." In AIP Conference Proceedings Volume 151. AIP, 1986. http://dx.doi.org/10.1063/1.36246.

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Wang, Maolin, Chenbin Zhang, Yu Pan, Jing Xu, and Zenglin Xu. "Tensor Ring Restricted Boltzmann Machines." In 2019 International Joint Conference on Neural Networks (IJCNN). IEEE, 2019. http://dx.doi.org/10.1109/ijcnn.2019.8852432.

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Popa, Calin-Adrian. "Complex-Valued Deep Boltzmann Machines." In 2018 International Joint Conference on Neural Networks (IJCNN). IEEE, 2018. http://dx.doi.org/10.1109/ijcnn.2018.8489359.

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Bounds, D. G. "Numerical simulations of Boltzmann Machines." In AIP Conference Proceedings Volume 151. AIP, 1986. http://dx.doi.org/10.1063/1.36220.

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Liu, Ying. "Image compression using Boltzmann machines." In SPIE's 1993 International Symposium on Optics, Imaging, and Instrumentation, edited by Su-Shing Chen. SPIE, 1993. http://dx.doi.org/10.1117/12.162027.

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Yu, Jianjun, and Xiaogang Ruan. "Optimal Control with Boltzmann Machines." In 2008 Fourth International Conference on Natural Computation. IEEE, 2008. http://dx.doi.org/10.1109/icnc.2008.692.

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Ticknor, Anthony J., and Harrison H. Barrett. "Optical implementation in Boltzmann machines." In OSA Annual Meeting. Optica Publishing Group, 1986. http://dx.doi.org/10.1364/oam.1986.mdd6.

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The Boltzmann machine, a hardware implementation of the simulated annealing algorithm, easily and directly takes advantage of highly interconnected systems for parallel processing. Optical systems for matrix operations and random number generation greatly relax the computational burden of the digital electronics and increase the speed by many orders of magnitude over the software implementation. The algorithm is inherently fault-tolerant, but simple use of an analog optical system for matrix operations might critically limit available dynamic range and space-bandwidth product. We therefore pre
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Passos, Leandro Aparecido, and Joao Paulo Papa. "Fine-Tuning Infinity Restricted Boltzmann Machines." In 2017 30th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI). IEEE, 2017. http://dx.doi.org/10.1109/sibgrapi.2017.15.

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