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Graph Signal Recovery Using Restricted Boltzmann Machines [article]

Ankith Mohan, Aiichiro Nakano, Emilio Ferrara
2020 arXiv   pre-print
We propose a model-agnostic pipeline to recover graph signals from an expert system by exploiting the content addressable memory property of restricted Boltzmann machine and the representational ability  ...  Although this pipeline can deal with noise in any dataset, it is particularly effective for graph-structured datasets.  ...  Restricted Boltzmann Machines Restricted Boltzmann machines (RBMs) [16] are a powerful class of energybased models (EBMs).  ... 
arXiv:2011.10549v1 fatcat:dqt52dllhnbqdggmsfrgojk22u

Inferring sparsity: Compressed sensing using generalized restricted Boltzmann machines

Eric W. Tramel, Andre Manoel, Francesco Caltagirone, Marylou Gabrie, Florent Krzakala
2016 2016 IEEE Information Theory Workshop (ITW)  
Assuming that a generative statistical model, such as a Boltzmann machine, can be trained in an unsupervised manner on example signals, we demonstrate how this signal model can be used within a Bayesian  ...  By deriving a message-passing inference for general distribution restricted Boltzmann machines, we are able to integrate these inferred signal models into approximate message passing for compressed sensing  ...  For this, we turn to real-valued restricted Boltzmann machines (RBMs).  ... 
doi:10.1109/itw.2016.7606837 dblp:conf/itw/TramelMCGK16 fatcat:adgpnjdq5jbm7ik44dtj3n67le

Exploiting Statistical Dependencies in Sparse Representations for Signal Recovery

Tomer Peleg, Yonina C. Eldar, Michael Elad
2012 IEEE Transactions on Signal Processing  
We follow the suggestion of two recent works and assume that the sparsity pattern is modeled by a Boltzmann machine, a commonly used graphical model.  ...  The main goal of this paper is to introduce a statistical model that takes such dependencies into account and show how this model can be used for sparse signal recovery.  ...  The Boltzmann Machine In this subsection we focus on the BM, a widely used MRF. We are about to show that this can serve as a useful and powerful prior on the sparsity pattern.  ... 
doi:10.1109/tsp.2012.2188520 fatcat:b2z3kvttdbh5jmqcire22qsuhe

An Underwater Acoustic Target Recognition Method Based on Restricted Boltzmann Machine

Xinwei Luo, Yulin Feng
2020 Sensors  
This method takes the normalized frequency spectrum of the signal as input, uses a restricted Boltzmann machine to perform unsupervised automatic encoding of the data, extracts the deep data structure  ...  This method was tested using actual ship radiated noise database, and the results show that proposed classification system has better recognition accuracy and adaptability than the hand-crafted feature  ...  The deep Boltzmann machine can be decomposed into a deep neural network formed by stacking multiple restricted Boltzmann machines (RBM).  ... 
doi:10.3390/s20185399 pmid:32967172 pmcid:PMC7570896 fatcat:nnn5st6dqnc3hiey4ulasmmnou

Approximate message passing with restricted Boltzmann machine priors

Eric W Tramel, Angélique Drémeau, Florent Krzakala
2016 Journal of Statistical Mechanics: Theory and Experiment  
The AMP framework provides modularity in the choice of signal prior; here we propose a hierarchical form of the Gauss-Bernouilli prior which utilizes a Restricted Boltzmann Machine (RBM) trained on the  ...  Finally, using the MNIST handwritten digit dataset, we show experimentally that using an RBM allows AMP to approach oracle-support performance.  ...  BINARY RESTRICTED BOLTZMANN MACHINES A restricted Boltzmann machine is an Energy based model similar to what is called an Ising model on a bipartite graph in statistical physics.  ... 
doi:10.1088/1742-5468/2016/07/073401 fatcat:voh24c72obcwrms6t4wumh3yly

Deep Learning Based Advanced Spatio-Temporal Extraction Model In Medical Sports Rehabilitation For Motion Analysis and Data Processing

Huayun Cui, Cunqiang Chang
2020 IEEE Access  
A Limited Boltzmann Model (LBM) theory is based on the Advanced Spatio-Temporal Extraction Model (ASTEM), which has been used for analyzing the physiological motion of human skeletons.  ...  INDEX TERMS Sports medical data, limited Boltzmann model, advanced spatio-temporal extraction model.  ...  A useful motion graph has been combined to restrict the time and graph connectivity intelligently. Continuing pairs of frames have a wide area based on a suitable transmitting point.  ... 
doi:10.1109/access.2020.3003652 fatcat:6scq56rjlzbbthi62jw2uyuaia

Boltzmann Machines as Generalized Hopfield Networks: A Review of Recent Results and Outlooks

Chiara Marullo, Elena Agliari
2020 Entropy  
The Hopfield model and the Boltzmann machine are among the most popular examples of neural networks.  ...  Interestingly, the Boltzmann machine and the Hopfield network, if considered to be two cognitive processes (learning and information retrieval), are nothing more than two sides of the same coin.  ...  Acknowledgments: The authors acknowledge Adriano Barra, Alberto Fachechi, Francesco Alemanno and Linda Albanese for useful discussions.  ... 
doi:10.3390/e23010034 pmid:33383716 fatcat:h6vy6r6scbd43mcvqpqrb2arwm

Application of Improved Deep Belief Network Model in 3D Art Design

Zilin Ye, Naeem Jan
2022 Mathematical Problems in Engineering  
The deep belief network DBN is formed by stacking the restricted Boltzmann machine RBM.  ...  driven by the high-speed computing performance of computers and massive data on the Internet, deep nervine networks with highly abstract feature extraction and classification capabilities have been widely used  ...  D Art Design Restricted Boltzmann Machine.  ... 
doi:10.1155/2022/2213561 fatcat:co4ofii5ercpvcngdiuffe5pli

A Deep Belief Network Based Brain Tumor Detection in MRI Images

2017 International Journal of Science and Research (IJSR)  
MRI (Magnetic Resonance Imaging) is widely used medical imaging technique used to assess tumors, but large amount of data produced by MRI may vary greatly. Thus manual detection will be a challenge.  ...  A DBN (Deep Belief Network) based classification method is used to identify brain tumor in MRI images which can yield the result more accurately.  ...  b) Restricted Boltzmann Machines Restricted Boltzmann Machines Boltzmann Machine is a stochastic recurrent neural network.  ... 
doi:10.21275/art20175321 fatcat:gdfqhhhy7jdcpnymeqcuezwlzm

LBG-SQUARE Fault-Tolerant, Locality-Aware Co-Allocation in P2P Grids

Gérard Dethier, Cyril Briquet, Pierre Marchot, Pierre-Arnoul de Marneffe
2008 2008 Ninth International Conference on Parallel and Distributed Computing, Applications and Technologies  
Acknowledgments We want to thank Xavier Dalem for code contributed to the LBG middleware and Valérie Leroy for useful discussions about graph optimization.  ...  Authorized licensed use limited to: IEEE Xplore. Downloaded on January 9, 2009 at 05:26 from IEEE Xplore. Restrictions apply.  ...  Resources run Tasks in a dedicated Java Virtual Machine, separate from the middleware. A security policy is enforced to sandbox Task execution, i.e. to restrict interactions with its environment.  ... 
doi:10.1109/pdcat.2008.24 dblp:conf/pdcat/DethierBMM08 fatcat:docmucsh4zgmvh5y3hh7erdd3y

Dreaming Machines: On multimodal fusion and information retrieval using neural-symbolic cognitive agents

Leo De Penning, Artur D'Avila Garcez, John-Jules C. Meyer, Marc Herbstritt
2013 Imperial College Computing Student Workshop  
Deep Boltzmann Machines (DBM) have been used as a computational cognitive model in various AI-related research and applications, notably in computational vision and multimodal fusion.  ...  We describe how this agent can be used to simulate certain neurological mechanisms related to hallucinations and dreaming and how these mechanisms are beneficial to the integrity of the DBM.  ...  A DBM can learn hierarchical representations of data, using several layers of Restricted Boltzmann Machines (RBMs) [11] .  ... 
doi:10.4230/oasics.iccsw.2013.89 dblp:conf/iccsw/PenningGM13 fatcat:xnrx67youbbjddtjazmv4rxrca

A novel unsupervised analysis of electrophysiological signals reveals new sleep substages in mice

Vasiliki-Maria Katsageorgiou, Diego Sona, Matteo Zanotto, Glenda Lassi, Celina Garcia-Garcia, Valter Tucci, Vittorio Murino, Karunesh Ganguly
2018 PLoS Biology  
By using our new substages classification, we have identified novel differences among various genetic backgrounds.  ...  Acknowledgments We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan X GPU used for the training of the mcRBM.  ...  Out of all potential models, we decided to adopt the mean-covariance restricted Boltzmann machine (mcRBM) [8] to model brain and muscle activities.  ... 
doi:10.1371/journal.pbio.2003663 pmid:29813050 pmcid:PMC5993302 fatcat:xtgzderltbeurldctwtrp5xg44

Clinical Characteristics and Mathematical Analysis of Curative Effect of Hemodialysis in Curing Poisoning Caused by Snakebite

Guoliang Huang, Bingbing Chen, Yi Luo, Liming Chen, Shaojie Wu, Shijun Wang, Danilo Pelusi
2022 Scanning  
In order to explore the clinical characteristics of hemodialysis in curing poisoning from snakebites, a two-classification model of nuclear logistic neural network based on restricted Boltzmann machine  ...  The network first performs feature learning through unsupervised training of restricted Boltzmann machines and obtains the initial values of the parameters to be identified, which reduces the influence  ...  The proposed nuclear logistic neural network model based on the restricted Boltzmann machine analyzes the modeling data, and the classification results are used to mine the index variables used to establish  ... 
doi:10.1155/2022/2312972 pmid:35601870 pmcid:PMC9106513 fatcat:n6v7wbnpivecbfoa7nrcvj5jey

Compressed Sensing ECG using Restricted Boltzmann Machines [article]

Luisa Polania, Rafael Plaza
2018 arXiv   pre-print
In this paper, we aim at further reducing the number of necessary measurements to achieve faithful reconstruction by exploiting the representational power of restricted Boltzmann machines (RBMs) to model  ...  the probability distribution of the sparsity pattern of ECG signals.  ...  Background Restricted Boltzmann Machines Restricted Boltzmann machines are a type of undirected graphical models formed by a layer of binary stochastic hidden units and a layer of stochastic visible  ... 
arXiv:1806.01779v1 fatcat:vbnjyf37wna2xmtrozpbqhw73a

A Deterministic and Generalized Framework for Unsupervised Learning with Restricted Boltzmann Machines [article]

Eric W. Tramel and Marylou Gabrié and Andre Manoel and Francesco Caltagirone and Florent Krzakala
2017 arXiv   pre-print
Restricted Boltzmann machines (RBMs) are energy-based neural-networks which are commonly used as the building blocks for deep architectures neural architectures.  ...  In this work, we derive a deterministic framework for the training, evaluation, and use of RBMs based upon the Thouless-Anderson-Palmer (TAP) mean-field approximation of widely-connected systems with weak  ...  RESTRICTED BOLTZMANN MACHINES Restricted Boltzmann Machines (RBMs) [27] are latentvariable generative models often used in the context of unsupervised learning.  ... 
arXiv:1702.03260v2 fatcat:ngxwzpu77bghndibp6ny4conzm
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