[PDF] Top 20 Discrete Restricted Boltzmann Machines
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Discrete Restricted Boltzmann Machines
... A discrete RBM is a product of experts (Hinton, 1999); each hidden unit represents an expert which is a mixture model of product distributions, or na¨ıve Bayes ... See full document
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RECOMMENDATION ENGINE FOR COMPETITIVE CODING QUESTIONS USING RESTRICTED BOLTZMANN MACHINES, A HYBRID APPROACH
... In this paper, we have used a neural network algorithm, Restricted Boltzmann Machine to perform collaborative filtering. As [6] has proved that these can easily scale for millions of users and can include a ... See full document
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Generalising the Discriminative Restricted Boltzmann Machine
... Binomial DRBM: It was demonstrated in [18] how groups of N (where N is a positive integer greater than 1) stochastic units of the standard RBM can be combined in order to approximate discrete-valued functions in ... See full document
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Universal Approximation Results for the Temporal Restricted Boltzmann Machine and the Recurrent Temporal Restricted Boltzmann Machine
... is restricted to a relatively small number of hidden nodes (Sutskever and Hinton, 2010)(Le Roux and Bengio, 2010)(Montu- far and Ay, ...of Boltzmann machines (Montufar et ... See full document
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Hardware Acceleration on Cloud Services: The use of Restricted Boltzmann Machines on Handwritten Digits Recognition
... Section 2 gives an overview of existing implementations, especially on handwritten digits recognition. Section 3 analyzes Restricted Boltzmann Machines and how they can be modified in order to be ... See full document
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Online Sequential Extreme Learning Machine: A New Training Scheme for Restricted Boltzmann Machines
... Restricted Boltzmann machine (RBM) is the first types of neural networks used for unsupervised ...considered restricted because no two nodes in the same layer are sharing ... See full document
7
Learning motion-difference features using Gaussian restricted Boltzmann machines for efficient human action recognition
... Recently, unsupervised learning algorithms have been em- ployed to learn spatio-temporal features from video for visual word construction. In [6], Bo et al. proposed the Space- Time Deep Belief Network, a model based on ... See full document
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Performance Improvement In Clustering Using Similarity Measurement Method By Categorizing The Data Items
... The author write on Probabilistic Clustering and Classification for Textual Data: an Online and Incremental Approach[12]. This paper proposes an incremental, online and probabilistic clustering algorithm for textual ... See full document
15
Deep Learning: Approaches and Challenges
... It di- vides the deep learning models into four categories based on the structure of the model: Restricted Boltzmann Machines, Autoencoder, Convolutional Neural Networks, and Deep Belief[r] ... See full document
8
Representation decomposition for knowledge extraction and sharing using restricted Boltzmann machines
... as restricted Boltzmann machines (RBMs) and deep be- lief networks (DBNs) can learn useful patterns for recognition tasks in wide range of ... See full document
179
Voice conversion using speaker-dependent conditional restricted Boltzmann machine
... speaker-dependent restricted Boltzmann machines (RBMs) [26] (or deep belief nets (DBN) [27]) that captures high-order features in an unsu- pervised manner and a concatenating ... See full document
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Article Imposing Higher-Level Structure in Polyphonic Music Generation Using Convolutional Restricted Boltzmann Machines and Constraints
... RNN Restricted Boltzmann Machine (RNN-RBM) model for polyphonic music generation introduced in (Boulanger- Lewandowski et ...stricted Boltzmann Machine (RBM) component is used for sampling a ... See full document
31
How to Center Deep Boltzmann Machines
... centered Restricted Boltzmann Machines (RBMs) and centered Deep Boltzmann Machines (DBMs), where centering is done by subtracting offset values from visible and hidden ...mann ... See full document
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Multimodal Learning with Deep Boltzmann Machines
... linear Restricted Boltzmann Machine (RBM) model with Gaussian hidden units together with Gaussian and Poisson visible ...Deep Boltzmann Machine which is a probabilistic generative model as opposed to ... See full document
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Learning Algorithms for the Classification Restricted Boltzmann Machine
... In this paper, we argue that RBMs can provide a self-contained and competitive framework for solving supervised learning problems. Based on the Classification Restricted Boltzmann Machine (ClassRBM), the ... See full document
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Stochastic Complexity and Generalization Error of a Restricted Boltzmann Machine in Bayesian Estimation
... certain restricted Boltzmann machines, ex- plicitly (Theorem 2 and Theorem 3), and give new bounds for the generalization error of the other types (Theorem 4), using both a new method of eigenvalue ... See full document
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Restricted Boltzmann Machine and its Potential to Better Predict Cancer Survival
... 8. Hinton GE (2012) A practical guide to training restricted Boltzmann machines. In Neural networks: Tricks of the trade. Springer, pp. 599-619. 9. Leke C, Marwala T (2016) Missing data estimation in ... See full document
5
LCD: A fast contrastive divergence based training algorithm for restricted Boltzmann machine
... As a generative model, Restricted Boltzmann Machine (RBM) has been used for extracting meaningful high-level representations (e.g., hidden features in images) from many different types of data input ... See full document
10
An Informational Proof of H Theorem
... a restricted case) important consequences about the relationship between Boltzmann and Gibbs approaches to the microscopic representation of thermodynamical ...with Boltzmann intuition (exactly in ... See full document
15
FPGA Implementation of a Scalable and Highly Parallel Architecture for Restricted Boltzmann Machines
... Restricted Boltzmann Machines (RBMs) are an effective model for machine learning; however, they require a significant amount of processing ... See full document
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