LCD: A Fast Contrastive Divergence Based Algorithm for Restricted Boltzmann Machine

Ning, Lin; Pittman, Randall; Shen, Xipeng

doi:10.1016/j.neunet.2018.08.018

Cited by 14 publications

(4 citation statements)

References 12 publications

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“…SRBM A sparse variant that each hidden unit connects to part of the visible units, preventing the model overfitting based on hierarchical latent tree analysis. FRBM (Ning et al, 2018) A fast variant trained by the lean CD algorithm in which the bounds-based filtering and delta product reduce the redundant dot product calculations. TTRBM (Ju et al, 2019) A compact variant that the parameters between the visible layer and hidden layer are reduced by transforming into the tensor-train format.…”

Section: The Deep Belief Net (Dbn)mentioning

confidence: 99%

“…For instance, to avoid network overfitting, Chen, Zhang, Yeung, and Chen (2017) designed the sparse Boltzmann machine that learns the network structure based the hierarchical latent tree. Ning, Pittman, and Shen (2018) introduced fast contrastive-divergence algorithms to RBMs, where the bounds-based filtering and delta product are used to reduce the redundant dot product calculations in computations. To protect the internal structure of multidimensional data, Ju et al (2019) proposed the tensor RBM, learning the high-level distribution hidden in multidimensional data, in which tensor decomposition is used to avoid the dimensional disaster.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

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A Survey on Deep Learning for Multimodal Data Fusion

et al. 2020

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With the wide deployments of heterogeneous networks, huge amounts of data with characteristics of high volume, high variety, high velocity, and high veracity are generated. These data, referred to multimodal big data, contain abundant intermodality and cross-modality information and pose vast challenges on traditional data fusion methods. In this review, we present some pioneering deep learning models to fuse these multimodal big data. With the increasing exploration of the multimodal big data, there are still some challenges to be addressed. Thus, this review presents a survey on deep learning for multimodal data fusion to provide readers, regardless of their original community, with the fundamentals of multimodal deep learning fusion method and to motivate new multimodal data fusion techniques of deep learning. Specifically, representative architectures that are widely used are summarized as fundamental to the understanding of multimodal deep learning. Then the current pioneering multimodal data fusion deep learning models are summarized. Finally, some challenges and future topics of multimodal data fusion deep learning models are described.

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Section: The Deep Belief Net (Dbn)mentioning

confidence: 99%

Section: Convolutional Neural Networkmentioning

confidence: 99%

A Survey on Deep Learning for Multimodal Data Fusion

et al. 2020

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“…Then, the hidden layer output vector calculated by {b, c, W} can be input to the next RBM. The Contrastive Divergence (CD) algorithm can be adopted to train RBM layer by layer quickly (Ning et al, 2018). The whole pretraining process can be regarded as an unsupervised learning process, which make the DBN have superior performance in feature extraction and automatic data dimension reduction (Cao et al, 2022).…”

Section: P(x Hmentioning

confidence: 99%

A hybrid deep belief network-based label distribution learning system for seismic damage estimation of liquid storage tanks

Men

Chen

Reniers

et al. 2023

Process Safety and Environmental Protection

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“…Fischer and Igel [21] discussed different RBM learning algorithms such as CD and parallel tempering. The modified CD algorithm called 'Lean Contrastive Divergence' was proposed by Ning et al [22] to increase the speed of the learning and prediction process. Pujahari et al [23] proposed a CF model by taking the preference relationship as an input to RBM and by combining the side information of the movies, trying to generate the ranking of the items.…”

Section: Introductionmentioning

confidence: 99%

Hybrid model for movie recommendation system using content K-nearest neighbors and restricted Boltzmann machine

Behera

Das

Swetanisha

et al. 2021

IJEECS

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<span>One of the most commonly used techniques in the recommendation framework is collaborative filtering (CF). It performs better with sufficient records of user rating but is not good in sparse data. Content-based filtering works well in the sparse dataset as it finds the similarity between movies by using attributes of the movies. RBM is an energy-based model serving as a backbone of deep learning and performs well in rating prediction. However, the rating prediction is not preferable by a single model. The hybrid model achieves better results by integrating the results of more than one model. This paper analyses the weighted hybrid CF system by integrating content K-nearest neighbors (KNN) with restricted Boltzmann machine (RBM). Movies are recommended to the active user in the proposed system by integrating the effects of both content-based and collaborative filtering. Model efficacy was tested with MovieLens benchmark datasets.</span>

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LCD: A Fast Contrastive Divergence Based Algorithm for Restricted Boltzmann Machine

Cited by 14 publications

References 12 publications

A Survey on Deep Learning for Multimodal Data Fusion

A Survey on Deep Learning for Multimodal Data Fusion

A hybrid deep belief network-based label distribution learning system for seismic damage estimation of liquid storage tanks

Hybrid model for movie recommendation system using content K-nearest neighbors and restricted Boltzmann machine

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