Development and Application of Matrix Variate Restricted Boltzmann Machine

Qi, Guanglei; Sun, Yi; Li, Mingang; Hou, Xiaogang

doi:10.1109/access.2020.3012603

Cited by 2 publications

(2 citation statements)

References 36 publications

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“…2, we first present a data vector in the visible units. Afterwards, we update hidden neurons and visible neurons based on (10) and (12) a number of times. When the state distribution reaches equilibrium, the probability values being 1 of the hidden nodes are used as the extracted features.…”

Section: B Restricted Boltzmann Machine For Feature Extractionmentioning

confidence: 99%

“…The restricted Boltzmann machine (RBM) model is a variant of BM, in which connection weights only exist between visible neurons and hidden neurons. The RBM model is very efficient for feature extraction and is widely used in dimensionality reduction [10], classification [11], and image processing [12], [13]. In addition, it can act as the building blocks for deep neural networks [14].…”

Section: Introductionmentioning

confidence: 99%

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Analysis on Noisy Boltzmann Machines and Noisy Restricted Boltzmann Machines

Leung

Sum

2021

IEEE Access

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The Boltzmann machine (BM) and restricted Boltzmann machine (RBM) models are representative stochastic neural networks, in which neuron states are determined by stochastic activation functions. They are widely used in many applications. However, when analog circuits are used to realize a neural network, noise are not avoidable. The noise could come from external environments, such as power supplies and thermal noise, and they will affect the neurons' stochastic behaviour in BM and RBM. Hence it is important to theoretically study how the noise affect the BM and RBM. To best of our knowledge, there are little works related to the analysis on noisy BMs and noisy RBMs. This paper considers that there are additive noise in the neurons of BMs or RBMs, and theoretically studies the behaviors of BM and RBM under this imperfect condition. It is found that the effect of additive noise is similar to increasing the temperature factor of BM and RBM. Since the input noise may make the networks to have wrong stochastic behaviour, there is Kullback Leibler (KL) divergence loss in noisy BMs and noisy RBMs. Based on the Gaussian-distributed noise assumption, a noise compensation method is also proposed to suppress the effect of additive noise. Experiments show that the proposed noise compensation method can greatly suppress the KL divergence loss. In addition, from the experimental results, our method is also effective for handling non-Gaussian noise.

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Section: B Restricted Boltzmann Machine For Feature Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analysis on Noisy Boltzmann Machines and Noisy Restricted Boltzmann Machines

Leung

Sum

2021

IEEE Access

View full text Add to dashboard Cite

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Multimodal data integration for oncology in the era of deep neural networks: a review

Waqas,

Tripathi,

Ramachandran

et al. 2024

Front. Artif. Intell.

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Cancer research encompasses data across various scales, modalities, and resolutions, from screening and diagnostic imaging to digitized histopathology slides to various types of molecular data and clinical records. The integration of these diverse data types for personalized cancer care and predictive modeling holds the promise of enhancing the accuracy and reliability of cancer screening, diagnosis, and treatment. Traditional analytical methods, which often focus on isolated or unimodal information, fall short of capturing the complex and heterogeneous nature of cancer data. The advent of deep neural networks has spurred the development of sophisticated multimodal data fusion techniques capable of extracting and synthesizing information from disparate sources. Among these, Graph Neural Networks (GNNs) and Transformers have emerged as powerful tools for multimodal learning, demonstrating significant success. This review presents the foundational principles of multimodal learning including oncology data modalities, taxonomy of multimodal learning, and fusion strategies. We delve into the recent advancements in GNNs and Transformers for the fusion of multimodal data in oncology, spotlighting key studies and their pivotal findings. We discuss the unique challenges of multimodal learning, such as data heterogeneity and integration complexities, alongside the opportunities it presents for a more nuanced and comprehensive understanding of cancer. Finally, we present some of the latest comprehensive multimodal pan-cancer data sources. By surveying the landscape of multimodal data integration in oncology, our goal is to underline the transformative potential of multimodal GNNs and Transformers. Through technological advancements and the methodological innovations presented in this review, we aim to chart a course for future research in this promising field. This review may be the first that highlights the current state of multimodal modeling applications in cancer using GNNs and transformers, presents comprehensive multimodal oncology data sources, and sets the stage for multimodal evolution, encouraging further exploration and development in personalized cancer care.

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Development and Application of Matrix Variate Restricted Boltzmann Machine

Cited by 2 publications

References 36 publications

Analysis on Noisy Boltzmann Machines and Noisy Restricted Boltzmann Machines

Analysis on Noisy Boltzmann Machines and Noisy Restricted Boltzmann Machines

Multimodal data integration for oncology in the era of deep neural networks: a review

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