Results 61 to 70 of about 64,044 (203)

Systematic identification of latent disease-gene associations from PubMed articles. [PDF]

open access: yesPLoS ONE, 2018
Recent scientific advances have accumulated a tremendous amount of biomedical knowledge providing novel insights into the relationship between molecular and cellular processes and diseases.
Yuji Zhang   +7 more
doaj   +1 more source

Interpreting atomization of agricultural spray image patterns using latent Dirichlet allocation techniques

open access: yesArtificial Intelligence in Agriculture, 2020
Breakup patterns of agricultural formulations are explored using unsupervised learning techniques to elucidate the mechanics of atomization for oil-in-water formulations.
Hongfei Li   +3 more
doaj  

Modeling Word Relatedness in Latent Dirichlet Allocation [PDF]

open access: yesarXiv, 2014
Standard LDA model suffers the problem that the topic assignment of each word is independent and word correlation hence is neglected. To address this problem, in this paper, we propose a model called Word Related Latent Dirichlet Allocation (WR-LDA) by incorporating word correlation into LDA topic models.
arxiv  

Learning from LDA using Deep Neural Networks [PDF]

open access: yesarXiv, 2015
Latent Dirichlet Allocation (LDA) is a three-level hierarchical Bayesian model for topic inference. In spite of its great success, inferring the latent topic distribution with LDA is time-consuming. Motivated by the transfer learning approach proposed by~\newcite{hinton2015distilling}, we present a novel method that uses LDA to supervise the training ...
arxiv  

EvaLDA: Efficient Evasion Attacks Towards Latent Dirichlet Allocation [PDF]

open access: yesarXiv, 2020
As one of the most powerful topic models, Latent Dirichlet Allocation (LDA) has been used in a vast range of tasks, including document understanding, information retrieval and peer-reviewer assignment. Despite its tremendous popularity, the security of LDA has rarely been studied.
arxiv  

A 'Gibbs-Newton' Technique for Enhanced Inference of Multivariate Polya Parameters and Topic Models [PDF]

open access: yesarXiv, 2015
Hyper-parameters play a major role in the learning and inference process of latent Dirichlet allocation (LDA). In order to begin the LDA latent variables learning process, these hyper-parameters values need to be pre-determined. We propose an extension for LDA that we call 'Latent Dirichlet allocation Gibbs Newton' (LDA-GN), which places non ...
arxiv  

Scene Semantic Recognition Based on Probability Topic Model

open access: yesInformation, 2018
In recent years, scene semantic recognition has become the most exciting and fastest growing research topic. Lots of scene semantic analysis methods thus have been proposed for better scene content interpretation.
Jiangfan Feng, Amin Fu
doaj   +1 more source

Hyperspectral Unmixing with Endmember Variability using Semi-supervised Partial Membership Latent Dirichlet Allocation [PDF]

open access: yesarXiv, 2017
A semi-supervised Partial Membership Latent Dirichlet Allocation approach is developed for hyperspectral unmixing and endmember estimation while accounting for spectral variability and spatial information. Partial Membership Latent Dirichlet Allocation is an effective approach for spectral unmixing while representing spectral variability and leveraging
arxiv  

Profiling heterogeneity of Alzheimer's disease using white-matter impairment factors

open access: yesNeuroImage: Clinical, 2018
The clinical presentation of Alzheimer's disease (AD) is not unitary as heterogeneity exists in the disease's clinical and anatomical characteristics. MRI studies have revealed that heterogeneous gray matter atrophy patterns are associated with specific ...
Xiuchao Sui, Jagath C. Rajapakse
doaj  

Variable Selection for Latent Dirichlet Allocation [PDF]

open access: yesarXiv, 2012
In latent Dirichlet allocation (LDA), topics are multinomial distributions over the entire vocabulary. However, the vocabulary usually contains many words that are not relevant in forming the topics. We adopt a variable selection method widely used in statistical modeling as a dimension reduction tool and combine it with LDA. In this variable selection
arxiv  

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