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Добірка наукової літератури з теми "Latent"

Автор: Grafiati

Опубліковано: 4 червня 2021

Оновлено: 10 березня 2023

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Статті в журналах з теми "Latent"

Keeling, R. "Latest developments in latent defects insurance." Property Management 11, no. 3 (March 1993): 220–25. http://dx.doi.org/10.1108/eum0000000003400.

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Wolfson, Susan J. "Yeats's Latent Keats / Keats's Latent Yeats." PMLA/Publications of the Modern Language Association of America 131, no. 3 (May 2016): 603–21. http://dx.doi.org/10.1632/pmla.2016.131.3.603.

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Keats's tracks into the nineteenth century angle toward a “modernism” often defined at his expense—yet with latent identifications. In relations of past and present, figurai identifications may register in nuances different from conscious allusion or the psychodramas of influence, ravages and resistance, hauntings and felt belatedness that issue in self-interested misreadings. “Latent Keats” and “Latent Yeats” play into an important, underreported current in both Keats studies and Yeats studies: a “Long Romanticism” in intimate verbal figures that trouble any “Modernism” of definitional difference from “Romantic.” Keats's writing harbors figures to which Yeats could respond, even correspond, vexed as he was by “Keats” as the name for the puerile outsider's dreamy sensuousness that a proper “modernist” needed to spurn. Such complication is one of the variable formations by which a “modernist” program manages to conjure the “Romantic” precedence it would supersede.

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McCutcheon, Allen L., Rolf Langeheine, and Jurgen Rost. "Latent Trait and Latent Class Models." Contemporary Sociology 18, no. 5 (September 1989): 836. http://dx.doi.org/10.2307/2073408.

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Guo, J. "Latent class regression on latent factors." Biostatistics 7, no. 1 (May 25, 2005): 145–63. http://dx.doi.org/10.1093/biostatistics/kxi046.

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FLORES, RICARDO, SONIA DELGADO, MARÍA-ELENA RODIO, SILVIA AMBRÓS, CARMEN HERNÁNDEZ, and FRANCESCO DI SERIO. "Peach latent mosaic viroid: not so latent." Molecular Plant Pathology 7, no. 4 (July 2006): 209–21. http://dx.doi.org/10.1111/j.1364-3703.2006.00332.x.

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Croon, Marcel. "Latent class analysis with ordered latent classe." British Journal of Mathematical and Statistical Psychology 43, no. 2 (November 1990): 171–92. http://dx.doi.org/10.1111/j.2044-8317.1990.tb00934.x.

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Chaudhary, Neha, and Priti Dimri. "LATENT FINGERPRINT IMAGE ENHANCEMENT BASED ON OPTIMIZED BENT IDENTITY BASED CONVOLUTIONAL NEURAL NETWORK." Indian Journal of Computer Science and Engineering 12, no. 5 (October 20, 2021): 1477–93. http://dx.doi.org/10.21817/indjcse/2021/v12i5/211205124.

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Fingerprints are unique biometric systems (BSs) in which none of the human possesses similar fingerprint structures. It is one of the most significant biometric processes used in the identification of criminals. Latent fingerprints or latents are generated mainly by the finger sweat or oil deposits which is left by the suspects unintentionally. The impressions of latents are blurred or smudgy in nature and not viewed by naked eye. These fingerprints are of low quality, corrupted by noise, degraded by technological factors and exhibit minor details. Latents display consistent structural info when observed as an image. Image Enhancement is necessary in latents, to transform the latent (noisy) image into fine-quality (enhanced) image. In this work, a new image enhancement approach named BI-CNN (Bent Identity-Convolution Neural Network) with Spatial Pyramid Max Pooling (SPMP) model optimized using TSOA (Tunicate Swarm Optimization Algorithm) is presented to produce an enhanced latent at the output. This procedure involves the integration of ROI (Region Of Interest) Estimation, Anisotropic Gaussian Filter (AGF) based Pre-filtering, Fingerprint alignment using Sobel Filter, Intrinsic Feature patch extraction using Optimized BI-CNN, GAT (Graph Attention) network based Similarity Estimation followed by image reconstruction and feedback module. The implementation tool used in this work is PYTHON platform. The proposed optimized BI-CNN framework tested on dual public datasets namely IIITD-latent finger print and IIITD-MOLF have shown enhanced outcomes. Thus, the IIITD -latent fingerprint database obtained 83.33% on Rank-10 accuracy and 39.33% on Rank-25 accuracy.

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Zellweger, Jean-Pierre. "Latent Tuberculosis Infection." European Respiratory & Pulmonary Diseases 4, no. 1 (2018): 21. http://dx.doi.org/10.17925/erpd.2018.4.1.21.

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Tuberculosis (TB) is a major global public health problem and is the leading cause of death linked to a single pathogen, ranking above human immunodeficiency virus (HIV).¹ Clinically, TB has been categorised as active disease (patients who are generally symptomatic and may be infectious if pulmonary involvement is present) and latent infection (asymptomatic and not infectious, but at variable risk for progression to active TB disease). It is increasingly being recognised that latent TB infection (LTBI) reflects diverse responses to infection with Mycobacterium tuberculosis and may lead to heterogeneous clinical outcomes. In an expert interview, Jean-Pierre Zellweger discusses the latest World Health Organisation (WHO) guidelines on the management of LTBI.

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IQBAL, JAVED, ALTAF HUSSAIN MALIK, and Aftab JAMIL. "LATENT TUBERCULOSIS;." Professional Medical Journal 19, no. 01 (January 3, 2012): 059–62. http://dx.doi.org/10.29309/tpmj/2012.19.01.1949.

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Objectives: To assess the degree to which Latent Tuberculosis exist among long–term inmates in jail. Study Design:Prospective Cohort Analytic Experimental Quantitative Data. Setting and Period: Jails at Bahawalpur between 2009-2010. Methods: Wemonitored the Mountex Test of prisoners within 48 to 72 h and those who were 10 mm or more were considered positive and for HIV positive 5mm criteria were set to declare positive. Group 1-who were in jail for more than a year and those, Group- 2-who were in jail for less than onemonth. Data was collected on a proforma. Each prisoner had thorough clinical examination with detailed clinical history and Chest X-ray.Inclusion Criteria: 1. All those prisoners who never had tuberculosis in past. 2. All those prisoners who were not on Anti Tuberculous Therapy.3. All those prisoner whose chest x ray was normal and had no symptoms of tuberculosis. Exclusion Criteria: 1. All those who had tuberculosisin past or were on antituberculous treatment currently. 2. All those who were having chronic cough. Results: Total number of prisoners in group1 were 298 and number of prisoners in group 2 were 128. Latent tuberculosis was found in total of 31(10.40%) of prisoners in group 1 and noneof prisoners in group 2 were having latent tuberculosis. Conclusions: Jail inmates for more than 1 year did show more numbers of latenttuberculosis patients than the new inmates. These results suggest that the close contacts harbor the live tubercle bacilli and in future they mayconvert into active cases.

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Toister, Yanai. "Latent digital." Journal of Visual Art Practice 19, no. 2 (January 14, 2020): 125–36. http://dx.doi.org/10.1080/14702029.2019.1701915.

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Дисертації з теми "Latent"

Anaya, Leticia H. "Comparing Latent Dirichlet Allocation and Latent Semantic Analysis as Classifiers." Thesis, University of North Texas, 2011. https://digital.library.unt.edu/ark:/67531/metadc103284/.

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In the Information Age, a proliferation of unstructured text electronic documents exists. Processing these documents by humans is a daunting task as humans have limited cognitive abilities for processing large volumes of documents that can often be extremely lengthy. To address this problem, text data computer algorithms are being developed. Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA) are two text data computer algorithms that have received much attention individually in the text data literature for topic extraction studies but not for document classification nor for comparison studies. Since classification is considered an important human function and has been studied in the areas of cognitive science and information science, in this dissertation a research study was performed to compare LDA, LSA and humans as document classifiers. The research questions posed in this study are: R1: How accurate is LDA and LSA in classifying documents in a corpus of textual data over a known set of topics? R2: How accurate are humans in performing the same classification task? R3: How does LDA classification performance compare to LSA classification performance? To address these questions, a classification study involving human subjects was designed where humans were asked to generate and classify documents (customer comments) at two levels of abstraction for a quality assurance setting. Then two computer algorithms, LSA and LDA, were used to perform classification on these documents. The results indicate that humans outperformed all computer algorithms and had an accuracy rate of 94% at the higher level of abstraction and 76% at the lower level of abstraction. At the high level of abstraction, the accuracy rates were 84% for both LSA and LDA and at the lower level, the accuracy rate were 67% for LSA and 64% for LDA. The findings of this research have many strong implications for the improvement of information systems that process unstructured text. Document classifiers have many potential applications in many fields (e.g., fraud detection, information retrieval, national security, and customer management). Development and refinement of algorithms that classify text is a fruitful area of ongoing research and this dissertation contributes to this area.

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Xiong, Hao. "Diversified Latent Variable Models." Thesis, The University of Sydney, 2018. http://hdl.handle.net/2123/18512.

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Latent variable model is a common probabilistic framework which aims to estimate the hidden states of observations. More specifically, the hidden states can be the position of a robot, the low dimensional representation of an observation. Meanwhile, various latent variable models have been explored, such as hidden Markov models (HMM), Gaussian mixture model (GMM), Bayesian Gaussian process latent variable model (BGPLVM), etc. Moreover, these latent variable models have been successfully applied to a wide range of fields, such as robotic navigation, image and video compression, natural language processing. So as to make the learning of latent variable more efficient and robust, some approaches seek to integrate latent variables with related priors. For instance, the dynamic prior can be incorporated so that the learned latent variables take into account the time sequence. Besides, some methods introduce inducing points as a small set representing the large size latent variable to enhance the optimization speed of the model. Though those priors are effective to facilitate the robustness of the latent variable models, the learned latent variables are inclined to be dense rather than diverse. This is to say that there are significant overlapping between the generated latent variables. Consequently, the latent variable model will be ambiguous after optimization. Clearly, a proper diversity prior play a pivotal role in having latent variables capture more diverse features of the observations data. In this thesis, we propose diversified latent variable models incorporated by different types of diversity priors, such as single/dual diversity encouraging prior, multi-layered DPP prior, shared diversity prior. Furthermore, we also illustrate how to formulate the diversity priors in different latent variable models and perform learning, inference on the reformulated latent variable models.

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Etessami, Pantea. "Mutagenesis studies on the genome of cassava latent virus : (African cassava latent virus)." Thesis, University of East Anglia, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.235620.

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Murphy, Sean Michael. "Disease management and latent choices." Online access for everyone, 2008. http://www.dissertations.wsu.edu/Dissertations/Summer2008/S_Murphy_062608.pdf.

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Cartmill, Ian. "Builders' liability for latent defects." Thesis, University of Oxford, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.302694.

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Shafia, Aminath. "Latent infection of Botrytis cinerea." Thesis, University of Reading, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.499372.

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Latent B. cinerea was detected in nine symptomless wild host species from the families Asteraceae and Brassicaceae, in addition to greenhouse grown lettuce. Conventional testing methods revealed that latent B. cinerea was equally prevalent in the root system as the above ground parts. Incidence of latent infection was moderate in some species (Achillea milleforlium, Arabidopsis thaliana, Centraurea nigra, Cirsium vulgare, Senecio jacobaea, Senecio vulgaris and Taraaxacum agg.) and rare in others (Tussilago farfara and Bellis perennis). In greenhouse lettuce, latent infection was activated by prolonged water stress and artificial inoculation. Despite inoculation, unstressed, vigorously growing lettuce and Arabidopsis plants remained asymptomatic throughout the growing period. Fungicide seed treatment did not significantly affect the amount of latent B. cinerea recovered from the lettuce plants. Introduction of antagonistic micro-organism Trichoderma harzianum T-39 into the soil decreased the amount of latent infection recovered from lettuce leaves but increased it in the stem. A weak negative correlation was found between photosynthesis and the amount of B. cinerea recovered from the leaves. Weight of the plants was reduced due to inoculation of B. cinerea even though latent infection was unaltered. There was no relation between plant weight and total endophytic B. cinerea. A marginal increase of the phenolic contents of the leaf was observed due to inoculation, but no changes to the antioxidant activity, chlorophyll content or carotenoids were found. The high incidence of latent infection found in greenhouse grown lettuce plants with or without successful inoculation may have been due to the presence of several genetically distinct isolates of B. cinerea. Eight different haplotypes were identified among the 32 isolates assessed. A single very common haplotype presumably originated from seed borne infection, because it was rare in plants grown from fungicide treated seed. Latency may be attributed to a mild strain defence response by the presence of several genetically different strains of the pathogen present within the plant as endophytes.

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Ponweiser, Martin. "Latent Dirichlet Allocation in R." WU Vienna University of Economics and Business, 2012. http://epub.wu.ac.at/3558/1/main.pdf.

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Topic models are a new research field within the computer sciences information retrieval and text mining. They are generative probabilistic models of text corpora inferred by machine learning and they can be used for retrieval and text mining tasks. The most prominent topic model is latent Dirichlet allocation (LDA), which was introduced in 2003 by Blei et al. and has since then sparked off the development of other topic models for domain-specific purposes. This thesis focuses on LDA's practical application. Its main goal is the replication of the data analyses from the 2004 LDA paper ``Finding scientific topics'' by Thomas Griffiths and Mark Steyvers within the framework of the R statistical programming language and the R~package topicmodels by Bettina Grün and Kurt Hornik. The complete process, including extraction of a text corpus from the PNAS journal's website, data preprocessing, transformation into a document-term matrix, model selection, model estimation, as well as presentation of the results, is fully documented and commented. The outcome closely matches the analyses of the original paper, therefore the research by Griffiths/Steyvers can be reproduced. Furthermore, this thesis proves the suitability of the R environment for text mining with LDA. (author's abstract)
Series: Theses / Institute for Statistics and Mathematics

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Creagh-Osborne, Jane. "Latent variable generalized linear models." Thesis, University of Plymouth, 1998. http://hdl.handle.net/10026.1/1885.

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Generalized Linear Models (GLMs) (McCullagh and Nelder, 1989) provide a unified framework for fixed effect models where response data arise from exponential family distributions. Much recent research has attempted to extend the framework to include random effects in the linear predictors. Different methodologies have been employed to solve different motivating problems, for example Generalized Linear Mixed Models (Clayton, 1994) and Multilevel Models (Goldstein, 1995). A thorough review and classification of this and related material is presented. In Item Response Theory (IRT) subjects are tested using banks of pre-calibrated test items. A useful model is based on the logistic function with a binary response dependent on the unknown ability of the subject. Item parameters contribute to the probability of a correct response. Within the framework of the GLM, a latent variable, the unknown ability, is introduced as a new component of the linear predictor. This approach affords the opportunity to structure intercept and slope parameters so that item characteristics are represented. A methodology for fitting such GLMs with latent variables, based on the EM algorithm (Dempster, Laird and Rubin, 1977) and using standard Generalized Linear Model fitting software GLIM (Payne, 1987) to perform the expectation step, is developed and applied to a model for binary response data. Accurate numerical integration to evaluate the likelihood functions is a vital part of the computational process. A study of the comparative benefits of two different integration strategies is undertaken and leads to the adoption, unusually, of Gauss-Legendre rules. It is shown how the fitting algorithms are implemented with GLIM programs which incorporate FORTRAN subroutines. Examples from IRT are given. A simulation study is undertaken to investigate the sampling distributions of the estimators and the effect of certain numerical attributes of the computational process. Finally a generalized latent variable model is developed for responses from any exponential family distribution.

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Mao, Cheng Ph D. Massachusetts Institute of Technology. "Matrix estimation with latent permutations." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/117863.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mathematics, 2018.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 151-167).
Motivated by various applications such as seriation, network alignment and ranking from pairwise comparisons, we study the problem of estimating a structured matrix with rows and columns shuffled by latent permutations, given noisy and incomplete observations of its entries. This problem is at the intersection of shape constrained estimation which has a long history in statistics, and latent permutation learning which has driven a recent surge of interest in the machine learning community. Shape constraints on matrices, such as monotonicity and smoothness, are generally more robust than parametric assumptions, and often allow for adaptive and efficient estimation in high dimensions. On the other hand, latent permutations underlie many graph matching and assignment problems that are computationally intractable in the worst-case and not yet well-understood in the average-case. Therefore, it is of significant interest to both develop statistical approaches and design efficient algorithms for problems where shape constraints meet latent permutations. In this work, we consider three specific models: the statistical seriation model, the noisy sorting model and the strong stochastic transitivity model. First, statistical seriation consists in permuting the rows of a noisy matrix in such a way that all its columns are approximately monotone, or more generally, unimodal. We study both global and adaptive rates of estimation for this model, and introduce an efficient algorithm for the monotone case. Next, we move on to ranking from pairwise comparisons, and consider the noisy sorting model. We establish the minimax rates of estimation for noisy sorting, and propose a near-linear time multistage algorithm that achieves a near-optimal rate. Finally, we study the strong stochastic transitivity model that significantly generalizes the noisy sorting model for estimation from pairwise comparisons. Our efficient algorithm achieves the rate (n- 3 /4 ), narrowing a gap between the statistically optimal rate Õ(n-1 ) and the state-of-the-art computationally efficient rate [Theta] (n- 1/ 2 ). In addition, we consider the scenario where a fixed subset of pairwise comparisons is given. A dichotomy exists between the worst-case design, where consistent estimation is often impossible, and an average-case design, where we show that the optimal rate of estimation depends on the degree sequence of the comparison topology.
by Cheng Mao.
Ph. D.

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Dallaire, Patrick. "Bayesian nonparametric latent variable models." Doctoral thesis, Université Laval, 2016. http://hdl.handle.net/20.500.11794/26848.

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L’un des problèmes importants en apprentissage automatique est de déterminer la complexité du modèle à apprendre. Une trop grande complexité mène au surapprentissage, ce qui correspond à trouver des structures qui n’existent pas réellement dans les données, tandis qu’une trop faible complexité mène au sous-apprentissage, c’est-à-dire que l’expressivité du modèle est insuffisante pour capturer l’ensemble des structures présentes dans les données. Pour certains modèles probabilistes, la complexité du modèle se traduit par l’introduction d’une ou plusieurs variables cachées dont le rôle est d’expliquer le processus génératif des données. Il existe diverses approches permettant d’identifier le nombre approprié de variables cachées d’un modèle. Cette thèse s’intéresse aux méthodes Bayésiennes nonparamétriques permettant de déterminer le nombre de variables cachées à utiliser ainsi que leur dimensionnalité. La popularisation des statistiques Bayésiennes nonparamétriques au sein de la communauté de l’apprentissage automatique est assez récente. Leur principal attrait vient du fait qu’elles offrent des modèles hautement flexibles et dont la complexité s’ajuste proportionnellement à la quantité de données disponibles. Au cours des dernières années, la recherche sur les méthodes d’apprentissage Bayésiennes nonparamétriques a porté sur trois aspects principaux : la construction de nouveaux modèles, le développement d’algorithmes d’inférence et les applications. Cette thèse présente nos contributions à ces trois sujets de recherches dans le contexte d’apprentissage de modèles à variables cachées. Dans un premier temps, nous introduisons le Pitman-Yor process mixture of Gaussians, un modèle permettant l’apprentissage de mélanges infinis de Gaussiennes. Nous présentons aussi un algorithme d’inférence permettant de découvrir les composantes cachées du modèle que nous évaluons sur deux applications concrètes de robotique. Nos résultats démontrent que l’approche proposée surpasse en performance et en flexibilité les approches classiques d’apprentissage. Dans un deuxième temps, nous proposons l’extended cascading Indian buffet process, un modèle servant de distribution de probabilité a priori sur l’espace des graphes dirigés acycliques. Dans le contexte de réseaux Bayésien, ce prior permet d’identifier à la fois la présence de variables cachées et la structure du réseau parmi celles-ci. Un algorithme d’inférence Monte Carlo par chaîne de Markov est utilisé pour l’évaluation sur des problèmes d’identification de structures et d’estimation de densités. Dans un dernier temps, nous proposons le Indian chefs process, un modèle plus général que l’extended cascading Indian buffet process servant à l’apprentissage de graphes et d’ordres. L’avantage du nouveau modèle est qu’il admet les connections entres les variables observables et qu’il prend en compte l’ordre des variables. Nous présentons un algorithme d’inférence Monte Carlo par chaîne de Markov avec saut réversible permettant l’apprentissage conjoint de graphes et d’ordres. L’évaluation est faite sur des problèmes d’estimations de densité et de test d’indépendance. Ce modèle est le premier modèle Bayésien nonparamétrique permettant d’apprendre des réseaux Bayésiens disposant d’une structure complètement arbitraire.
One of the important problems in machine learning is determining the complexity of the model to learn. Too much complexity leads to overfitting, which finds structures that do not actually exist in the data, while too low complexity leads to underfitting, which means that the expressiveness of the model is insufficient to capture all the structures present in the data. For some probabilistic models, the complexity depends on the introduction of one or more latent variables whose role is to explain the generative process of the data. There are various approaches to identify the appropriate number of latent variables of a model. This thesis covers various Bayesian nonparametric methods capable of determining the number of latent variables to be used and their dimensionality. The popularization of Bayesian nonparametric statistics in the machine learning community is fairly recent. Their main attraction is the fact that they offer highly flexible models and their complexity scales appropriately with the amount of available data. In recent years, research on Bayesian nonparametric learning methods have focused on three main aspects: the construction of new models, the development of inference algorithms and new applications. This thesis presents our contributions to these three topics of research in the context of learning latent variables models. Firstly, we introduce the Pitman-Yor process mixture of Gaussians, a model for learning infinite mixtures of Gaussians. We also present an inference algorithm to discover the latent components of the model and we evaluate it on two practical robotics applications. Our results demonstrate that the proposed approach outperforms, both in performance and flexibility, the traditional learning approaches. Secondly, we propose the extended cascading Indian buffet process, a Bayesian nonparametric probability distribution on the space of directed acyclic graphs. In the context of Bayesian networks, this prior is used to identify the presence of latent variables and the network structure among them. A Markov Chain Monte Carlo inference algorithm is presented and evaluated on structure identification problems and as well as density estimation problems. Lastly, we propose the Indian chefs process, a model more general than the extended cascading Indian buffet process for learning graphs and orders. The advantage of the new model is that it accepts connections among observable variables and it takes into account the order of the variables. We also present a reversible jump Markov Chain Monte Carlo inference algorithm which jointly learns graphs and orders. Experiments are conducted on density estimation problems and testing independence hypotheses. This model is the first Bayesian nonparametric model capable of learning Bayesian learning networks with completely arbitrary graph structures.

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Книги з теми "Latent"

Langeheine, Rolf, and Jürgen Rost, eds. Latent Trait and Latent Class Models. Boston, MA: Springer US, 1988. http://dx.doi.org/10.1007/978-1-4757-5644-9.

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Rolf, Langeheine, Rost Jürgen, and Universität Kiel. Institut für die Pädagogik der Naturwissenschaften., eds. Latent trait and latent class models. New York: Plenum Press, 1988.

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Collins, Linda M. Latent class and latent transition analysis. Hoboken, N.J: Wiley, 2010.

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A, Marcoulides George, and Moustaki Irini, eds. Latent variable and latent structure models. Mahwah, N.J: Lawrence Earlbaum Publishers, 2002.

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Lubow, R. E., and Ina Weiner, eds. Latent Inhibition. Cambridge: Cambridge University Press, 2009. http://dx.doi.org/10.1017/cbo9780511730184.

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Forrellad, Luisa. Foc latent. Barcelona: Angle Editorial, 2006.

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Barbu, Ion. Nadir latent. Bucarest: Minerva, 1985.

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Royal Institute of British Architects., ed. Latent defects. London: RIBA, 1990.

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Frühwirth, Michaela. Latent image. Amsterdam: Roma Publications, 2016.

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Ghasi, Samuel. Latent error. Enugu, Nigeria: Rhyce Kerex Publishers, 2016.

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Частини книг з теми "Latent"

Herwig, Heinz. "Latente Wärme (latent heat)." In Wärmeübertragung A-Z, 137–41. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-56940-1_32.

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Flaherty, Brian P., and Cara J. Kiff. "Latent class and latent profile models." In APA handbook of research methods in psychology, Vol 3: Data analysis and research publication., 391–404. Washington: American Psychological Association, 2012. http://dx.doi.org/10.1037/13621-019.

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Miller, Laura T., Lionel Stange, Charles MacVean, Jorge R. Rey, J. H. Frank, R. F. Mizell, John B. Heppner, et al. "Latent Infection." In Encyclopedia of Entomology, 2142. Dordrecht: Springer Netherlands, 2008. http://dx.doi.org/10.1007/978-1-4020-6359-6_1969.

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Miller, Laura T., Lionel Stange, Charles MacVean, Jorge R. Rey, J. H. Frank, R. F. Mizell, John B. Heppner, et al. "Latent Learning." In Encyclopedia of Entomology, 2142. Dordrecht: Springer Netherlands, 2008. http://dx.doi.org/10.1007/978-1-4020-6359-6_1970.

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Fernandes, Rodney A. "Latent Functionality." In Protecting-Group-Free Organic Synthesis, 229–57. Chichester, UK: John Wiley & Sons, Ltd, 2018. http://dx.doi.org/10.1002/9781119295266.ch9.

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Fischer, Gabriele, Annemarie Unger, W. Wolfgang Fleischhacker, Cécile Viollet, Jacques Epelbaum, Daniel Hoyer, Ina Weiner, et al. "Latent Inhibition." In Encyclopedia of Psychopharmacology, 686–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-540-68706-1_344.

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Pierrehumbert, Ray. "Latent Heat." In Encyclopedia of Astrobiology, 913. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-11274-4_130.

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Kramer, Oliver. "Latent Sorting." In Dimensionality Reduction with Unsupervised Nearest Neighbors, 55–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38652-7_5.

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Franzen, Michael D. "Latent Variable." In Encyclopedia of Clinical Neuropsychology, 1434. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-0-387-79948-3_1212.

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Malik, Jamil A., Theresa A. Morgan, Falk Kiefer, Mustafa Al’Absi, Anna C. Phillips, Patricia Cristine Heyn, Katherine S. Hall, et al. "Latent Variable." In Encyclopedia of Behavioral Medicine, 1145–47. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-1005-9_758.

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Тези доповідей конференцій з теми "Latent"

Sankaran, Anush, Tejas I. Dhamecha, Mayank Vatsa, and Richa Singh. "On matching latent to latent fingerprints." In 2011 IEEE International Joint Conference on Biometrics (IJCB). IEEE, 2011. http://dx.doi.org/10.1109/ijcb.2011.6117525.

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"Latent Ambiguity in Latent Semantic Analysis?" In International Conference on Pattern Recognition Applications and Methods. SciTePress - Science and and Technology Publications, 2013. http://dx.doi.org/10.5220/0004178301150120.

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Nakjai, Pisit, Jiradej Ponsawat, and Tatpong Katanyukul. "Latent cognizance." In the 2nd International Conference. New York, New York, USA: ACM Press, 2019. http://dx.doi.org/10.1145/3357254.3357266.

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Korman, Simon, and Roee Litman. "Latent RANSAC." In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2018. http://dx.doi.org/10.1109/cvpr.2018.00700.

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Agarwal, Deepak, and Bee-Chung Chen. "Latent OLAP." In the 2011 international conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/1989323.1989415.

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Beutel, Alex, Paul Covington, Sagar Jain, Can Xu, Jia Li, Vince Gatto, and Ed H. Chi. "Latent Cross." In WSDM 2018: The Eleventh ACM International Conference on Web Search and Data Mining. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3159652.3159727.

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Anadol, Refik. "Latent History." In MM '19: The 27th ACM International Conference on Multimedia. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3343031.3355700.

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Hu, Bo. "Generalized Latent Interdependence Model and Latent Nonindependence Model." In 2020 AERA Annual Meeting. Washington DC: AERA, 2020. http://dx.doi.org/10.3102/1577529.

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Koltcov, Sergei, Olessia Koltsova, and Sergey Nikolenko. "Latent dirichlet allocation." In the 2014 ACM conference. New York, New York, USA: ACM Press, 2014. http://dx.doi.org/10.1145/2615569.2615680.

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Pasternack, Jeff, and Dan Roth. "Latent credibility analysis." In the 22nd international conference. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2488388.2488476.

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Звіти організацій з теми "Latent"

Henn, Soeren, and James Robinson. Africa's Latent Assets. Cambridge, MA: National Bureau of Economic Research, March 2021. http://dx.doi.org/10.3386/w28603.

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Grinfeld, Michael A., and Steven B. Segletes. Latent Work and Latent Heat of the Liquid/Vapor Transformation. Fort Belvoir, VA: Defense Technical Information Center, August 2014. http://dx.doi.org/10.21236/ada608749.

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Taylor, Melissa, Will Chapman, Austin Hicklin, George Kiebuzinski, John Mayer-Splain, Rachel Wallner, and Peter Komarinski. Latent Interoperability Transmission Specification. National Institute of Standards and Technology, January 2013. http://dx.doi.org/10.6028/nist.sp.1152.

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Garris, Michael D. Latent fingerprint training with NIST special database 27 and universal latent workstation. Gaithersburg, MD: National Institute of Standards and Technology, 2001. http://dx.doi.org/10.6028/nist.ir.6799.

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Massague, Joan. Dissecting and Targeting Latent Metastasis. Fort Belvoir, VA: Defense Technical Information Center, September 2013. http://dx.doi.org/10.21236/ada605186.

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Lee, Paul, Haiying Guan, Andrew Dienstfrey, Mary Theofanos, Brian Stanton, and Matthew T. Schwarz. Forensic latent fingerprint preprocessing assessment. Gaithersburg, MD: National Institute of Standards and Technology, June 2018. http://dx.doi.org/10.6028/nist.ir.8215.

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Lettau, Martin, and Markus Pelger. Estimating Latent Asset-Pricing Factors. Cambridge, MA: National Bureau of Economic Research, May 2018. http://dx.doi.org/10.3386/w24618.

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Bonhomme, Stéphane, and Manuel Arellano. Recovering Latent Variables by Matching. The IFS, January 2020. http://dx.doi.org/10.1920/wp.cem.2020.220.

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Meagher, S., and V. Dvornychenko. Defining AFIS latent print "Lights-Out. Gaithersburg, MD: National Institute of Standards and Technology, 2011. http://dx.doi.org/10.6028/nist.ir.7811.

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Schennach, Susanne M. Entropic Latent Variable Integration via Simulation. Cemmap, July 2013. http://dx.doi.org/10.1920/wp.cem.2013.3213.

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