Literatura científica selecionada sobre o tema "Latent block models"
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Artigos de revistas sobre o assunto "Latent block models"
Wyse, Jason, e Nial Friel. "Block clustering with collapsed latent block models". Statistics and Computing 22, n.º 2 (5 de maio de 2011): 415–28. http://dx.doi.org/10.1007/s11222-011-9233-4.
Texto completo da fonteBartolucci, Francesco, Silvia Pandolfi e Fulvia Pennoni. "Discrete Latent Variable Models". Annual Review of Statistics and Its Application 9, n.º 1 (7 de março de 2022): 425–52. http://dx.doi.org/10.1146/annurev-statistics-040220-091910.
Texto completo da fonteWatanabe, Chihiro, e Taiji Suzuki. "Goodness-of-fit test for latent block models". Computational Statistics & Data Analysis 154 (fevereiro de 2021): 107090. http://dx.doi.org/10.1016/j.csda.2020.107090.
Texto completo da fonteNorget, Julia, e Axel Mayer. "Block-Wise Model Fit for Structural Equation Models With Experience Sampling Data". Zeitschrift für Psychologie 230, n.º 1 (janeiro de 2022): 47–59. http://dx.doi.org/10.1027/2151-2604/a000482.
Texto completo da fonteMoron-Lopez, Sara, Sushama Telwatte, Indra Sarabia, Emilie Battivelli, Mauricio Montano, Amanda B. Macedo, Dvir Aran et al. "Human splice factors contribute to latent HIV infection in primary cell models and blood CD4+ T cells from ART-treated individuals". PLOS Pathogens 16, n.º 11 (30 de novembro de 2020): e1009060. http://dx.doi.org/10.1371/journal.ppat.1009060.
Texto completo da fonteMariadassou, Mahendra, e Catherine Matias. "Convergence of the groups posterior distribution in latent or stochastic block models". Bernoulli 21, n.º 1 (fevereiro de 2015): 537–73. http://dx.doi.org/10.3150/13-bej579.
Texto completo da fonteSANTOS, Naiara Caroline Aparecido dos, e Jorge Luiz BAZÁN. "RESIDUAL ANALYSIS IN RASCH POISSON COUNTS MODELS". REVISTA BRASILEIRA DE BIOMETRIA 39, n.º 1 (31 de março de 2021): 206–20. http://dx.doi.org/10.28951/rbb.v39i1.531.
Texto completo da fonteKihal-Talantikite, Wahida, Pauline Le Nouveau, Pierre Legendre, Denis Zmirou Navier, Arlette Danzon, Marion Carayol e Séverine Deguen. "Adverse Birth Outcomes as Indicators of Poor Fetal Growth Conditions in a French Newborn Population—A Stratified Analysis by Neighborhood Deprivation Level". International Journal of Environmental Research and Public Health 16, n.º 21 (23 de outubro de 2019): 4069. http://dx.doi.org/10.3390/ijerph16214069.
Texto completo da fonteXie, Fangzheng, e Yanxun Xu. "Optimal Bayesian estimation for random dot product graphs". Biometrika 107, n.º 4 (6 de julho de 2020): 875–89. http://dx.doi.org/10.1093/biomet/asaa031.
Texto completo da fonteGong, Shiqi, Peiyan Hu, Qi Meng, Yue Wang, Rongchan Zhu, Bingguang Chen, Zhiming Ma, Hao Ni e Tie-Yan Liu. "Deep Latent Regularity Network for Modeling Stochastic Partial Differential Equations". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 6 (26 de junho de 2023): 7740–47. http://dx.doi.org/10.1609/aaai.v37i6.25938.
Texto completo da fonteTeses / dissertações sobre o assunto "Latent block models"
Corneli, Marco. "Dynamic stochastic block models, clustering and segmentation in dynamic graphs". Thesis, Paris 1, 2017. http://www.theses.fr/2017PA01E012/document.
Texto completo da fonteThis thesis focuses on the statistical analysis of dynamic graphs, both defined in discrete or continuous time. We introduce a new extension of the stochastic block model (SBM) for dynamic graphs. The proposed approach, called dSBM, adopts non homogeneous Poisson processes to model the interaction times between pairs of nodes in dynamic graphs, either in discrete or continuous time. The intensity functions of the processes only depend on the node clusters, in a block modelling perspective. Moreover, all the intensity functions share some regularity properties on hidden time intervals that need to be estimated. A recent estimation algorithm for SBM, based on the greedy maximization of an exact criterion (exact ICL) is adopted for inference and model selection in dSBM. Moreover, an exact algorithm for change point detection in time series, the "pruned exact linear time" (PELT) method is extended to deal with dynamic graph data modelled via dSBM. The approach we propose can be used for change point analysis in graph data. Finally, a further extension of dSBM is developed to analyse dynamic net- works with textual edges (like social networks, for instance). In this context, the graph edges are associated with documents exchanged between the corresponding vertices. The textual content of the documents can provide additional information about the dynamic graph topological structure. The new model we propose is called "dynamic stochastic topic block model" (dSTBM).Graphs are mathematical structures very suitable to model interactions between objects or actors of interest. Several real networks such as communication networks, financial transaction networks, mobile telephone networks and social networks (Facebook, Linkedin, etc.) can be modelled via graphs. When observing a network, the time variable comes into play in two different ways: we can study the time dates at which the interactions occur and/or the interaction time spans. This thesis only focuses on the first time dimension and each interaction is assumed to be instantaneous, for simplicity. Hence, the network evolution is given by the interaction time dates only. In this framework, graphs can be used in two different ways to model networks. Discrete time […] Continuous time […]. In this thesis both these perspectives are adopted, alternatively. We consider new unsupervised methods to cluster the vertices of a graph into groups of homogeneous connection profiles. In this manuscript, the node groups are assumed to be time invariant to avoid possible identifiability issues. Moreover, the approaches that we propose aim to detect structural changes in the way the node clusters interact with each other. The building block of this thesis is the stochastic block model (SBM), a probabilistic approach initially used in social sciences. The standard SBM assumes that the nodes of a graph belong to hidden (disjoint) clusters and that the probability of observing an edge between two nodes only depends on their clusters. Since no further assumption is made on the connection probabilities, SBM is a very flexible model able to detect different network topologies (hubs, stars, communities, etc.)
Febrissy, Mickaël. "Nonnegative Matrix Factorization and Probabilistic Models : A unified framework for text data". Electronic Thesis or Diss., Paris, CNAM, 2021. http://www.theses.fr/2021CNAM1291.
Texto completo da fonteSince the exponential growth of available Data (Big data), dimensional reduction techniques became essential for the exploration and analysis of high-dimensional data arising from many scientific areas. By creating a low-dimensional space intrinsic to the original data space, theses techniques offer better understandings across many data Science applications. In the context of text analysis where the data gathered are mainly nonnegative, recognized techniques producing transformations in the space of real numbers (e.g. Principal component analysis, Latent semantic analysis) became less intuitive as they could not provide a straightforward interpretation. Such applications show the need of dimensional reduction techniques like Nonnegative Matrix factorization (NMF) useful to embed, for instance, documents or words in the space of reduced dimension. By definition, NMF aims at approximating a nonnegative matrix by the product of two lower dimensionalnonnegative matrices, which results in the solving of a nonlinear optimization problem. Note however that this objective can be harnessed to document/word clustering domain even it is not the objective of NMF. In relying on NMF, this thesis focuses on improving clustering of large text data arising in the form of highly sparse document-term matrices. This objective is first achieved, by proposing several types of regularizations of the original NMF objective function. Setting this objective in a probabilistic context, a new NMF model is introduced bringing theoretical foundations for establishing the connection between NMF and Finite Mixture Models of exponential families leading, therefore, to offer interesting regularizations. This allows to set NMF in a real clustering spirit. Finally, a Bayesian Poisson Latent Block model is proposed to improve document andword clustering simultaneously by capturing noisy term features. This can be connected to NMTF (Nonnegative Matrix factorization Tri-factorization) devoted to co-clustering. Experiments on real datasets have been carried out to support the proposals of the thesis
Galindo-Prieto, Beatriz. "Novel variable influence on projection (VIP) methods in OPLS, O2PLS, and OnPLS models for single- and multi-block variable selection : VIPOPLS, VIPO2PLS, and MB-VIOP methods". Doctoral thesis, Umeå universitet, Kemiska institutionen, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-130579.
Texto completo da fonteRobert, Valérie. "Classification croisée pour l'analyse de bases de données de grandes dimensions de pharmacovigilance". Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLS111/document.
Texto completo da fonteThis thesis gathers methodological contributions to the statistical analysis of large datasets in pharmacovigilance. The pharmacovigilance datasets produce sparse and large matrices and these two characteritics are the main statistical challenges for modelling them. The first part of the thesis is dedicated to the coclustering of the pharmacovigilance contingency table thanks to the normalized Poisson latent block model. The objective is on the one hand, to provide pharmacologists with some interesting and reduced areas to explore more precisely. On the other hand, this coclustering remains a useful background information for dealing with individual database. Within this framework, a parameter estimation procedure for this model is detailed and objective model selection criteria are developed to choose the best fit model. Datasets are so large that we propose a procedure to explore the model space in coclustering, in a non exhaustive way but a relevant one. Additionnally, to assess the performances of the methods, a convenient coclustering index is developed to compare partitions with high numbers of clusters. The developments of these statistical tools are not specific to pharmacovigilance and can be used for any coclustering issue. The second part of the thesis is devoted to the statistical analysis of the large individual data, which are more numerous but also provides even more valuable information. The aim is to produce individual clusters according their drug profiles and subgroups of drugs and adverse effects with possible links, which overcomes the coprescription and masking phenomenons, common contingency table issues in pharmacovigilance. Moreover, the interaction between several adverse effects is taken into account. For this purpose, we propose a new model, the multiple latent block model which enables to cocluster two binary tables by imposing the same row ranking. Assertions inherent to the model are discussed and sufficient identifiability conditions for the model are presented. Then a parameter estimation algorithm is studied and objective model selection criteria are developed. Moreover, a numeric simulation model of the individual data is proposed to compare existing methods and study its limits. Finally, the proposed methodology to deal with individual pharmacovigilance data is presented and applied to a sample of the French pharmacovigilance database between 2002 and 2010
Brault, Vincent. "Estimation et sélection de modèle pour le modèle des blocs latents". Thesis, Paris 11, 2014. http://www.theses.fr/2014PA112238/document.
Texto completo da fonteClassification aims at sharing data sets in homogeneous subsets; the observations in a class are more similar than the observations of other classes. The problem is compounded when the statistician wants to obtain a cross classification on the individuals and the variables. The latent block model uses a law for each crossing object class and class variables, and observations are assumed to be independent conditionally on the choice of these classes. However, factorizing the joint distribution of the labels is impossible, obstructing the calculation of the log-likelihood and the using of the EM algorithm. Several methods and criteria exist to find these partitions, some frequentist ones, some bayesian ones, some stochastic ones... In this thesis, we first proposed sufficient conditions to obtain the identifiability of the model. In a second step, we studied two proposed algorithms to counteract the problem of the EM algorithm: the VEM algorithm (Govaert and Nadif (2008)) and the SEM-Gibbs algorithm (Keribin, Celeux and Govaert (2010)). In particular, we analyzed the combination of both and highlighted why the algorithms degenerate (term used to say that it returns empty classes). By choosing priors wise, we then proposed a Bayesian adaptation to limit this phenomenon. In particular, we used a Gibbs sampler and we proposed a stopping criterion based on the statistics of Brooks-Gelman (1998). We also proposed an adaptation of the Largest Gaps algorithm (Channarond et al. (2012)). By taking their demonstrations, we have shown that the labels and parameters estimators obtained are consistent when the number of rows and columns tend to infinity. Furthermore, we proposed a method to select the number of classes in row and column, the estimation provided is also consistent when the number of row and column is very large. To estimate the number of classes, we studied the ICL criterion (Integrated Completed Likelihood) whose we proposed an exact shape. After studying the asymptotic approximation, we proposed a BIC criterion (Bayesian Information Criterion) and we conjecture that the two criteria select the same results and these estimates are consistent; conjecture supported by theoretical and empirical results. Finally, we compared the different combinations and proposed a methodology for co-clustering
Tami, Myriam. "Approche EM pour modèles multi-blocs à facteurs à une équation structurelle". Thesis, Montpellier, 2016. http://www.theses.fr/2016MONTT303/document.
Texto completo da fonteStructural equation models enable the modeling of interactions between observed variables and latent ones. The two leading estimation methods are partial least squares on components and covariance-structure analysis. In this work, we first describe the PLS and LISREL methods and, then, we propose an estimation method using the EM algorithm in order to maximize the likelihood of a structural equation model with latent factors. Through a simulation study, we investigate how fast and accurate the method is, and thanks to an application to real environmental data, we show how one can handly construct a model or evaluate its quality. Finally, in the context of oncology, we apply the EM approach on health-related quality-of-life data. We show that it simplifies the longitudinal analysis of quality-of-life and helps evaluating the clinical benefit of a treatment
Laclau, Charlotte. "Hard and fuzzy block clustering algorithms for high dimensional data". Thesis, Sorbonne Paris Cité, 2016. http://www.theses.fr/2016USPCB014.
Texto completo da fonteWith the increasing number of data available, unsupervised learning has become an important tool used to discover underlying patterns without the need to label instances manually. Among different approaches proposed to tackle this problem, clustering is arguably the most popular one. Clustering is usually based on the assumption that each group, also called cluster, is distributed around a center defined in terms of all features while in some real-world applications dealing with high-dimensional data, this assumption may be false. To this end, co-clustering algorithms were proposed to describe clusters by subsets of features that are the most relevant to them. The obtained latent structure of data is composed of blocks usually called co-clusters. In first two chapters, we describe two co-clustering methods that proceed by differentiating the relevance of features calculated with respect to their capability of revealing the latent structure of the data in both probabilistic and distance-based framework. The probabilistic approach uses the mixture model framework where the irrelevant features are assumed to have a different probability distribution that is independent of the co-clustering structure. On the other hand, the distance-based (also called metric-based) approach relied on the adaptive metric where each variable is assigned with its weight that defines its contribution in the resulting co-clustering. From the theoretical point of view, we show the global convergence of the proposed algorithms using Zangwill convergence theorem. In the last two chapters, we consider a special case of co-clustering where contrary to the original setting, each subset of instances is described by a unique subset of features resulting in a diagonal structure of the initial data matrix. Same as for the two first contributions, we consider both probabilistic and metric-based approaches. The main idea of the proposed contributions is to impose two different kinds of constraints: (1) we fix the number of row clusters to the number of column clusters; (2) we seek a structure of the original data matrix that has the maximum values on its diagonal (for instance for binary data, we look for diagonal blocks composed of ones with zeros outside the main diagonal). The proposed approaches enjoy the convergence guarantees derived from the results of the previous chapters. Finally, we present both hard and fuzzy versions of the proposed algorithms. We evaluate our contributions on a wide variety of synthetic and real-world benchmark binary and continuous data sets related to text mining applications and analyze advantages and inconvenients of each approach. To conclude, we believe that this thesis covers explicitly a vast majority of possible scenarios arising in hard and fuzzy co-clustering and can be seen as a generalization of some popular biclustering approaches
Laclau, Charlotte. "Hard and fuzzy block clustering algorithms for high dimensional data". Electronic Thesis or Diss., Sorbonne Paris Cité, 2016. http://www.theses.fr/2016USPCB014.
Texto completo da fonteWith the increasing number of data available, unsupervised learning has become an important tool used to discover underlying patterns without the need to label instances manually. Among different approaches proposed to tackle this problem, clustering is arguably the most popular one. Clustering is usually based on the assumption that each group, also called cluster, is distributed around a center defined in terms of all features while in some real-world applications dealing with high-dimensional data, this assumption may be false. To this end, co-clustering algorithms were proposed to describe clusters by subsets of features that are the most relevant to them. The obtained latent structure of data is composed of blocks usually called co-clusters. In first two chapters, we describe two co-clustering methods that proceed by differentiating the relevance of features calculated with respect to their capability of revealing the latent structure of the data in both probabilistic and distance-based framework. The probabilistic approach uses the mixture model framework where the irrelevant features are assumed to have a different probability distribution that is independent of the co-clustering structure. On the other hand, the distance-based (also called metric-based) approach relied on the adaptive metric where each variable is assigned with its weight that defines its contribution in the resulting co-clustering. From the theoretical point of view, we show the global convergence of the proposed algorithms using Zangwill convergence theorem. In the last two chapters, we consider a special case of co-clustering where contrary to the original setting, each subset of instances is described by a unique subset of features resulting in a diagonal structure of the initial data matrix. Same as for the two first contributions, we consider both probabilistic and metric-based approaches. The main idea of the proposed contributions is to impose two different kinds of constraints: (1) we fix the number of row clusters to the number of column clusters; (2) we seek a structure of the original data matrix that has the maximum values on its diagonal (for instance for binary data, we look for diagonal blocks composed of ones with zeros outside the main diagonal). The proposed approaches enjoy the convergence guarantees derived from the results of the previous chapters. Finally, we present both hard and fuzzy versions of the proposed algorithms. We evaluate our contributions on a wide variety of synthetic and real-world benchmark binary and continuous data sets related to text mining applications and analyze advantages and inconvenients of each approach. To conclude, we believe that this thesis covers explicitly a vast majority of possible scenarios arising in hard and fuzzy co-clustering and can be seen as a generalization of some popular biclustering approaches
Schmutz, Amandine. "Contributions à l'analyse de données fonctionnelles multivariées, application à l'étude de la locomotion du cheval de sport". Thesis, Lyon, 2019. http://www.theses.fr/2019LYSE1241.
Texto completo da fonteWith the growth of smart devices market to provide athletes and trainers a systematic, objective and reliable follow-up, more and more parameters are monitored for a same individual. An alternative to laboratory evaluation methods is the use of inertial sensors which allow following the performance without hindering it, without space limits and without tedious initialization procedures. Data collected by those sensors can be classified as multivariate functional data: some quantitative entities evolving along time and collected simultaneously for a same individual. The aim of this thesis is to find parameters for analysing the athlete horse locomotion thanks to a sensor put in the saddle. This connected device (inertial sensor, IMU) for equestrian sports allows the collection of acceleration and angular velocity along time in the three space directions and with a sampling frequency of 100 Hz. The database used for model development is made of 3221 canter strides from 58 ridden jumping horses of different age and level of competition. Two different protocols are used to collect data: one for straight path and one for curved path. We restricted our work to the prediction of three parameters: the speed per stride, the stride length and the jump quality. To meet the first to objectives, we developed a multivariate functional clustering method that allow the division of the database into smaller more homogeneous sub-groups from the collected signals point of view. This method allows the characterization of each group by it average profile, which ease the data understanding and interpretation. But surprisingly, this clustering model did not improve the results of speed prediction, Support Vector Machine (SVM) is the model with the lowest percentage of error above 0.6 m/s. The same applied for the stride length where an accuracy of 20 cm is reached thanks to SVM model. Those results can be explained by the fact that our database is build from 58 horses only, which is a quite low number of individuals for a clustering method. Then we extend this method to the co-clustering of multivariate functional data in order to ease the datamining of horses’ follow-up databases. This method might allow the detection and prevention of locomotor disturbances, main source of interruption of jumping horses. Lastly, we looked for correlation between jumping quality and signals collected by the IMU. First results show that signals collected by the saddle alone are not sufficient to differentiate finely the jumping quality. Additional information will be needed, for example using complementary sensors or by expanding the database to have a more diverse range of horses and jump profiles
Ben, slimen Yosra. "Knowledge extraction from huge volume of heterogeneous data for an automated radio network management". Thesis, Lyon, 2018. http://www.theses.fr/2018LYSE2046.
Texto completo da fonteIn order to help the mobile operators with the management of their radio access networks, three models are proposed. The first model is a supervised approach for mobile anomalies prevention. Its objective is to detect future malfunctions of a set of cells, by only observing key performance indicators (KPIs) that are considered as functional data. Thus, by alerting the engineers as well as self-organizing networks, mobile operators can be saved from a certain performance degradation. The model has proven its efficiency with an application on real data that aims to detect capacity degradation, accessibility and call drops anomalies for LTE networks.Due to the diversity of mobile network technologies, the volume of data that has to be observed by mobile operators in a daily basis became enormous. This huge volume became an obstacle to mobile networks management. The second model aims to provide a simplified representation of KPIs for an easier analysis. Hence, a model-based co-clustering algorithm for functional data is proposed. The algorithm relies on the latent block model in which each curve is identified by its functional principal components that are modeled by a multivariate Gaussian distribution whose parameters are block-specific. These latter are estimated by a stochastic EM algorithm embedding a Gibbs sampling. This model is the first co-clustering approach for functional data and it has proven its efficiency on simulated data and on a real data application that helps to optimize the topology of 4G mobile networks.The third model aims to resume the information of data issued from KPIs and also alarms. A model-based co-clustering algorithm for mixed data, functional and binary, is therefore proposed. The approach relies on the latent block model, and three algorithms are compared for its inference: stochastic EM within Gibbs sampling, classification EM and variational EM. The proposed model is the first co-clustering algorithm for mixed data that deals with functional and binary features. It has proven its efficiency on simulated data and on real data extracted from live 4G mobile networks
Livros sobre o assunto "Latent block models"
Fedorov, Viktor, e Mihail San'kov. Management: theory and practice. ru: INFRA-M Academic Publishing LLC., 2023. http://dx.doi.org/10.12737/1859086.
Texto completo da fonteEfremov, German. Modeling of chemical and technological processes. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1090526.
Texto completo da fonteKuz'mina, Natal'ya. Criminology and crime prevention. ru: INFRA-M Academic Publishing LLC., 2023. http://dx.doi.org/10.12737/1900600.
Texto completo da fonteFrom the Norman Conquest to the Black Death: An anthology of writings from England. Oxford: Oxford University Press, 2011.
Encontre o texto completo da fonteThe Black campus movement: Black students and the racial reconstitution of higher education, 1965-1972. New York: Palgrave Macmillan, 2012.
Encontre o texto completo da fonte1941-, Schumacher Ulrich, Lotz Rouven e Emil Schumacher Museum, eds. Karel Appel: Der abstrakte Blick. Hagen: Emil Schumacher Museum Hagen, 2016.
Encontre o texto completo da fonteRankine, Patrice D. Ulysses in Black: Ralph Ellison, classicism, and African American literature. Madison, WS: University of Wisconsin Press, 2007.
Encontre o texto completo da fonteSucci, Sauro. Lattice Boltzmann Models without Underlying Boolean Microdynamics. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199592357.003.0013.
Texto completo da fontePalomäki, Outi, e Petri Volmanen. Alternative neural blocks for labour analgesia. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780198713333.003.0018.
Texto completo da fonteHoffnung-Garskof, Jesse E. Racial Migrations. Princeton University Press, 2019. http://dx.doi.org/10.23943/princeton/9780691183534.001.0001.
Texto completo da fonteCapítulos de livros sobre o assunto "Latent block models"
Boutalbi, Rafika, Lazhar Labiod e Mohamed Nadif. "Latent Block Regression Model". In Studies in Classification, Data Analysis, and Knowledge Organization, 73–81. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-09034-9_9.
Texto completo da fonteKhoufache, Reda, Anisse Belhadj, Hanene Azzag e Mustapha Lebbah. "Distributed MCMC Inference for Bayesian Non-parametric Latent Block Model". In Advances in Knowledge Discovery and Data Mining, 271–83. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-2242-6_22.
Texto completo da fonteLücke, Jörg, Zhenwen Dai e Georgios Exarchakis. "Truncated Variational Sampling for ‘Black Box’ Optimization of Generative Models". In Latent Variable Analysis and Signal Separation, 467–78. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93764-9_43.
Texto completo da fonteGuarino, Stefano, Enrico Mastrostefano e Davide Torre. "The Hyperbolic Geometric Block Model and Networks with Latent and Explicit Geometries". In Complex Networks and Their Applications XI, 109–21. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-21131-7_9.
Texto completo da fonteSalinas Ruíz, Josafhat, Osval Antonio Montesinos López, Gabriela Hernández Ramírez e Jose Crossa Hiriart. "Generalized Linear Mixed Models for Repeated Measurements". In Generalized Linear Mixed Models with Applications in Agriculture and Biology, 377–423. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-32800-8_9.
Texto completo da fonteOsborne, Martin J., e Ariel Rubinstein. "Choice". In Models in Microeconomic Theory, 17–30. 2a ed. Cambridge, UK: Open Book Publishers, 2023. http://dx.doi.org/10.11647/obp.0362.02.
Texto completo da fonteOsborne, Martin J., e Ariel Rubinstein. "Choice". In Models in Microeconomic Theory, 17–30. 2a ed. Cambridge, UK: Open Book Publishers, 2023. http://dx.doi.org/10.11647/obp.0361.02.
Texto completo da fonteBanaś, Monika. "Women's “Black Protest” in Poland". In Protest in Late Modern Societies, 117–31. London: Routledge, 2023. http://dx.doi.org/10.4324/9781003270065-9.
Texto completo da fonteMarchello, Giulia, Marco Corneli e Charles Bouveyron. "A Deep Dynamic Latent Block Model for the Co-Clustering of Zero-Inflated Data Matrices". In Machine Learning and Knowledge Discovery in Databases: Research Track, 695–710. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43412-9_41.
Texto completo da fonteLampridis, Orestis, Riccardo Guidotti e Salvatore Ruggieri. "Explaining Sentiment Classification with Synthetic Exemplars and Counter-Exemplars". In Discovery Science, 357–73. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61527-7_24.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Latent block models"
Li, Changsheng, Handong Ma, Zhao Kang, Ye Yuan, Xiao-Yu Zhang e Guoren Wang. "On Deep Unsupervised Active Learning". In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/364.
Texto completo da fonteZhu, Feida, Junwei Zhu, Wenqing Chu, Ying Tai, Zhifeng Xie, Xiaoming Huang e Chengjie Wang. "HifiHead: One-Shot High Fidelity Neural Head Synthesis with 3D Control". In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/244.
Texto completo da fonteRywik, Marcin, Axel Zimmermann, Alexander J. Eder, Edoardo Scoletta e Wolfgang Polifke. "Spatially Resolved Modeling of the Nonlinear Dynamics of a Laminar Premixed Flame With a Multilayer Perceptron - Convolution Autoencoder Network". In ASME Turbo Expo 2023: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2023. http://dx.doi.org/10.1115/gt2023-102543.
Texto completo da fonteCao, Bingyi, Kenneth A. Ross, Martha A. Kim e Stephen A. Edwards. "Implementing latency-insensitive dataflow blocks". In 2015 ACM/IEEE International Conference on Formal Methods and Models for Codesign (MEMOCODE). IEEE, 2015. http://dx.doi.org/10.1109/memcod.2015.7340485.
Texto completo da fonteAilem, Melissa, Francois Role e Mohamed Nadif. "Sparse Poisson Latent Block Model for Document Clustering (Extended Abstract)". In 2018 IEEE 34th International Conference on Data Engineering (ICDE). IEEE, 2018. http://dx.doi.org/10.1109/icde.2018.00229.
Texto completo da fonteLomet, Aurore, Gerard Govaert e Yves Grandvalet. "An Approximation of the Integrated Classification Likelihood for the Latent Block Model". In 2012 IEEE 12th International Conference on Data Mining Workshops. IEEE, 2012. http://dx.doi.org/10.1109/icdmw.2012.32.
Texto completo da fonteShahiri, Mohammad, e Mahdi Eskandari. "Exact Recovery of Two-Latent Variable Stochastic Block Model with Side Information". In 2021 7th International Conference on Contemporary Information Technology and Mathematics (ICCITM). IEEE, 2021. http://dx.doi.org/10.1109/iccitm53167.2021.9677645.
Texto completo da fonteSchindler, Miguel Horacio. "Phase Envelopes From Black-Oil Models". In Latin American & Caribbean Petroleum Engineering Conference. Society of Petroleum Engineers, 2007. http://dx.doi.org/10.2118/106855-ms.
Texto completo da fonteBishop, M., X. Moonan, W. Lalla e L. Anderson. "Another Look at Bovallius, Onshore Southern Basin Trinidad". In SPE Latin American and Caribbean Petroleum Engineering Conference. SPE, 2023. http://dx.doi.org/10.2118/213174-ms.
Texto completo da fonteDoersch, Stefan, Maria Starnberg e Haike Brick. "Acoustic Certification of New Composite Brake Blocks". In EuroBrake 2021. FISITA, 2021. http://dx.doi.org/10.46720/1766833eb2021-stp-022.
Texto completo da fonteRelatórios de organizações sobre o assunto "Latent block models"
Chronopoulos, Ilias, Katerina Chrysikou, George Kapetanios, James Mitchell e Aristeidis Raftapostolos. Deep Neural Network Estimation in Panel Data Models. Federal Reserve Bank of Cleveland, julho de 2023. http://dx.doi.org/10.26509/frbc-wp-202315.
Texto completo da fonteZagorevski, A., e C. R. van Staal. Cordilleran magmatism in Yukon and northern British Columbia: characteristics, temporal variations, and significance for the tectonic evolution of the northern Cordillera. Natural Resources Canada/CMSS/Information Management, 2021. http://dx.doi.org/10.4095/326063.
Texto completo da fonteSentcоv, Valentin, Andrei Reutov e Vyacheslav Kuzmin. Electronic training manual "Acute poisoning with alcohols and alcohol-containing liquids". SIB-Expertise, janeiro de 2024. http://dx.doi.org/10.12731/er0778.29012024.
Texto completo da fonteTao, Yang, Amos Mizrach, Victor Alchanatis, Nachshon Shamir e Tom Porter. Automated imaging broiler chicksexing for gender-specific and efficient production. United States Department of Agriculture, dezembro de 2014. http://dx.doi.org/10.32747/2014.7594391.bard.
Texto completo da fonteChejanovsky, Nor, e Suzanne M. Thiem. Isolation of Baculoviruses with Expanded Spectrum of Action against Lepidopteran Pests. United States Department of Agriculture, dezembro de 2002. http://dx.doi.org/10.32747/2002.7586457.bard.
Texto completo da fonteKaracic, Almir, e Anneli Adler. Fertilization of poplar plantations with dried sludge : a demonstration trial in Hillebola - central Sweden. Department of Crop Production Ecology, Swedish University of Agricultural Sciences, 2023. http://dx.doi.org/10.54612/a.2q9iahfphk.
Texto completo da fonteMeir, Shimon, Michael Reid, Cai-Zhong Jiang, Amnon Lers e Sonia Philosoph-Hadas. Molecular Studies of Postharvest Leaf and Flower Abscission. United States Department of Agriculture, 2005. http://dx.doi.org/10.32747/2005.7696523.bard.
Texto completo da fonteHarris, L. B., P. Adiban e E. Gloaguen. The role of enigmatic deep crustal and upper mantle structures on Au and magmatic Ni-Cu-PGE-Cr mineralization in the Superior Province. Natural Resources Canada/CMSS/Information Management, 2021. http://dx.doi.org/10.4095/328984.
Texto completo da fonteCommunity involvement in reproductive health: Findings from research in Karnataka, India. Population Council, 2004. http://dx.doi.org/10.31899/rh17.1007.
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