Готові списки джерел за темами / Processus gaussiens latents

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Добірка наукової літератури з теми "Processus gaussiens latents"

Автор: Grafiati
Опубліковано: 10 листопада 2022

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Статті в журналах з теми "Processus gaussiens latents"

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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 (2021): 1477–93. http://dx.doi.org/10.21817/indjcse/2021/v12i5/211205124.

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Анотація:
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 wh
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Alvarez, M. A., D. Luengo, and N. D. Lawrence. "Linear Latent Force Models Using Gaussian Processes." IEEE Transactions on Pattern Analysis and Machine Intelligence 35, no. 11 (2013): 2693–705. http://dx.doi.org/10.1109/tpami.2013.86.

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Oune, Nicholas, and Ramin Bostanabad. "Latent map Gaussian processes for mixed variable metamodeling." Computer Methods in Applied Mechanics and Engineering 387 (December 2021): 114128. http://dx.doi.org/10.1016/j.cma.2021.114128.

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Panos, Aristeidis, Petros Dellaportas, and Michalis K. Titsias. "Large scale multi-label learning using Gaussian processes." Machine Learning 110, no. 5 (2021): 965–87. http://dx.doi.org/10.1007/s10994-021-05952-5.

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Анотація:
AbstractWe introduce a Gaussian process latent factor model for multi-label classification that can capture correlations among class labels by using a small set of latent Gaussian process functions. To address computational challenges, when the number of training instances is very large, we introduce several techniques based on variational sparse Gaussian process approximations and stochastic optimization. Specifically, we apply doubly stochastic variational inference that sub-samples data instances and classes which allows us to cope with Big Data. Furthermore, we show it is possible and bene
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Hall, Peter, Hans-Georg Mller, and Fang Yao. "Modelling sparse generalized longitudinal observations with latent Gaussian processes." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 70, no. 4 (2008): 703–23. http://dx.doi.org/10.1111/j.1467-9868.2008.00656.x.

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Mattos, César Lincoln C., Andreas Damianou, Guilherme A. Barreto, and Neil D. Lawrence. "Latent Autoregressive Gaussian Processes Models for Robust System Identification." IFAC-PapersOnLine 49, no. 7 (2016): 1121–26. http://dx.doi.org/10.1016/j.ifacol.2016.07.353.

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Gammelli, Daniele, Inon Peled, Filipe Rodrigues, Dario Pacino, Haci A. Kurtaran, and Francisco C. Pereira. "Estimating latent demand of shared mobility through censored Gaussian Processes." Transportation Research Part C: Emerging Technologies 120 (November 2020): 102775. http://dx.doi.org/10.1016/j.trc.2020.102775.

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Dew, Ryan, Asim Ansari, and Yang Li. "Modeling Dynamic Heterogeneity Using Gaussian Processes." Journal of Marketing Research 57, no. 1 (2019): 55–77. http://dx.doi.org/10.1177/0022243719874047.

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Анотація:
Marketing research relies on individual-level estimates to understand the rich heterogeneity of consumers, firms, and products. While much of the literature focuses on capturing static cross-sectional heterogeneity, little research has been done on modeling dynamic heterogeneity, or the heterogeneous evolution of individual-level model parameters. In this work, the authors propose a novel framework for capturing the dynamics of heterogeneity, using individual-level, latent, Bayesian nonparametric Gaussian processes. Similar to standard heterogeneity specifications, this Gaussian process dynami
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Zhang, Dongmei, Yuyang Zhang, Bohou Jiang, Xinwei Jiang, and Zhijiang Kang. "Gaussian Processes Proxy Model with Latent Variable Models and Variogram-Based Sensitivity Analysis for Assisted History Matching." Energies 13, no. 17 (2020): 4290. http://dx.doi.org/10.3390/en13174290.

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Анотація:
Reservoir history matching is a well-known inverse problem for production prediction where enormous uncertain reservoir parameters of a reservoir numerical model are optimized by minimizing the misfit between the simulated and history production data. Gaussian Process (GP) has shown promising performance for assisted history matching due to the efficient nonparametric and nonlinear model with few model parameters to be tuned automatically. Recently introduced Gaussian Processes proxy models and Variogram Analysis of Response Surface-based sensitivity analysis (GP-VARS) uses forward and inverse
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Lu, Chi-Ken, and Patrick Shafto. "Conditional Deep Gaussian Processes: Multi-Fidelity Kernel Learning." Entropy 23, no. 11 (2021): 1545. http://dx.doi.org/10.3390/e23111545.

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Анотація:
Deep Gaussian Processes (DGPs) were proposed as an expressive Bayesian model capable of a mathematically grounded estimation of uncertainty. The expressivity of DPGs results from not only the compositional character but the distribution propagation within the hierarchy. Recently, it was pointed out that the hierarchical structure of DGP well suited modeling the multi-fidelity regression, in which one is provided sparse observations with high precision and plenty of low fidelity observations. We propose the conditional DGP model in which the latent GPs are directly supported by the fixed lower
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Дисертації з теми "Processus gaussiens latents"

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Cuesta, Ramirez Jhouben Janyk. "Optimization of a computationally expensive simulator with quantitative and qualitative inputs." Thesis, Lyon, 2022. http://www.theses.fr/2022LYSEM010.

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Анотація:
Dans cette thèse, les problèmes mixtes couteux sont abordés par le biais de processus gaussiens où les variables discrètes sont relaxées en variables latentes continues. L'espace continu est plus facilement exploité par les techniques classiques d'optimisation bayésienne que ne le serait un espace mixte. Les variables discrètes sont récupérées soit après l'optimisation continue, soit simultanément avec une contrainte supplémentaire de compatibilité continue-discrète qui est traitée avec des lagrangiens augmentés. Plusieurs implémentations possibles de ces optimiseurs mixtes bayésiens sont comp
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Wenzel, Florian. "Scalable Inference in Latent Gaussian Process Models." Doctoral thesis, Humboldt-Universität zu Berlin, 2020. http://dx.doi.org/10.18452/20926.

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Анотація:
Latente Gauß-Prozess-Modelle (latent Gaussian process models) werden von Wissenschaftlern benutzt, um verborgenen Muster in Daten zu er- kennen, Expertenwissen in probabilistische Modelle einfließen zu lassen und um Vorhersagen über die Zukunft zu treffen. Diese Modelle wurden erfolgreich in vielen Gebieten wie Robotik, Geologie, Genetik und Medizin angewendet. Gauß-Prozesse definieren Verteilungen über Funktionen und können als flexible Bausteine verwendet werden, um aussagekräftige probabilistische Modelle zu entwickeln. Dabei ist die größte Herausforderung, eine geeignete Inferenzmethode zu
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Hartmann, Marcelo. "Métodos de Monte Carlo Hamiltoniano na inferência Bayesiana não-paramétrica de valores extremos." Universidade Federal de São Carlos, 2015. https://repositorio.ufscar.br/handle/ufscar/4601.

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Анотація:
Made available in DSpace on 2016-06-02T20:06:51Z (GMT). No. of bitstreams: 1 6609.pdf: 3049383 bytes, checksum: 33c7f1618f776ca50cf4694aaba80ea5 (MD5) Previous issue date: 2015-03-09<br>In this work we propose a Bayesian nonparametric approach for modeling extreme value data. We treat the location parameter _ of the generalized extreme value distribution as a random function following a Gaussian process model (Rasmussem & Williams 2006). This configuration leads to no closed-form expressions for the highdimensional posterior distribution. To tackle this problem we use the Riemannian Manifold
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Karipidou, Kelly. "Modelling the body language of a musical conductor using Gaussian Process Latent Variable Models." Thesis, KTH, Datorseende och robotik, CVAP, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-176101.

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Анотація:
Motion capture data of a musical conductor's movements when conducting a string quartet is analysed in this work using the Gaussian Process Latent Variable Model (GP-LVM) framework. A dimensionality reduction on the high dimensional motion capture data to a two dimensional representation using a GP-LVM is performed, followed by classification of conduction movements belonging to different interpretations of the same musical piece. A dynamical prior is used for the GP-LVM, resulting in a representative latent space for the sequential conduction motion data. Classification results with great per
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Mendes, Armando Praça. "A gestão da estratégia mercadologica sob uma nova perspectiva: existe relação entre a física e a administração?" reponame:Repositório Institucional do FGV, 2004. http://hdl.handle.net/10438/3884.

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Made available in DSpace on 2009-11-18T19:01:13Z (GMT). No. of bitstreams: 0 Previous issue date: 2004<br>A Física e a Administração concentram suas pesquisas sobre fenômenos que, de certa forma, se assemelham, fazendo com que nos questionemos a respeito da grande integral do universo a que estamos submetidos. Em uma exploração por analogias, aproxima-se aqui o mundo organizacional ao dos sistemas UnIVerSaIS, instáveis e não-integráveis, onde a flecha do tempo é quem determina a evolução dos mesmos. Mostra-se que na Administração, como na Física, tudo parece convergir na direção de um inesg
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Sauer, Patrick Martin. "Model-based understanding of facial expressions." Thesis, University of Manchester, 2013. https://www.research.manchester.ac.uk/portal/en/theses/modelbased-understanding-of-facial-expressions(e88bff4f-d72e-4d11-b964-fc20f009609b).html.

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Анотація:
In this thesis we present novel methods for constructing and fitting 2d models of shape and appearance which are used for analysing human faces. The first contribution builds on previous work on discriminative fitting strategies for active appearance models (AAMs) in which regression models are trained to predict the location of shapes based on texture samples. In particular, we investigate non-parametric regression methods including random forests and Gaussian processes which are used together with gradient-like features for shape model fitting. We then develop two training algorithms which c
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Hall, Otto. "Inference of buffer queue times in data processing systems using Gaussian Processes : An introduction to latency prediction for dynamic software optimization in high-end trading systems." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-214791.

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Анотація:
This study investigates whether Gaussian Process Regression can be applied to evaluate buffer queue times in large scale data processing systems. It is additionally considered whether high-frequency data stream rates can be generalized into a small subset of the sample space. With the aim of providing basis for dynamic software optimization, a promising foundation for continued research is introduced. The study is intended to contribute to Direct Market Access financial trading systems which processes immense amounts of market data daily. Due to certain limitations, we shoulder a naïve approac
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Qian, Zhiguang. "Computer experiments [electronic resource] : design, modeling and integration /." Diss., Georgia Institute of Technology, 2006. http://hdl.handle.net/1853/11480.

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Анотація:
The use of computer modeling is fast increasing in almost every scientific, engineering and business arena. This dissertation investigates some challenging issues in design, modeling and analysis of computer experiments, which will consist of four major parts. In the first part, a new approach is developed to combine data from approximate and detailed simulations to build a surrogate model based on some stochastic models. In the second part, we propose some Bayesian hierarchical Gaussian process models to integrate data from different types of experiments. The third part concerns the developm
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Ancelet, Sophie. "Exploiter l'approche hiérarchique bayésienne pour la modélisation statistique de structures spatiales: application en écologie des populations." Phd thesis, AgroParisTech, 2008. http://pastel.archives-ouvertes.fr/pastel-00004396.

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Анотація:
Dans la plupart des questions écologiques, les phénomènes aléatoires d'intérêt sont spatialement structurés et issus de l'effet combiné de multiples variables aléatoires, observées ou non, et inter-agissant à diverses échelles. En pratique, dès lors que les données de terrain ne peuvent être directement traitées avec des structures spatiales standards, les observations sont généralement considérées indépendantes. Par ailleurs, les modèles utilisés sont souvent basés sur des hypothèses simplificatrices trop fortes par rapport à la complexité des phénomènes étudiés. Dans ce travail, la démarche
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Wang, Xiaojing. "Bayesian Modeling Using Latent Structures." Diss., 2012. http://hdl.handle.net/10161/5848.

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Анотація:
<p>This dissertation is devoted to modeling complex data from the</p><p>Bayesian perspective via constructing priors with latent structures.</p><p>There are three major contexts in which this is done -- strategies for</p><p>the analysis of dynamic longitudinal data, estimating</p><p>shape-constrained functions, and identifying subgroups. The</p><p>methodology is illustrated in three different</p><p>interdisciplinary contexts: (1) adaptive measurement testing in</p><p>education; (2) emulation of computer models for vehicle crashworthiness; and (3) subgroup analyses based on biomarkers.</p><p>Ch
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Частини книг з теми "Processus gaussiens latents"

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Fantinato, Denis G., Leonardo T. Duarte, Bertrand Rivet, Bahram Ehsandoust, Romis Attux, and Christian Jutten. "Gaussian Processes for Source Separation in Overdetermined Bilinear Mixtures." In Latent Variable Analysis and Signal Separation. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53547-0_29.

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Nickisch, Hannes, and Carl Edward Rasmussen. "Gaussian Mixture Modeling with Gaussian Process Latent Variable Models." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15986-2_28.

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Xiao, Zedong, Junli Zhao, Xuejun Qiao, and Fuqing Duan. "Craniofacial Reconstruction Using Gaussian Process Latent Variable Models." In Computer Analysis of Images and Patterns. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23192-1_38.

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Nirwan, Rajbir S., and Nils Bertschinger. "Applications of Gaussian Process Latent Variable Models in Finance." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-29513-4_87.

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Lv, Fengmao, Guowu Yang, Jinzhao Wu, Chuan Liu, and Yuhong Yang. "Anomaly Detection for Categorical Observations Using Latent Gaussian Process." In Neural Information Processing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70139-4_29.

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Bütepage, Judith, Lucas Maystre, and Mounia Lalmas. "Gaussian Process Encoders: VAEs with Reliable Latent-Space Uncertainty." In Machine Learning and Knowledge Discovery in Databases. Research Track. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86520-7_6.

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Li, Jinxing, Bob Zhang, and David Zhang. "Information Fusion Based on Gaussian Process Latent Variable Model." In Information Fusion. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8976-5_3.

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Taubert, Nick, and Martin A. Giese. "Hierarchical Deep Gaussian Processes Latent Variable Model via Expectation Propagation." In Lecture Notes in Computer Science. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86365-4_26.

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Souriau, Rémi, Vincent Vigneron, Jean Lerbet, and Hsin Chen. "Probit Latent Variables Estimation for a Gaussian Process Classifier: Application to the Detection of High-Voltage Spindles." In Latent Variable Analysis and Signal Separation. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93764-9_47.

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Eleftheriadis, Stefanos, Ognjen Rudovic, and Maja Pantic. "Shared Gaussian Process Latent Variable Model for Multi-view Facial Expression Recognition." In Advances in Visual Computing. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41914-0_52.

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Тези доповідей конференцій з теми "Processus gaussiens latents"

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Yang, Liu, Cassandra Heiselman, J. Gerald Quirk, and Petar M. Djuric. "Class-Imbalanced Classifiers Using Ensembles of Gaussian Processes And Gaussian Process Latent Variable Models." In ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2021. http://dx.doi.org/10.1109/icassp39728.2021.9414754.

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Lawrence, Neil D., and Andrew J. Moore. "Hierarchical Gaussian process latent variable models." In the 24th international conference. ACM Press, 2007. http://dx.doi.org/10.1145/1273496.1273557.

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Chen, Kai, Twan van Laarhoven, Elena Marchiori, Feng Yin, and Shuguang Cui. "Multitask Gaussian Process With Hierarchical Latent Interactions." In ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2022. http://dx.doi.org/10.1109/icassp43922.2022.9746570.

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PFINGSTL, SIMON, CHRISTIAN BRAUN, and MARKUS ZIMMERMANN. "WARPED GAUSSIAN PROCESSES FOR PROGNOSTIC HEALTH MONITORING." In Structural Health Monitoring 2021. Destech Publications, Inc., 2022. http://dx.doi.org/10.12783/shm2021/36358.

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Анотація:
Gaussian process regression is a powerful method for predicting states associated with uncertainty. A common application field is to predict damage states of structural systems. Recently, Gaussian processes became very popular as they deliver credible intervals for the predicted states. However, one major disadvantage of Gaussian processes is that they assume a normal distribution. This is not justified when the relevant variables can only assume positive values, such as crack lengths or damage states. This paper presents a way to bypass this problem by using warped Gaussian processes: We (1)
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Su, Chang, and Sargur Srihari. "Latent Fingerprint Core Point Prediction Based on Gaussian Processes." In 2010 20th International Conference on Pattern Recognition (ICPR). IEEE, 2010. http://dx.doi.org/10.1109/icpr.2010.404.

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Li, Shibo, Wei Xing, Robert M. Kirby, and Shandian Zhe. "Scalable Gaussian Process Regression Networks." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/340.

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Анотація:
Gaussian process regression networks (GPRN) are powerful Bayesian models for multi-output regression, but their inference is intractable. To address this issue, existing methods use a fully factorized structure (or a mixture of such structures) over all the outputs and latent functions for posterior approximation, which, however, can miss the strong posterior dependencies among the latent variables and hurt the inference quality. In addition, the updates of the variational parameters are inefficient and can be prohibitively expensive for a large number of outputs. To overcome these limitations
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Zhang, Jiayuan, Ziqi Zhu, and Jixin Zou. "Supervised Gaussian process latent variable model based on Gaussian mixture model." In 2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC). IEEE, 2017. http://dx.doi.org/10.1109/spac.2017.8304262.

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Liu, Yuhao, and Petar M. Djuric. "Tracking the Dimensions of Latent Spaces of Gaussian Process Latent Variable Models." In ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2022. http://dx.doi.org/10.1109/icassp43922.2022.9746538.

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Song, Guoli, Shuhui Wang, Qingming Huang, and Qi Tian. "Multimodal Gaussian Process Latent Variable Models with Harmonization." In 2017 IEEE International Conference on Computer Vision (ICCV). IEEE, 2017. http://dx.doi.org/10.1109/iccv.2017.538.

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Eciolaza, Luka, M. Alkarouri, N. D. Lawrence, V. Kadirkamanathan, and P. J. Fleming. "Gaussian Process Latent Variable Models for Fault Detection." In 2007 IEEE Symposium on Computational Intelligence and Data Mining. IEEE, 2007. http://dx.doi.org/10.1109/cidm.2007.368886.

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