Littérature scientifique sur le sujet « Variational Infernce »
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Articles de revues sur le sujet "Variational Infernce"
Yun-Shan Sun, Yun-Shan Sun, Hong-Yan Xu Yun-Shan Sun et Yan-Qin Li Hong-Yan Xu. « Missing Data Interpolation with Variational Bayesian Inference for Socio-economic Statistics Applications ». 電腦學刊 33, no 2 (avril 2022) : 169–76. http://dx.doi.org/10.53106/199115992022043302015.
Texte intégralYun-Shan Sun, Yun-Shan Sun, Hong-Yan Xu Yun-Shan Sun et Yan-Qin Li Hong-Yan Xu. « Missing Data Interpolation with Variational Bayesian Inference for Socio-economic Statistics Applications ». 電腦學刊 33, no 2 (avril 2022) : 169–76. http://dx.doi.org/10.53106/199115992022043302015.
Texte intégralJaakkola, T. S., et M. I. Jordan. « Variational Probabilistic Inference and the QMR-DT Network ». Journal of Artificial Intelligence Research 10 (1 mai 1999) : 291–322. http://dx.doi.org/10.1613/jair.583.
Texte intégralUnlu, Ali, et Laurence Aitchison. « Gradient Regularization as Approximate Variational Inference ». Entropy 23, no 12 (3 décembre 2021) : 1629. http://dx.doi.org/10.3390/e23121629.
Texte intégralMerlo, A., A. Pavone, D. Böckenhoff, E. Pasch, G. Fuchert, K. J. Brunner, K. Rahbarnia et al. « Accelerated Bayesian inference of plasma profiles with self-consistent MHD equilibria at W7-X via neural networks ». Journal of Instrumentation 18, no 11 (1 novembre 2023) : P11012. http://dx.doi.org/10.1088/1748-0221/18/11/p11012.
Texte intégralBecker, McCoy R., Alexander K. Lew, Xiaoyan Wang, Matin Ghavami, Mathieu Huot, Martin C. Rinard et Vikash K. Mansinghka. « Probabilistic Programming with Programmable Variational Inference ». Proceedings of the ACM on Programming Languages 8, PLDI (20 juin 2024) : 2123–47. http://dx.doi.org/10.1145/3656463.
Texte intégralFourment, Mathieu, et Aaron E. Darling. « Evaluating probabilistic programming and fast variational Bayesian inference in phylogenetics ». PeerJ 7 (18 décembre 2019) : e8272. http://dx.doi.org/10.7717/peerj.8272.
Texte intégralFrank, Philipp, Reimar Leike et Torsten A. Enßlin. « Geometric Variational Inference ». Entropy 23, no 7 (2 juillet 2021) : 853. http://dx.doi.org/10.3390/e23070853.
Texte intégralKiselev, Igor. « Variational BEJG Solvers for Marginal-MAP Inference with Accurate Approximation of B-Conditional Entropy ». Proceedings of the AAAI Conference on Artificial Intelligence 33 (17 juillet 2019) : 9957–58. http://dx.doi.org/10.1609/aaai.v33i01.33019957.
Texte intégralChi, Jinjin, Zhichao Zhang, Zhiyao Yang, Jihong Ouyang et Hongbin Pei. « Generalized Variational Inference via Optimal Transport ». Proceedings of the AAAI Conference on Artificial Intelligence 38, no 10 (24 mars 2024) : 11534–42. http://dx.doi.org/10.1609/aaai.v38i10.29035.
Texte intégralThèses sur le sujet "Variational Infernce"
Rouillard, Louis. « Bridging Simulation-based Inference and Hierarchical Modeling : Applications in Neuroscience ». Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASG024.
Texte intégralNeuroimaging investigates the brain's architecture and function using magnetic resonance (MRI). To make sense of the complex observed signal, Neuroscientists posit explanatory models, governed by interpretable parameters. This thesis tackles statistical inference : guessing which parameters could have yielded the signal through the model.Inference in Neuroimaging is complexified by at least three hurdles : a large dimensionality, a large uncertainty, and the hierarchcial structure of data. We look into variational inference (VI) as an optimization-based method to tackle this regime.Specifically, we conbine structured stochastic VI and normalizing flows (NFs) to design expressive yet scalable variational families. We apply those techniques in diffusion and functional MRI, on tasks including individual parcellation, microstructure inference and directional coupling estimation. Through these applications, we underline the interplay between the forward and reverse Kullback-Leibler (KL) divergences as complemen-tary tools for inference. We also demonstrate the ability of automatic VI (AVI) as a reliable and scalable inference method to tackle the challenges of model-driven Neuroscience
Calabrese, Chris M. Eng Massachusetts Institute of Technology. « Distributed inference : combining variational inference with distributed computing ». Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/85407.
Texte intégralCataloged from PDF version of thesis.
Includes bibliographical references (pages 95-97).
The study of inference techniques and their use for solving complicated models has taken off in recent years, but as the models we attempt to solve become more complex, there is a worry that our inference techniques will be unable to produce results. Many problems are difficult to solve using current approaches because it takes too long for our implementations to converge on useful values. While coming up with more efficient inference algorithms may be the answer, we believe that an alternative approach to solving this complicated problem involves leveraging the computation power of multiple processors or machines with existing inference algorithms. This thesis describes the design and implementation of such a system by combining a variational inference implementation (Variational Message Passing) with a high-level distributed framework (Graphlab) and demonstrates that inference is performed faster on a few large graphical models when using this system.
by Chris Calabrese.
M. Eng.
Lawrence, Neil David. « Variational inference in probabilistic models ». Thesis, University of Cambridge, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.621104.
Texte intégralBeal, Matthew James. « Variational algorithms for approximate Bayesian inference ». Thesis, University College London (University of London), 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.404387.
Texte intégralWang, Pengyu. « Collapsed variational inference for computational linguistics ». Thesis, University of Oxford, 2016. https://ora.ox.ac.uk/objects/uuid:13c08f60-1441-4ea5-b52f-7ffd0d7a744f.
Texte intégralMamikonyan, Arsen. « Variational inference for non-stationary distributions ». Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/113125.
Texte intégralThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (page 49).
In this thesis, I look at multiple Variational Inference algorithm, transform Kalman Variational Bayes and Stochastic Variational Inference into streaming algorithms and try to identify if any of them work with non-stationary distributions. I conclude that Kalman Variational Bayes can do as good as any other algorithm for stationary distributions, and tracks non-stationary distributions better than any other algorithm in question.
by Arsen Mamikonyan.
M. Eng.
Thorpe, Matthew. « Variational methods for geometric statistical inference ». Thesis, University of Warwick, 2015. http://wrap.warwick.ac.uk/74241/.
Texte intégralChallis, E. A. L. « Variational approximate inference in latent linear models ». Thesis, University College London (University of London), 2013. http://discovery.ucl.ac.uk/1414228/.
Texte intégralMatthews, Alexander Graeme de Garis. « Scalable Gaussian process inference using variational methods ». Thesis, University of Cambridge, 2017. https://www.repository.cam.ac.uk/handle/1810/278022.
Texte intégralMaestrini, Luca. « On variational approximations for frequentist and bayesian inference ». Doctoral thesis, Università degli studi di Padova, 2018. http://hdl.handle.net/11577/3424936.
Texte intégralLe approssimazioni variazionali sono tecniche di inferenza approssimata per modelli statisticicomplessi che si propongono come alternative, più rapide e di tipo deterministico,a metodi tradizionali che, sebbene accurati, necessitano di maggiori tempi per l'adattamento. Vengono qui sviluppati e valutati alcuni strumenti variazionali per l'inferenzabasata sulla verosimiglianza e per l'inferenza bayesiana, estendendo dei risultati recentiin letteratura sulle approssimazioni variazionali. In particolare, la prima parte dellatesi impiega una strategia basata su un'approssimazione variazionale gaussiana per la funzione di verosimiglianza di modelli lineari generalizzati misti con matrici di disegnodegli effetti casuali generiche, includenti, per esempio, funzioni di basi spline. Questometodo consiste nell'approssimare la distribuzione del vettore degli effetti casuali,condizionatamente alle risposte, con una densità gaussiana. Il secondo filone concerneinvece una particolare classe di approssimazioni variazionali nota come mean field variational Bayes, che impone un prodotto di densità come restrizione non parametrica sulla densità approssimante. Vengono sviluppati algoritmi per l'inferenza e l'adattamento dimodelli con risposte elaborate, adottando la prospettiva del variational message passing. La modularità del variational message passing è tale da consentire estensioni amodelli con strutture di verosimiglianza più complesse e scalabilità a insiemi di dati di grandi dimensioni con relativa semplicità. Vengono inoltre derivati in forma esplicitadegli algoritmi per modelli contenenti effetti casuali su più livelli e risposte non normali,introducendo semplicazioni atte a incrementare l'efficienza computazionale. Sonoinclusi studi numerici e illustrazioni, considerando come riferimento per un confronto il metodo Markov chain Monte Carlo.
Livres sur le sujet "Variational Infernce"
Quah, Danny. Exploiting cross section variation for unit root inference in dynamic data. London : London School of Economics, Financial Markets Group, 1994.
Trouver le texte intégralQuah, Danny. Exploiting cross section variation for unit root inference in dynamic data. Stockholm : Stockholm University, Institute for International Economic Studies, 1993.
Trouver le texte intégralBartholomew, David J. Statistics without Mathematics. London, UK : SAGE Publications Ltd, 2015.
Trouver le texte intégralUnited States. National Aeronautics and Space Administration., dir. Compositional variation in Apollo 16 impact-melt breccias and inferences for the geology and bombardment history of the central highlands of the moon. [Washington, DC : National Aeronautics and Space Administration, 1994.
Trouver le texte intégralUnited States. National Aeronautics and Space Administration., dir. Compositional variation in Apollo 16 impact-melt breccias and inferences for the geology and bombardment history of the central highlands of the moon. [Washington, DC : National Aeronautics and Space Administration, 1994.
Trouver le texte intégralGraphical Models, Exponential Families, and Variational Inference. Now Publishers, 2008.
Trouver le texte intégralSekhon, Jasjeet. The Neyman— Rubin Model of Causal Inference and Estimation Via Matching Methods. Sous la direction de Janet M. Box-Steffensmeier, Henry E. Brady et David Collier. Oxford University Press, 2009. http://dx.doi.org/10.1093/oxfordhb/9780199286546.003.0011.
Texte intégralBortone, Pietro. Language and Nationality : Social Inferences, Cultural Differences, and Linguistic Misconceptions. Bloomsbury Academic & Professional, 2023.
Trouver le texte intégralBortone, Pietro. Language and Nationality : Social Inferences, Cultural Differences, and Linguistic Misconceptions. Bloomsbury Publishing Plc, 2021.
Trouver le texte intégralSchadt, Eric E. Network Methods for Elucidating the Complexity of Common Human Diseases. Sous la direction de Dennis S. Charney, Eric J. Nestler, Pamela Sklar et Joseph D. Buxbaum. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190681425.003.0002.
Texte intégralChapitres de livres sur le sujet "Variational Infernce"
Cohen, Shay. « Variational Inference ». Dans Synthesis Lectures on Human Language Technologies, 131–49. Cham : Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-031-02161-9_6.
Texte intégralCohen, Shay. « Variational Inference ». Dans Bayesian Analysis in Natural Language Processing, 135–53. Cham : Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-031-02170-1_6.
Texte intégralJiang, Di, Chen Zhang et Yuanfeng Song. « Variational Inference ». Dans Probabilistic Topic Models, 79–93. Singapore : Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-2431-8_6.
Texte intégralErwig, Martin, et Karl Smeltzer. « Variational Pictures ». Dans Diagrammatic Representation and Inference, 55–70. Cham : Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91376-6_9.
Texte intégralDrori, Iddo. « Deep Variational Inference ». Dans Handbook of Variational Methods for Nonlinear Geometric Data, 361–76. Cham : Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-31351-7_12.
Texte intégralEgorov, Evgenii, Kirill Neklydov, Ruslan Kostoev et Evgeny Burnaev. « MaxEntropy Pursuit Variational Inference ». Dans Advances in Neural Networks – ISNN 2019, 409–17. Cham : Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22796-8_43.
Texte intégralMohamad, Saad, Abdelhamid Bouchachia et Moamar Sayed-Mouchaweh. « Asynchronous Stochastic Variational Inference ». Dans Proceedings of the International Neural Networks Society, 296–308. Cham : Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-16841-4_31.
Texte intégralLongford, Nicholas T. « Inference about variation ». Dans Models for Uncertainty in Educational Testing, 1–15. New York, NY : Springer New York, 1995. http://dx.doi.org/10.1007/978-1-4613-8463-2_1.
Texte intégralAyabe, Hiroaki, Emmanuel Manalo, Mari Fukuda et Norihiro Sadato. « What Diagrams Are Considered Useful for Solving Mathematical Word Problems in Japan ? » Dans Diagrammatic Representation and Inference, 79–83. Cham : Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86062-2_8.
Texte intégralMcGrory, Clare A. « Variational Bayesian Inference for Mixture Models ». Dans Case Studies in Bayesian Statistical Modelling and Analysis, 388–402. Chichester, UK : John Wiley & Sons, Ltd, 2012. http://dx.doi.org/10.1002/9781118394472.ch23.
Texte intégralActes de conférences sur le sujet "Variational Infernce"
Gianniotis, Nikolaos. « Mixed Variational Inference ». Dans 2019 International Joint Conference on Neural Networks (IJCNN). IEEE, 2019. http://dx.doi.org/10.1109/ijcnn.2019.8852348.
Texte intégralBouchard, Guillaume, et Onno Zoeter. « Split variational inference ». Dans the 26th Annual International Conference. New York, New York, USA : ACM Press, 2009. http://dx.doi.org/10.1145/1553374.1553382.
Texte intégralChen, Yuqiao, Yibo Yang, Sriraam Natarajan et Nicholas Ruozzi. « Lifted Hybrid Variational Inference ». Dans 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.2020/585.
Texte intégralHu, Pingbo, et Yang Weng. « Accelerated Stochastic Variational Inference ». Dans 2019 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom). IEEE, 2019. http://dx.doi.org/10.1109/ispa-bdcloud-sustaincom-socialcom48970.2019.00183.
Texte intégralXu, Xiaopeng, Chuancai Liu et Xiaochun Zhang. « Laplacian Black Box Variational Inference ». Dans the International Conference. New York, New York, USA : ACM Press, 2017. http://dx.doi.org/10.1145/3175684.3175700.
Texte intégralChantas, Giannis, Nikolaos Galatsanos, Rafael Molina et Aggelos Katsaggelos. « Variational Bayesian inference image restoration using a product of total variation-like image priors ». Dans 2010 2nd International Workshop on Cognitive Information Processing (CIP). IEEE, 2010. http://dx.doi.org/10.1109/cip.2010.5604259.
Texte intégralDresdner, Gideon, Saurav Shekhar, Fabian Pedregosa, Francesco Locatello et Gunnar Rätsch. « Boosting Variational Inference With Locally Adaptive Step-Sizes ». Dans Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California : International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/322.
Texte intégralXiu, Zidi, Chenyang Tao et Ricardo Henao. « Variational learning of individual survival distributions ». Dans ACM CHIL '20 : ACM Conference on Health, Inference, and Learning. New York, NY, USA : ACM, 2020. http://dx.doi.org/10.1145/3368555.3384454.
Texte intégralAziz, Wilker, et Philip Schulz. « Variational Inference and Deep Generative Models ». Dans Proceedings of ACL 2018, Tutorial Abstracts. Stroudsburg, PA, USA : Association for Computational Linguistics, 2018. http://dx.doi.org/10.18653/v1/p18-5003.
Texte intégralPlotz, Tobias, Anne S. Wannenwetsch et Stefan Roth. « Stochastic Variational Inference with Gradient Linearization ». Dans 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2018. http://dx.doi.org/10.1109/cvpr.2018.00169.
Texte intégralRapports d'organisations sur le sujet "Variational Infernce"
Chertkov, Michael, Sungsoo Ahn et Jinwoo Shin. Gauging Variational Inference. Office of Scientific and Technical Information (OSTI), mai 2017. http://dx.doi.org/10.2172/1360686.
Texte intégralTeh, Yee W., David Newman et Max Welling. A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation. Fort Belvoir, VA : Defense Technical Information Center, septembre 2007. http://dx.doi.org/10.21236/ada629956.
Texte intégralWalker, David. Developing variational Bayesian inference for applications to gene expression data. Ames (Iowa) : Iowa State University, janvier 2021. http://dx.doi.org/10.31274/cc-20240624-535.
Texte intégralRoberson, Madeleine, Kathleen Inman, Ashley Carey, Isaac Howard et Jameson Shannon. Probabilistic neural networks that predict compressive strength of high strength concrete in mass placements using thermal history. Engineer Research and Development Center (U.S.), juin 2022. http://dx.doi.org/10.21079/11681/44483.
Texte intégralLewin, Alex, Karla Diaz-Ordaz, Chris Bonell, James Hargreaves et Edoardo Masset. Machine learning for impact evaluation in CEDIL-funded studies : an ex ante lesson learning paper. Centre for Excellence and Development Impact and Learning (CEDIL), avril 2023. http://dx.doi.org/10.51744/llp3.
Texte intégralSadowski, Dieter. Board-Level Codetermination in Germany - The Importance and Economic Impact of Fiduciary Duties. Association Inter-University Centre Dubrovnik, mai 2021. http://dx.doi.org/10.53099/ntkd4304.
Texte intégralChen, Z., S. E. Grasby, C. Deblonde et X. Liu. AI-enabled remote sensing data interpretation for geothermal resource evaluation as applied to the Mount Meager geothermal prospective area. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/330008.
Texte intégralDeJaeghere, Joan, Bich-Hang Duong et Vu Dao. Teaching Practices That Support and Promote Learning : Qualitative Evidence from High and Low Performing Classes in Vietnam. Research on Improving Systems of Education (RISE), janvier 2021. http://dx.doi.org/10.35489/bsg-rise-ri_2021/024.
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