Literatura científica selecionada sobre o tema "Variational Infernce"
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Artigos de revistas sobre o assunto "Variational Infernce"
Yun-Shan Sun, Yun-Shan Sun, Hong-Yan Xu Yun-Shan Sun e Yan-Qin Li Hong-Yan Xu. "Missing Data Interpolation with Variational Bayesian Inference for Socio-economic Statistics Applications". 電腦學刊 33, n.º 2 (abril de 2022): 169–76. http://dx.doi.org/10.53106/199115992022043302015.
Texto completo da fonteYun-Shan Sun, Yun-Shan Sun, Hong-Yan Xu Yun-Shan Sun e Yan-Qin Li Hong-Yan Xu. "Missing Data Interpolation with Variational Bayesian Inference for Socio-economic Statistics Applications". 電腦學刊 33, n.º 2 (abril de 2022): 169–76. http://dx.doi.org/10.53106/199115992022043302015.
Texto completo da fonteJaakkola, T. S., e M. I. Jordan. "Variational Probabilistic Inference and the QMR-DT Network". Journal of Artificial Intelligence Research 10 (1 de maio de 1999): 291–322. http://dx.doi.org/10.1613/jair.583.
Texto completo da fonteUnlu, Ali, e Laurence Aitchison. "Gradient Regularization as Approximate Variational Inference". Entropy 23, n.º 12 (3 de dezembro de 2021): 1629. http://dx.doi.org/10.3390/e23121629.
Texto completo da fonteMerlo, 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, n.º 11 (1 de novembro de 2023): P11012. http://dx.doi.org/10.1088/1748-0221/18/11/p11012.
Texto completo da fonteBecker, McCoy R., Alexander K. Lew, Xiaoyan Wang, Matin Ghavami, Mathieu Huot, Martin C. Rinard e Vikash K. Mansinghka. "Probabilistic Programming with Programmable Variational Inference". Proceedings of the ACM on Programming Languages 8, PLDI (20 de junho de 2024): 2123–47. http://dx.doi.org/10.1145/3656463.
Texto completo da fonteFourment, Mathieu, e Aaron E. Darling. "Evaluating probabilistic programming and fast variational Bayesian inference in phylogenetics". PeerJ 7 (18 de dezembro de 2019): e8272. http://dx.doi.org/10.7717/peerj.8272.
Texto completo da fonteFrank, Philipp, Reimar Leike e Torsten A. Enßlin. "Geometric Variational Inference". Entropy 23, n.º 7 (2 de julho de 2021): 853. http://dx.doi.org/10.3390/e23070853.
Texto completo da fonteKiselev, 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 de julho de 2019): 9957–58. http://dx.doi.org/10.1609/aaai.v33i01.33019957.
Texto completo da fonteChi, Jinjin, Zhichao Zhang, Zhiyao Yang, Jihong Ouyang e Hongbin Pei. "Generalized Variational Inference via Optimal Transport". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 10 (24 de março de 2024): 11534–42. http://dx.doi.org/10.1609/aaai.v38i10.29035.
Texto completo da fonteTeses / dissertações sobre o assunto "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.
Texto completo da fonteNeuroimaging 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.
Texto completo da fonteCataloged 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.
Texto completo da fonteBeal, 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.
Texto completo da fonteWang, Pengyu. "Collapsed variational inference for computational linguistics". Thesis, University of Oxford, 2016. https://ora.ox.ac.uk/objects/uuid:13c08f60-1441-4ea5-b52f-7ffd0d7a744f.
Texto completo da fonteMamikonyan, Arsen. "Variational inference for non-stationary distributions". Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/113125.
Texto completo da fonteThis 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/.
Texto completo da fonteChallis, E. A. L. "Variational approximate inference in latent linear models". Thesis, University College London (University of London), 2013. http://discovery.ucl.ac.uk/1414228/.
Texto completo da fonteMatthews, 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.
Texto completo da fonteMaestrini, Luca. "On variational approximations for frequentist and bayesian inference". Doctoral thesis, Università degli studi di Padova, 2018. http://hdl.handle.net/11577/3424936.
Texto completo da fonteLe 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.
Livros sobre o assunto "Variational Infernce"
Quah, Danny. Exploiting cross section variation for unit root inference in dynamic data. London: London School of Economics, Financial Markets Group, 1994.
Encontre o texto completo da fonteQuah, Danny. Exploiting cross section variation for unit root inference in dynamic data. Stockholm: Stockholm University, Institute for International Economic Studies, 1993.
Encontre o texto completo da fonteBartholomew, David J. Statistics without Mathematics. London, UK: SAGE Publications Ltd, 2015.
Encontre o texto completo da fonteUnited States. National Aeronautics and Space Administration., ed. 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.
Encontre o texto completo da fonteUnited States. National Aeronautics and Space Administration., ed. 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.
Encontre o texto completo da fonteGraphical Models, Exponential Families, and Variational Inference. Now Publishers, 2008.
Encontre o texto completo da fonteSekhon, Jasjeet. The Neyman— Rubin Model of Causal Inference and Estimation Via Matching Methods. Editado por Janet M. Box-Steffensmeier, Henry E. Brady e David Collier. Oxford University Press, 2009. http://dx.doi.org/10.1093/oxfordhb/9780199286546.003.0011.
Texto completo da fonteBortone, Pietro. Language and Nationality: Social Inferences, Cultural Differences, and Linguistic Misconceptions. Bloomsbury Academic & Professional, 2023.
Encontre o texto completo da fonteBortone, Pietro. Language and Nationality: Social Inferences, Cultural Differences, and Linguistic Misconceptions. Bloomsbury Publishing Plc, 2021.
Encontre o texto completo da fonteSchadt, Eric E. Network Methods for Elucidating the Complexity of Common Human Diseases. Editado por Dennis S. Charney, Eric J. Nestler, Pamela Sklar e Joseph D. Buxbaum. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190681425.003.0002.
Texto completo da fonteCapítulos de livros sobre o assunto "Variational Infernce"
Cohen, Shay. "Variational Inference". In 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.
Texto completo da fonteCohen, Shay. "Variational Inference". In 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.
Texto completo da fonteJiang, Di, Chen Zhang e Yuanfeng Song. "Variational Inference". In Probabilistic Topic Models, 79–93. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-2431-8_6.
Texto completo da fonteErwig, Martin, e Karl Smeltzer. "Variational Pictures". In Diagrammatic Representation and Inference, 55–70. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91376-6_9.
Texto completo da fonteDrori, Iddo. "Deep Variational Inference". In 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.
Texto completo da fonteEgorov, Evgenii, Kirill Neklydov, Ruslan Kostoev e Evgeny Burnaev. "MaxEntropy Pursuit Variational Inference". In 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.
Texto completo da fonteMohamad, Saad, Abdelhamid Bouchachia e Moamar Sayed-Mouchaweh. "Asynchronous Stochastic Variational Inference". In 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.
Texto completo da fonteLongford, Nicholas T. "Inference about variation". In 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.
Texto completo da fonteAyabe, Hiroaki, Emmanuel Manalo, Mari Fukuda e Norihiro Sadato. "What Diagrams Are Considered Useful for Solving Mathematical Word Problems in Japan?" In Diagrammatic Representation and Inference, 79–83. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86062-2_8.
Texto completo da fonteMcGrory, Clare A. "Variational Bayesian Inference for Mixture Models". In 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.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Variational Infernce"
Gianniotis, Nikolaos. "Mixed Variational Inference". In 2019 International Joint Conference on Neural Networks (IJCNN). IEEE, 2019. http://dx.doi.org/10.1109/ijcnn.2019.8852348.
Texto completo da fonteBouchard, Guillaume, e Onno Zoeter. "Split variational inference". In the 26th Annual International Conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1553374.1553382.
Texto completo da fonteChen, Yuqiao, Yibo Yang, Sriraam Natarajan e Nicholas Ruozzi. "Lifted Hybrid Variational Inference". 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.2020/585.
Texto completo da fonteHu, Pingbo, e Yang Weng. "Accelerated Stochastic Variational Inference". In 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.
Texto completo da fonteXu, Xiaopeng, Chuancai Liu e Xiaochun Zhang. "Laplacian Black Box Variational Inference". In the International Conference. New York, New York, USA: ACM Press, 2017. http://dx.doi.org/10.1145/3175684.3175700.
Texto completo da fonteChantas, Giannis, Nikolaos Galatsanos, Rafael Molina e Aggelos Katsaggelos. "Variational Bayesian inference image restoration using a product of total variation-like image priors". In 2010 2nd International Workshop on Cognitive Information Processing (CIP). IEEE, 2010. http://dx.doi.org/10.1109/cip.2010.5604259.
Texto completo da fonteDresdner, Gideon, Saurav Shekhar, Fabian Pedregosa, Francesco Locatello e Gunnar Rätsch. "Boosting Variational Inference With Locally Adaptive Step-Sizes". In 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.
Texto completo da fonteXiu, Zidi, Chenyang Tao e Ricardo Henao. "Variational learning of individual survival distributions". In ACM CHIL '20: ACM Conference on Health, Inference, and Learning. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3368555.3384454.
Texto completo da fonteAziz, Wilker, e Philip Schulz. "Variational Inference and Deep Generative Models". In Proceedings of ACL 2018, Tutorial Abstracts. Stroudsburg, PA, USA: Association for Computational Linguistics, 2018. http://dx.doi.org/10.18653/v1/p18-5003.
Texto completo da fontePlotz, Tobias, Anne S. Wannenwetsch e Stefan Roth. "Stochastic Variational Inference with Gradient Linearization". In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2018. http://dx.doi.org/10.1109/cvpr.2018.00169.
Texto completo da fonteRelatórios de organizações sobre o assunto "Variational Infernce"
Chertkov, Michael, Sungsoo Ahn e Jinwoo Shin. Gauging Variational Inference. Office of Scientific and Technical Information (OSTI), maio de 2017. http://dx.doi.org/10.2172/1360686.
Texto completo da fonteTeh, Yee W., David Newman e Max Welling. A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation. Fort Belvoir, VA: Defense Technical Information Center, setembro de 2007. http://dx.doi.org/10.21236/ada629956.
Texto completo da fonteWalker, David. Developing variational Bayesian inference for applications to gene expression data. Ames (Iowa): Iowa State University, janeiro de 2021. http://dx.doi.org/10.31274/cc-20240624-535.
Texto completo da fonteRoberson, Madeleine, Kathleen Inman, Ashley Carey, Isaac Howard e 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.), junho de 2022. http://dx.doi.org/10.21079/11681/44483.
Texto completo da fonteLewin, Alex, Karla Diaz-Ordaz, Chris Bonell, James Hargreaves e 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), abril de 2023. http://dx.doi.org/10.51744/llp3.
Texto completo da fonteSadowski, Dieter. Board-Level Codetermination in Germany - The Importance and Economic Impact of Fiduciary Duties. Association Inter-University Centre Dubrovnik, maio de 2021. http://dx.doi.org/10.53099/ntkd4304.
Texto completo da fonteChen, Z., S. E. Grasby, C. Deblonde e 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.
Texto completo da fonteDeJaeghere, Joan, Bich-Hang Duong e 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), janeiro de 2021. http://dx.doi.org/10.35489/bsg-rise-ri_2021/024.
Texto completo da fonte