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Artykuły w czasopismach na temat "Estimation scalable de l'incertitude"
Perret, Christian, P. Marchand, Arnaud Belleville, Rémy Garcon, Damien Sevrez, Stéphanie Poligot-Pitsch, Rachel Puechberty i Gwen Glaziou. "La variabilité en fonction du temps des relations hauteur débit. Sa prise en compte dans l'estimation des incertitudes des données hydrométriques par une méthode tabulée". La Houille Blanche, nr 4 (sierpień 2018): 65–72. http://dx.doi.org/10.1051/lhb/2018043.
Pełny tekst źródłaJiang, Zhuqing, Likuo Wei, Ganmin Zeng, Shuwen Qi, Haiying Wang, Aidong Men i Yun Zhou. "Bitrate Estimation for Spatial Scalable Videos". IEEE Transactions on Broadcasting 67, nr 2 (czerwiec 2021): 549–55. http://dx.doi.org/10.1109/tbc.2021.3064278.
Pełny tekst źródłaWang, Xianglu. "Gaussian graphical model estimation with measurement error". JUSTC 53, nr 11 (2023): 1105. http://dx.doi.org/10.52396/justc-2022-0108.
Pełny tekst źródłaCicala, Marco, Egidio D’Amato, Immacolata Notaro i Massimiliano Mattei. "Scalable Distributed State Estimation in UTM Context". Sensors 20, nr 9 (8.05.2020): 2682. http://dx.doi.org/10.3390/s20092682.
Pełny tekst źródłaLi, Cheng, Sanvesh Srivastava i David B. Dunson. "Simple, scalable and accurate posterior interval estimation". Biometrika 104, nr 3 (25.06.2017): 665–80. http://dx.doi.org/10.1093/biomet/asx033.
Pełny tekst źródłaEmerson, Joseph, Robert Alicki i Karol Życzkowski. "Scalable noise estimation with random unitary operators". Journal of Optics B: Quantum and Semiclassical Optics 7, nr 10 (21.09.2005): S347—S352. http://dx.doi.org/10.1088/1464-4266/7/10/021.
Pełny tekst źródłaChen, Dong, Hua You Su, Wen Mei, Li Xuan Wang i Chun Yuan Zhang. "Scalable Parallel Motion Estimation on Muti-GPU System". Applied Mechanics and Materials 347-350 (sierpień 2013): 3708–14. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.3708.
Pełny tekst źródłaHassan, Beenish, Sobia Baig i Saad Aslam. "On Scalability of FDD-Based Cell-Free Massive MIMO Framework". Sensors 23, nr 15 (7.08.2023): 6991. http://dx.doi.org/10.3390/s23156991.
Pełny tekst źródłaJu, Cheng, Susan Gruber, Samuel D. Lendle, Antoine Chambaz, Jessica M. Franklin, Richard Wyss, Sebastian Schneeweiss i Mark J. van der Laan. "Scalable collaborative targeted learning for high-dimensional data". Statistical Methods in Medical Research 28, nr 2 (22.09.2017): 532–54. http://dx.doi.org/10.1177/0962280217729845.
Pełny tekst źródłaADACHI, Ryosuke, Yuh YAMASHITA i Koichi KOBAYASHI. "Distributed Estimation over Delayed Sensor Network with Scalable Communication". IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E102.A, nr 5 (1.05.2019): 712–20. http://dx.doi.org/10.1587/transfun.e102.a.712.
Pełny tekst źródłaRozprawy doktorskie na temat "Estimation scalable de l'incertitude"
Candela, Rosa. "Robust and scalable probabilistic machine learning methods with applications to the airline industry". Electronic Thesis or Diss., Sorbonne université, 2021. http://www.theses.fr/2021SORUS078.
Pełny tekst źródłaIn the airline industry, price prediction plays a significant role both for customers and travel companies. The former are interested in knowing the price evolution to get the cheapest ticket, the latter want to offer attractive tour packages and maximize their revenue margin. In this work we introduce some practical approaches to help travelers in dealing with uncertainty in ticket price evolution and we propose a data-driven framework to monitor time-series forecasting models' performance. Stochastic Gradient Descent (SGD) represents the workhorse optimization method in the field of machine learning and this is true also for distributed systems, which in last years are increasingly used for complex models trained on massive datasets. In asynchronous systems workers can use stale versions of the parameters, which slows SGD convergence. In this thesis we fill the gap in the literature and study sparsification methods in asynchronous settings. We provide a concise convergence rate analysis when the joint effects of sparsification and asynchrony are taken into account, and show that sparsified SGD converges at the same rate of standard SGD. Recently, SGD has played an important role also as a way to perform approximate Bayesian Inference. Stochastic gradient MCMC algorithms use indeed SGD with constant learning rate to obtain samples from the posterior distribution. Despite some promising results restricted to simple models, most of the existing works fall short in easily dealing with the complexity of the loss landscape of deep models. In this thesis we introduce a practical approach to posterior sampling, which requires weaker assumptions than existing algorithms
Rossi, Simone. "Improving Scalability and Inference in Probabilistic Deep Models". Electronic Thesis or Diss., Sorbonne université, 2022. http://www.theses.fr/2022SORUS042.
Pełny tekst źródłaThroughout the last decade, deep learning has reached a sufficient level of maturity to become the preferred choice to solve machine learning-related problems or to aid decision making processes.At the same time, deep learning is generally not equipped with the ability to accurately quantify the uncertainty of its predictions, thus making these models less suitable for risk-critical applications.A possible solution to address this problem is to employ a Bayesian formulation; however, while this offers an elegant treatment, it is analytically intractable and it requires approximations.Despite the huge advancements in the last few years, there is still a long way to make these approaches widely applicable.In this thesis, we address some of the challenges for modern Bayesian deep learning, by proposing and studying solutions to improve scalability and inference of these models.The first part of the thesis is dedicated to deep models where inference is carried out using variational inference (VI).Specifically, we study the role of initialization of the variational parameters and we show how careful initialization strategies can make VI deliver good performance even in large scale models.In this part of the thesis we also study the over-regularization effect of the variational objective on over-parametrized models.To tackle this problem, we propose an novel parameterization based on the Walsh-Hadamard transform; not only this solves the over-regularization effect of VI but it also allows us to model non-factorized posteriors while keeping time and space complexity under control.The second part of the thesis is dedicated to a study on the role of priors.While being an essential building block of Bayes' rule, picking good priors for deep learning models is generally hard.For this reason, we propose two different strategies based (i) on the functional interpretation of neural networks and (ii) on a scalable procedure to perform model selection on the prior hyper-parameters, akin to maximization of the marginal likelihood.To conclude this part, we analyze a different kind of Bayesian model (Gaussian process) and we study the effect of placing a prior on all the hyper-parameters of these models, including the additional variables required by the inducing-point approximations.We also show how it is possible to infer free-form posteriors on these variables, which conventionally would have been otherwise point-estimated
Pinson, Pierre. "Estimation de l'incertitude des prédictions de production éolienne". Phd thesis, École Nationale Supérieure des Mines de Paris, 2006. http://pastel.archives-ouvertes.fr/pastel-00002187.
Pełny tekst źródłaLu, Ruijin. "Scalable Estimation and Testing for Complex, High-Dimensional Data". Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/93223.
Pełny tekst źródłaDoctor of Philosophy
With modern high-throughput technologies, scientists can now collect high-dimensional data of various forms, including brain images, medical spectrum curves, engineering signals, and biological measurements. These data provide a rich source of information on disease development, engineering systems, and many other scientific phenomena. The goal of this dissertation is to develop novel methods that enable scalable estimation, testing, and analysis of complex, high-dimensional data. It contains three parts: parameter estimation based on complex biological and engineering data, powerful testing of high-dimensional functional data, and the analysis of functional data supported on manifolds. The first part focuses on a family of parameter estimation problems in which the relationship between data and the underlying parameters cannot be explicitly specified using a likelihood function. We introduce a computation-based statistical approach that achieves efficient parameter estimation scalable to high-dimensional functional data. The second part focuses on developing a powerful testing method for functional data that can be used to detect important regions. We will show nice properties of our approach. The effectiveness of this testing approach will be demonstrated using two applications: the detection of regions of the spectrum that are related to pre-cancer using fluorescence spectroscopy data and the detection of disease-related regions using brain image data. The third part focuses on analyzing brain cortical thickness data, measured on the cortical surfaces of monkeys’ brains during early development. Subjects are measured on misaligned time-markers. By using functional data estimation and testing approach, we are able to: (1) identify asymmetric regions between their right and left brains across time, and (2) identify spatial regions on the cortical surface that reflect increase or decrease in cortical measurements over time.
Blier, Mylène. "Estimation temporelle avec interruption: les effets de localisation et de durée d'interruptions sont-ils sensibles à l'incertitude ?" Thesis, Université Laval, 2009. http://www.theses.ulaval.ca/2009/26367/26367.pdf.
Pełny tekst źródłaBlier, Mylène. "Estimation temporelle avec interruption : les effets de localisation et de durée d'interruption sont-ils sensibles à l'incertitude ?" Doctoral thesis, Université Laval, 2009. http://hdl.handle.net/20.500.11794/21201.
Pełny tekst źródłaKang, Seong-Ryong. "Performance analysis and network path characterization for scalable internet streaming". Texas A&M University, 2008. http://hdl.handle.net/1969.1/85912.
Pełny tekst źródłaRahmani, Mahmood. "Urban Travel Time Estimation from Sparse GPS Data : An Efficient and Scalable Approach". Doctoral thesis, KTH, Transportplanering, ekonomi och teknik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-167798.
Pełny tekst źródłaQC 20150525
Shriram, Alok Kaur Jasleen. "Efficient techniques for end-to-end bandwidth estimation performance evaluations and scalable deployment /". Chapel Hill, N.C. : University of North Carolina at Chapel Hill, 2009. http://dc.lib.unc.edu/u?/etd,2248.
Pełny tekst źródłaTitle from electronic title page (viewed Jun. 26, 2009). "... in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Computer Science." Discipline: Computer Science; Department/School: Computer Science.
Simsa, Jiri. "Systematic and Scalable Testing of Concurrent Programs". Research Showcase @ CMU, 2013. http://repository.cmu.edu/dissertations/285.
Pełny tekst źródłaCzęści książek na temat "Estimation scalable de l'incertitude"
Zhang, Ying-Jun Angela, Congmin Fan i Xiaojun Yuan. "Scalable Channel Estimation". W SpringerBriefs in Electrical and Computer Engineering, 23–47. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-15884-2_3.
Pełny tekst źródłaPelikan, Martin, Kumara Sastry i David E. Goldberg. "Multiobjective Estimation of Distribution Algorithms". W Scalable Optimization via Probabilistic Modeling, 223–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/978-3-540-34954-9_10.
Pełny tekst źródłaIzumi, Taisuke, i Hironobu Kanzaki. "Scalable Estimation of Network Average Degree". W Lecture Notes in Computer Science, 367–69. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-03089-0_32.
Pełny tekst źródłaSastry, Kumara, Martin Pelikan i David E. Goldberg. "Efficiency Enhancement of Estimation of Distribution Algorithms". W Scalable Optimization via Probabilistic Modeling, 161–85. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/978-3-540-34954-9_7.
Pełny tekst źródłaOcenasek, Jiri, Erick Cantú-Paz, Martin Pelikan i Josef Schwarz. "Design of Parallel Estimation of Distribution Algorithms". W Scalable Optimization via Probabilistic Modeling, 187–203. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/978-3-540-34954-9_8.
Pełny tekst źródłaChakrabarti, Indrajit, Kota Naga Srinivasarao Batta i Sumit Kumar Chatterjee. "Introduction to Scalable Image and Video Coding". W Motion Estimation for Video Coding, 85–108. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14376-7_7.
Pełny tekst źródłaBosman, Peter A. N., i Dirk Thierens. "Numerical Optimization with Real-Valued Estimation-of-Distribution Algorithms". W Scalable Optimization via Probabilistic Modeling, 91–120. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/978-3-540-34954-9_5.
Pełny tekst źródłaBaglietto, P., M. Maresca, A. Migliaro i M. Migliardi. "A VLSI scalable processor array for motion estimation". W Image Analysis and Processing, 127–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/3-540-60298-4_247.
Pełny tekst źródłaLee, Seongsoo. "Energy-Scalable Motion Estimation for Low-Power Multimedia Applications". W Interactive Multimedia on Next Generation Networks, 400–409. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-40012-7_33.
Pełny tekst źródłaCorona, Julio Camejo, Hector Gonzalez i Carlos Morell. "Scalable Generalized Multitarget Linear Regression With Output Dependence Estimation". W Progress in Artificial Intelligence and Pattern Recognition, 60–68. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-89691-1_7.
Pełny tekst źródłaStreszczenia konferencji na temat "Estimation scalable de l'incertitude"
Braspenning, Ralph A. C., Gerard de Haan i Christian Hentschel. "Complexity scalable motion estimation". W Electronic Imaging 2002, redaktor C. C. Jay Kuo. SPIE, 2002. http://dx.doi.org/10.1117/12.453085.
Pełny tekst źródłaZhai, Guangtao, Qian Chen, Xiaokang Yang i Wenjun Zhang. "Scalable visual sensitivity profile estimation". W ICASSP 2008 - 2008 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2008. http://dx.doi.org/10.1109/icassp.2008.4517749.
Pełny tekst źródłaCohen, Edith, Daniel Delling, Fabian Fuchs, Andrew V. Goldberg, Moises Goldszmidt i Renato F. Werneck. "Scalable similarity estimation in social networks". W the first ACM conference. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2512938.2512944.
Pełny tekst źródłaLooks, Moshe. "Scalable estimation-of-distribution program evolution". W the 9th annual conference. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1276958.1277072.
Pełny tekst źródłaKonieczny, Jacek, i Adam Luczak. "Motion estimation algorithm for scalable hardware implementation". W 2009 Picture Coding Symposium (PCS). IEEE, 2009. http://dx.doi.org/10.1109/pcs.2009.5167455.
Pełny tekst źródłaSun, Chuxiong, Rui Wang, Ruiying Li, Jiao Wu i Xiaohui Hu. "Efficient and Scalable Exploration via Estimation-Error". W 2019 International Joint Conference on Neural Networks (IJCNN). IEEE, 2019. http://dx.doi.org/10.1109/ijcnn.2019.8852234.
Pełny tekst źródłaSimko, Michal, Christian Mehlfuhrer, Martin Wrulich i Markus Rupp. "Doubly dispersive channel estimation with scalable complexity". W 2010 International ITG Workshop on Smart Antennas (WSA 2010). IEEE, 2010. http://dx.doi.org/10.1109/wsa.2010.5456443.
Pełny tekst źródłaLengwehasatit, Krisda, Antonio Ortega, Andrea Basso i Amy R. Reibman. "Novel computationally scalable algorithm for motion estimation". W Photonics West '98 Electronic Imaging, redaktorzy Sarah A. Rajala i Majid Rabbani. SPIE, 1998. http://dx.doi.org/10.1117/12.298382.
Pełny tekst źródłaNoshad, Morteza, i Alfred O. Hero. "Scalable Hash-Based Estimation of Divergence Measures". W 2018 Information Theory and Applications Workshop (ITA). IEEE, 2018. http://dx.doi.org/10.1109/ita.2018.8503092.
Pełny tekst źródłaNoshad, Morteza, Yu Zeng i Alfred O. Hero. "Scalable Mutual Information Estimation Using Dependence Graphs". W ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019. http://dx.doi.org/10.1109/icassp.2019.8683351.
Pełny tekst źródłaRaporty organizacyjne na temat "Estimation scalable de l'incertitude"
Hunter, Margaret, Jijo K. Mathew, Ed Cox, Matthew Blackwell i Darcy M. Bullock. Estimation of Connected Vehicle Penetration Rate on Indiana Roadways. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317343.
Pełny tekst źródłaMcMartin, I., M. S. Gauthier i A. V. Page. Updated post-glacial marine limits along western Hudson Bay, central mainland Nunavut and northern Manitoba. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/330940.
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