Relevant bibliographies by topics / Ensemblier

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Academic literature on the topic 'Ensemblier'

Author: Grafiati
Published: 3 May 2025

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Journal articles on the topic "Ensemblier"

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Niroumand-Jadidi, Milad, Carl J. Legleiter, and Francesca Bovolo. "Neural Network-Based Temporal Ensembling of Water Depth Estimates Derived from SuperDove Images." Remote Sensing 17, no. 7 (April 6, 2025): 1309. https://doi.org/10.3390/rs17071309.

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CubeSats provide a wealth of high-frequency observations at a meter-scale spatial resolution. However, most current methods of inferring water depth from satellite data consider only a single image. This approach is sensitive to the radiometric quality of the data acquired at that particular instant in time, which could be degraded by various confounding factors, such as sun glint or atmospheric effects. Moreover, using single images in isolation fails to exploit recent improvements in the frequency of satellite image acquisition. This study aims to leverage the dense image time series from the SuperDove constellation via an ensembling framework that helps to improve empirical (regression-based) bathymetry retrieval. Unlike previous studies that only ensembled the original spectral data, we introduce a neural network-based method that instead ensembles the water depths derived from multi-temporal imagery, provided the data are acquired under steady flow conditions. We refer to this new approach as NN-depth ensembling. First, every image is treated individually to derive multitemporal depth estimates. Then, we use another NN regressor to ensemble the temporal water depths. This step serves to automatically weight the contribution of the bathymetric estimates from each time instance to the final bathymetry product. Unlike methods that ensemble spectral data, NN-depth ensembling mitigates against propagation of uncertainties in spectral data (e.g., noise due to sun glint) to the final bathymetric product. The proposed NN-depth ensembling is applied to temporal SuperDove imagery of reaches from the American, Potomac, and Colorado rivers with depths of up to 10 m and evaluated against in situ measurements. The proposed method provided more accurate and robust bathymetry retrieval than single-image analyses and other ensembling approaches.
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Saphal, Rohan, Balaraman Ravindran, Dheevatsa Mudigere, Sasikanth Avancha, and Bharat Kaul. "ERLP: Ensembles of Reinforcement Learning Policies (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 10 (April 3, 2020): 13905–6. http://dx.doi.org/10.1609/aaai.v34i10.7225.

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Reinforcement learning algorithms are sensitive to hyper-parameters and require tuning and tweaking for specific environments for improving performance. Ensembles of reinforcement learning models on the other hand are known to be much more robust and stable. However, training multiple models independently on an environment suffers from high sample complexity. We present here a methodology to create multiple models from a single training instance that can be used in an ensemble through directed perturbation of the model parameters at regular intervals. This allows training a single model that converges to several local minima during the optimization process as a result of the perturbation. By saving the model parameters at each such instance, we obtain multiple policies during training that are ensembled during evaluation. We evaluate our approach on challenging discrete and continuous control tasks and also discuss various ensembling strategies. Our framework is substantially sample efficient, computationally inexpensive and is seen to outperform state of the art (SOTA) approaches
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ZHOU, ZHI-HUA, JIAN-XIN WU, WEI TANG, and ZHAO-QIAN CHEN. "COMBINING REGRESSION ESTIMATORS: GA-BASED SELECTIVE NEURAL NETWORK ENSEMBLE." International Journal of Computational Intelligence and Applications 01, no. 04 (December 2001): 341–56. http://dx.doi.org/10.1142/s1469026801000287.

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Neural network ensemble is a learning paradigm where a collection of neural networks is trained for the same task. In this paper, the relationship between the generalization ability of the neural network ensemble and the correlation of the individual neural networks constituting the ensemble is analyzed in the context of combining neural regression estimators, which reveals that ensembling a selective subset of trained networks is superior to ensembling all the trained networks in some cases. Based on such recognition, an approach named GASEN is proposed. GASEN trains a number of individual neural networks at first. Then it assigns random weights to the individual networks and employs a genetic algorithm to evolve those weights so that they can characterize to some extent the importance of the individual networks in constituting an ensemble. Finally it selects an optimum subset of individual networks based on the evolved weights to make up the ensemble. Experimental results show that, comparing with a popular ensemble approach, i.e., averaging all, and a theoretically optimum selective ensemble approach, i.e. enumerating, GASEN has preferable performance in generating ensembles with strong generalization ability in relatively small computational cost. This paper also analyzes the working mechanism of GASEN from the view of error-ambiguity decomposition, which reveals that GASEN improves generalization ability mainly through reducing the average generalization error of the individual neural networks constituting the ensemble.
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Akgun, O. Burak, and Elcin Kentel. "Ensemble Precipitation Estimation Using a Fuzzy Rule-Based Model." Engineering Proceedings 5, no. 1 (July 9, 2021): 48. http://dx.doi.org/10.3390/engproc2021005048.

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In this study, a Takagi-Sugeno (TS) fuzzy rule-based (FRB) model is used for ensembling precipitation time series. The TS FRB model takes precipitation predictions of grid-based regional climate models (RCMs) from the EUR11 domain, available from the CORDEX database, as inputs to generate ensembled precipitation time series for two meteorological stations (MSs) in the Mediterranean region of Turkey. For each MS, RCM data that are available at the closest grid to the corresponding MSs are used. To generate the fuzzy rules of the TS FRB model, the subtractive clustering algorithm (SC) is utilized. Together with the TS FRB, the simple ensemble mean approach is also applied, and the performances of these two model results and individual RCM predictions are compared. The results show that ensembled models outperform individual RCMs, for monthly precipitation, for both MSs. On the other hand, although ensemble models capture the general trend in the observations, they underestimate the peak precipitation events.
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Cawood, Pieter, and Terence Van Zyl. "Evaluating State-of-the-Art, Forecasting Ensembles and Meta-Learning Strategies for Model Fusion." Forecasting 4, no. 3 (August 18, 2022): 732–51. http://dx.doi.org/10.3390/forecast4030040.

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The techniques of hybridisation and ensemble learning are popular model fusion techniques for improving the predictive power of forecasting methods. With limited research that instigates combining these two promising approaches, this paper focuses on the utility of the Exponential Smoothing-Recurrent Neural Network (ES-RNN) in the pool of base learners for different ensembles. We compare against some state-of-the-art ensembling techniques and arithmetic model averaging as a benchmark. We experiment with the M4 forecasting dataset of 100,000 time-series, and the results show that the Feature-Based FORecast Model Averaging (FFORMA), on average, is the best technique for late data fusion with the ES-RNN. However, considering the M4’s Daily subset of data, stacking was the only successful ensemble at dealing with the case where all base learner performances were similar. Our experimental results indicate that we attain state-of-the-art forecasting results compared to Neural Basis Expansion Analysis (N-BEATS) as a benchmark. We conclude that model averaging is a more robust ensembling technique than model selection and stacking strategies. Further, the results show that gradient boosting is superior for implementing ensemble learning strategies.
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Hrúz, Marek, Ivan Gruber, Jakub Kanis, Matyáš Boháček, Miroslav Hlaváč, and Zdeněk Krňoul. "Ensemble Is What We Need: Isolated Sign Recognition Edition." Sensors 22, no. 13 (July 4, 2022): 5043. http://dx.doi.org/10.3390/s22135043.

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In this paper, we dive into sign language recognition, focusing on the recognition of isolated signs. The task is defined as a classification problem, where a sequence of frames (i.e., images) is recognized as one of the given sign language glosses. We analyze two appearance-based approaches, I3D and TimeSformer, and one pose-based approach, SPOTER. The appearance-based approaches are trained on a few different data modalities, whereas the performance of SPOTER is evaluated on different types of preprocessing. All the methods are tested on two publicly available datasets: AUTSL and WLASL300. We experiment with ensemble techniques to achieve new state-of-the-art results of 73.84% accuracy on the WLASL300 dataset by using the CMA-ES optimization method to find the best ensemble weight parameters. Furthermore, we present an ensembling technique based on the Transformer model, which we call Neural Ensembler.
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Alazba, Amal, and Hamoud Aljamaan. "Software Defect Prediction Using Stacking Generalization of Optimized Tree-Based Ensembles." Applied Sciences 12, no. 9 (April 30, 2022): 4577. http://dx.doi.org/10.3390/app12094577.

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Software defect prediction refers to the automatic identification of defective parts of software through machine learning techniques. Ensemble learning has exhibited excellent prediction outcomes in comparison with individual classifiers. However, most of the previous work utilized ensemble models in the context of software defect prediction with the default hyperparameter values, which are considered suboptimal. In this paper, we investigate the applicability of a stacking ensemble built with fine-tuned tree-based ensembles for defect prediction. We used grid search to optimize the hyperparameters of seven tree-based ensembles: random forest, extra trees, AdaBoost, gradient boosting, histogram-based gradient boosting, XGBoost and CatBoost. Then, a stacking ensemble was built utilizing the fine-tuned tree-based ensembles. The ensembles were evaluated using 21 publicly available defect datasets. Empirical results showed large impacts of hyperparameter optimization on extra trees and random forest ensembles. Moreover, our results demonstrated the superiority of the stacking ensemble over all fine-tuned tree-based ensembles.
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Kolczynski, Walter C., David R. Stauffer, Sue Ellen Haupt, Naomi S. Altman, and Aijun Deng. "Investigation of Ensemble Variance as a Measure of True Forecast Variance." Monthly Weather Review 139, no. 12 (December 1, 2011): 3954–63. http://dx.doi.org/10.1175/mwr-d-10-05081.1.

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Abstract The uncertainty in meteorological predictions is of interest for applications ranging from economic to recreational to public safety. One common method to estimate uncertainty is by using meteorological ensembles. These ensembles provide an easily quantifiable measure of the uncertainty in the forecast in the form of the ensemble variance. However, ensemble variance may not accurately reflect the actual uncertainty, so any measure of uncertainty derived from the ensemble should be calibrated to provide a more reliable estimate of the actual uncertainty in the forecast. A previous study introduced the linear variance calibration (LVC) as a simple method to determine the ensemble variance to error variance relationship and demonstrated this technique on real ensemble data. The LVC parameters, the slopes, and y intercepts, however, are generally different from the ideal values. This current study uses a stochastic model to examine the LVC in a controlled setting. The stochastic model is capable of simulating underdispersive and overdispersive ensembles as well as perfectly reliable ensembles. Because the underlying relationship is specified, LVC results can be compared to theoretical values of the slope and y intercept. Results indicate that all types of ensembles produce calibration slopes that are smaller than their theoretical values for ensemble sizes less than several hundred members, with corresponding y intercepts greater than their theoretical values. This indicates that all ensembles, even otherwise perfect ensembles, should be calibrated if the ensemble size is less than several hundred. In addition, it is shown that an adjustment factor can be computed for inadequate ensemble size. This adjustment factor is independent of the stochastic model and is applicable to any linear regression of error variance on ensemble variance. When applied to experiments using the stochastic model, the adjustment produces LVC parameters near their theoretical values for all ensemble sizes. Although the adjustment is unnecessary when applying LVC, it allows for a more accurate assessment of the reliability of ensembles, and a fair comparison of the reliability for differently sized ensembles.
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Du, Juan, Fei Zheng, He Zhang, and Jiang Zhu. "A Multivariate Balanced Initial Ensemble Generation Approach for an Atmospheric General Circulation Model." Water 13, no. 2 (January 7, 2021): 122. http://dx.doi.org/10.3390/w13020122.

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Based on the multivariate empirical orthogonal function (MEOF) method, a multivariate balanced initial ensemble generation method was applied to the ensemble data assimilation scheme. The initial ensembles were generated with a reasonable consideration of the physical relationships between different model variables. The spatial distribution derived from the MEOF analysis is combined with the 3-D random perturbation to generate a balanced initial perturbation field. The Local Ensemble Transform Kalman Filter (LETKF) data assimilation scheme was established for an atmospheric general circulation model. Ensemble data assimilation experiments using different initial ensemble generation methods, spatially random and MEOF-based balanced, are performed using realistic atmospheric observations. It is shown that the ensembles integrated from the balanced initial ensembles maintain a much more reasonable spread and a more reliable horizontal correlation compared with the historical model results than those from the randomly perturbed initial ensembles. The model predictions were also improved by adopting the MEOF-based balanced initial ensembles.
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Du, Juan, Fei Zheng, He Zhang, and Jiang Zhu. "A Multivariate Balanced Initial Ensemble Generation Approach for an Atmospheric General Circulation Model." Water 13, no. 2 (January 7, 2021): 122. http://dx.doi.org/10.3390/w13020122.

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Based on the multivariate empirical orthogonal function (MEOF) method, a multivariate balanced initial ensemble generation method was applied to the ensemble data assimilation scheme. The initial ensembles were generated with a reasonable consideration of the physical relationships between different model variables. The spatial distribution derived from the MEOF analysis is combined with the 3-D random perturbation to generate a balanced initial perturbation field. The Local Ensemble Transform Kalman Filter (LETKF) data assimilation scheme was established for an atmospheric general circulation model. Ensemble data assimilation experiments using different initial ensemble generation methods, spatially random and MEOF-based balanced, are performed using realistic atmospheric observations. It is shown that the ensembles integrated from the balanced initial ensembles maintain a much more reasonable spread and a more reliable horizontal correlation compared with the historical model results than those from the randomly perturbed initial ensembles. The model predictions were also improved by adopting the MEOF-based balanced initial ensembles.
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Dissertations / Theses on the topic "Ensemblier"

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Abbasian, Houman. "Inner Ensembles: Using Ensemble Methods in Learning Step." Thèse, Université d'Ottawa / University of Ottawa, 2014. http://hdl.handle.net/10393/31127.

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A pivotal moment in machine learning research was the creation of an important new research area, known as Ensemble Learning. In this work, we argue that ensembles are a very general concept, and though they have been widely used, they can be applied in more situations than they have been to date. Rather than using them only to combine the output of an algorithm, we can apply them to decisions made inside the algorithm itself, during the learning step. We call this approach Inner Ensembles. The motivation to develop Inner Ensembles was the opportunity to produce models with the similar advantages as regular ensembles, accuracy and stability for example, plus additional advantages such as comprehensibility, simplicity, rapid classification and small memory footprint. The main contribution of this work is to demonstrate how broadly this idea can be applied, and highlight its potential impact on all types of algorithms. To support our claim, we first provide a general guideline for applying Inner Ensembles to different algorithms. Then, using this framework, we apply them to two categories of learning methods: supervised and un-supervised. For the former we chose Bayesian network, and for the latter K-Means clustering. Our results show that 1) the overall performance of Inner Ensembles is significantly better than the original methods, and 2) Inner Ensembles provide similar performance improvements as regular ensembles.
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Bariset, Lucie Virginie. "Paul Iribe, une figure controversée dans l’émergence de l’Art déco." Electronic Thesis or Diss., Paris 1, 2024. http://www.theses.fr/2024PA01H058.

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Créateur insatiable à la personnalité complexe, il n’est pas aisé de fixer la physionomie artistique de Paul Iribe. Tour à tour, caricaturiste, journaliste, décorateur, créateur de bijoux, scénographe, styliste, photographe, éditeur, scénariste, metteur en scène, costumier… Iribe, à l’image de Cocteau, avec lequel il partage les premières années de sa vie de créateur, touche à tout avec une facilité et un talent peu communs, défiant ainsi tous ceux qui seraient tenter de l’enfermer dans un domaine artistique. Un constat s’impose. Si Iribe, en ce début des années 1910, est omniprésent dans tous les champs des arts décoratifs, ses créations mobilières semblent peu susciter l’attention de la presse spécialisée française. Ce manque d’intérêt des périodiques français s’explique, en outre, par le choix du jeune créateur de se tenir en dehors de tous circuits commerciaux. Tout en étant très sollicité par une clientèle cossue et « très imité » par ses confrères, Iribe reste en retrait des manifestations officielles. Snobisme à la des Esseintes ou juge-t-il la démarche peu fructueuse en termes de visibilité et donc de rentabilité ? Il y a un certain paradoxe à considérer qu’Iribe fait « tout » pour sortir de l’anonymat et devenir une figure artistique importante de ces années 1910, mais ne fait rien pour soigner sa popularité. Les quelques articles qui lui rendent hommage témoignent de cette effervescence créatrice qui procède à la fois du prodige et de l’art de la dispersion. Pour certains, Iribe est le précurseur de l’Art décoratif moderne, celui « qui montra la voix », pour d’autres, il est davantage l’homme des révolutions avortées, n’exploitant jamais jusqu’au bout ses intuitions géniales. Suivre l’itinéraire artistique de Paul Iribe, c’est à la fois suivre la voie d’un artiste original, farouchement attaché à sa liberté, mais aussi les problématiques esthétiques auxquelles se confrontent les artistes décorateurs modernes en ces années d’avant-guerre où la « querelle du cosmopolitisme » bat son plein. Suivre le parcours de Paul Iribe, c’est appréhender la complexité de cette période charnière, passage d’un style à un autre, de l’Art nouveau à l’Art déco. Ce « nouveau style », qui s’élabore dans les années 1910 doit, en effet, au moins autant à Paul Iribe qu’à des figures aujourd’hui emblématiques de cette transition, tel André Groult. La rose et le galuchat, si caractéristiques de cette période doivent à Iribe ; pour la première son statut iconique, pour le second, sa remise au goût du jour - jusqu’à devenir le symbole de l’Art déco. Exilé aux États-Unis, où il met son talent au service du producteur metteur en scène Cecil DeMille, il passe à côté de l’effervescence des années 1920 et disparait totalement de l’actualité parisienne. Mais, plus pénible encore est l’indifférence dans laquelle les organisateurs de l’Exposition internationale des Arts décoratifs de 1925, le tiennent. Aucun rappel n’est fait de ce style « à la rose ». C’est un oublié. En s’exilant Iribe s’est mis à l’index de toutes les tendances nouvelles - dont il fut l’un des précurseurs - et dont la guerre a accouché. L’aigreur pointe. Le décorateur audacieux des années d’avant-guerre laisse place à un virulent pamphlétaire. Il se lance dans une cabale contre l’art de son temps, et met toute sa fougue au service de la défense des industries du luxe et de l’artisanat. Mais, sont-ce là vraiment ses convictions ? Sa conquête de Gabrielle Chanel et ses talents de publiciste laissent toutefois présager de nouvelles perspectives pour son œuvre. Leur complicité affective, mais aussi artistique, lui est profitable. Auprès de cette dernière compagne, son talent, intact, s’exprime à nouveau sans retenue
An insatiable creator with a complex personality, it is not easy to define the artistic physiognomy of Paul Iribe. In turn, caricaturist, journalist, decorator, jewellery designer, scenographer, stylist, photographer, publisher, screenwriter, director, costume designer, ... Iribe, like Cocteau, with whom he shares the first years of his creative life, touches everything with an uncommon ease and talent, thus defying all those who would be tempted to confine him to an artistic domain. One observation is clear. If Iribe, in the early 1910s, is omnipresent in all fields of the decorative arts, his furniture creations seem to attract little attention from the specialized French press. This lack of interest from French periodicals is explained, among other things, by the young creator's choice to stay outside of all commercial circuits. While being highly sought after by a wealthy clientele and "very imitated" by his colleagues, Iribe remains aloof from official events. Snobbery in the style of des Esseintes, or does he judge the approach to be unproductive in terms of visibility and therefore profitability? There is a certain paradox in considering that Iribe does "everything" to emerge from anonymity and become an important artistic figure of these 10 years but does nothing to cultivate his popularity. The few articles that pay tribute to him testify to this creative effervescence that proceeds both from prodigy and the art of dispersion. For some, Iribe is the precursor of modern Decorative Art, the one "who showed the way", for others, he is more the man of aborted revolutions, never fully exploiting his brilliant intuitions. Also, to follow the artistic itinerary of Paul Iribe is to follow the path of an original artist, fiercely attached to his freedom, but also to follow the aesthetic problems that confront modern decorator artists in these pre-war years when the "cosmopolitanism quarrel" is in full swing. To follow the journey of Paul Iribe is to understand the complexity of this pivotal period, the passage from one style to another, from Art Nouveau to Art Deco. This "new style", which is being developed in the 1910s, owes at least as much to Paul Iribe as to figures now emblematic of this transition, such as André Groult. The rose and the shagreen, so characteristic of this period, owe to Iribe; for the first its iconic status, for the second, its revival, until it becomes the symbol of Art Deco. Exiled to the United States, where he puts his talent at the service of producer-director Cecil DeMille, he misses the effervescence of the 20s and disappears completely from the Parisian scene. But even more painful is the indifference in which the organizers of the 1925 International Exhibition of Decorative Arts hold him. No reminder is made of this "rose" style. He is forgotten. By exiling himself, Iribe has put himself on the index of all the new trends, of which he was one of the precursors, and which the war has given birth to. Bitterness is emerging. The daring decorator of the pre-war years gives way to a virulent pamphleteer. He launches a campaign against the art of his time and puts all his energy into defending the luxury industries and craftsmanship. But are these really his convictions? His conquest of Gabrielle Chanel and his talents as a publicist, however, suggest new perspectives for his work. Their emotional but also artistic complicity is profitable for him. With this last companion, his talent, intact, is expressed again without restraint
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Zajicek, Daniel J. "A rhetorical guide to Ebb." connect to online resource, 2006. http://www.unt.edu/etd/all/May2006/zajicek%5Fdaniel/index.htm.

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Thesis (M.M.)--University of North Texas, 2006.
For flute, oboe, clarinet, contrabassoon, horn , tuba, percussion (2 players), 2 violins, viola, cello, double bass, 1 laptop computer, and 2 or more loudspeakers. System requirements: Adobe Acrobat Reader. Duration: 10:00. Includes performance notes by the composer (p. 1-23). Includes bibliographical references (p. 23-24).
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Daigle, Elise. "Examining Music Ensemble Recruitment and Retention through Student Persistence into College Performing Ensembles." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1523523995130136.

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Swanzy, David Paul. "The wind ensemble and its music during the French Revolution (1789-1795) /." Ann Arbor : UMI, 2000. http://catalogue.bnf.fr/ark:/12148/cb370598520.

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Levine, Josh Levine Josh Levine Josh Levine Josh. "Between image, process, and memory /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2002. http://wwwlib.umi.com/cr/ucsd/fullcit?p3049670.

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Khatib, Souad El. "Espaces métriques dans la théorie des ensembles flous." Lyon 1, 1986. http://www.theses.fr/1986LYO10060.

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Cette these est composee de trois chapitres. Le premier est un rappel des travaux de michael erceg, sur la definition d'un espace metrique flou: metric spaces in fuzzy set theory, j. M. A. , 69, 1979 (205-230). Dans cet article l'auteur a defini un ecart (une distance), flou(e) sur un ensemble flou, prenant ses valeurs dans l'ensemble des reels positif. Dans le deuxieme chapitre on introduit l'ensemble des reels flous positifs, c'est l'ensemble des applications de l'ensemble des reels dans l'intervalle unite, qui sont continues a gauche, croissantes et qui prennent la valeur zero a zero et telles la borne superieure est egale a un. Puis on decrit l'operation d'addition cet ensemble definie par sherwood et m. D. Taylor dans leur article "some pm structures on the set of distribution functions". Le troisieme chapitre constitue le but premier de cette these et consiste a donner une definition d'un quasi-ecart (ecart) flou, sur un ensemble flou, prenant ses valeurs non pas dans l'ensemble des reels positifs, mais dans l'ensemble des reels flous positifs. Puis on definit une uniformite floue sur un espace pseudometrique flou, en utilisant la definition d'une uniformite floue donnee par hutton: "uniformities on fuzzy topological spaces". En completant les resultats de hutton, on obtient des resultats sur l'engendrement des topologies floues sur les ensembles flous par des quasi-ecarts flous, on definit encore l'ecart flou conjugue, puis la distance floue
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Andrle, Miroslav. "Ensembles modèles et analyse en ondelettes adaptées." Paris 7, 2002. http://www.theses.fr/2002PA077202.

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Dalton, Grant Beckett. "Nigel Westlake's Omphalo Centric Lecture guide for performance including a biography of the composer and an examination of the different versions of the work /." Columbus, Ohio : Ohio State University, 2006. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1155572878.

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Anderson, Andrew E. (Andrew Edwin). "Hailstones and Birdcages for Wind Ensemble." Thesis, University of North Texas, 1990. https://digital.library.unt.edu/ark:/67531/metadc500256/.

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Hailstones and Birdcages is a composition of approximately thirteen minutes' duration and is scored for two flutes and piccolo, two oboes and english horn, three Eb clarinets, E clarinete, bass clarinet, two bassoons, two Eb alto saxophones, Bb tenor saxophone, three Bb trumpets, four F horns, three trombones, euphonium, two tubas, and three percussionists. Four instruments--one each of flute, oboe, Bb clarinet, and trombone--are used in concertante like fashion, and there are prominent solo passages for the first bassoon, as well. The work is a single movement in three sections, fast - slow - fast, with ritornello. and employs a free use of the total chromatic. Technically, the work is within the capabilities of an above-average high school or average college wind ensemble
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Books on the topic "Ensemblier"

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Marcilhac, Félix. André Groult (1884-1966): Décorateur-ensemblier du XXe siècle. Paris: Les Éditions de l'Amateur, 1997.

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Glass, Philip. Songs from liquid days. New York, N.Y: CBS, 1986.

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McDonald, Donna. The odyssey of the Philip Jones Brass Ensemble. Bulle, Switzerland: Editions Bim, 1986.

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Ma, Yo-Yo. A playlist without borders. New York, NY: Masterworks, 2013.

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Vierdanck, Johann. Geistliche Konzerte: Zweiter Teil. Kassel: Bärenreiter, 1990.

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Vierdanck, Johann. Geistliche Konzerte: Erster Teil. Kassel: Bärenreiter, 1988.

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Radomski, Mikołaj. Dzieła wszystkie: Complete works. Warsaw: Dux, 2004.

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Górecki, Henryk Mikołaj. The essential Górecki: Orchestral & choral music. London: Olympia, 1993.

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Bockstal, Koen van. Radicale ensembles: Radical ensembles. Brussel: Academic & Scientific Publishers, 2018.

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Françoise, Siriex, ed. Leleu: Décorateurs ensembliers. Saint-Rémy-en-l'Eau: Monelle Hayot, 2007.

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Book chapters on the topic "Ensemblier"

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Resseguier, Valentin, Yicun Zhen, and Bertrand Chapron. "Constrained Random Diffeomorphisms for Data Assimilation." In Mathematics of Planet Earth, 281–92. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-40094-0_13.

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AbstractFor ensemble-based data assimilation purposes, there is a definite need for relevant ensemble sampling tools. Indeed, the quality and spreading of these ensembles have deep implications in the quality of the data assimilation, and—until recently—those so-called covariance inflation tools have mostly relied on unsuitable linear Gaussian frameworks. A promising alternative is the generation of ensembles through a stochastic remapping of the physical space.
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Hennicker, Rolf, Alexander Knapp, and Martin Wirsing. "Epistemic Ensembles." In Leveraging Applications of Formal Methods, Verification and Validation. Adaptation and Learning, 110–26. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-19759-8_8.

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AbstractAn ensemble consists of a set of computing entities which collaborate to reach common goals. We introduce epistemic ensembles that use shared knowledge for collaboration between agents. Collaboration is achieved by different kinds of knowledge announcements. For specifying epistemic ensemble behaviours we use formulas of dynamic logic with compound ensemble actions. Our semantics relies on an epistemic notion of ensemble transition systems as behavioural models. These transition systems describe control flow over epistemic states for expressing knowledge-based collaboration of agents. Specifications are implemented by epistemic processes that are composed in parallel to form ensemble realisations. We give a formal operational semantics of these processes that generates an epistemic ensemble transition system. A realisation is correct w. r. t. an ensemble specification if its semantics is a model of the specification.
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Yin, Xu-Cheng, Kaizhu Huang, and Hong-Wei Hao. "Dynamic Ensemble of Ensembles in Nonstationary Environments." In Neural Information Processing, 76–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-42042-9_10.

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Brazdil, Pavel, Jan N. van Rijn, Carlos Soares, and Joaquin Vanschoren. "Metalearning in Ensemble Methods." In Metalearning, 189–200. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-67024-5_10.

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AbstractThis chapter discusses some approaches that exploit metalearning methods in ensemble learning. It starts by presenting a set of issues, such as the ensemble method used, which affect the process of ensemble learning and the resulting ensemble. In this chapter we discuss various lines of research that were followed. Some approaches seek an ensemble-based solution for the whole dataset, others for individual instances. Regarding the first group, we focus on metalearning in the construction, pruning and integration phase. Modeling the interdependence of models plays an important part in this process. In the second group, the dynamic selection of models is carried out for each instance. A separate section is dedicated to hierarchical ensembles and some methods used in their design. As this area involves potentially very large configuration spaces, recourse to advanced methods, including metalearning, is advantageous. It can be exploited to define the competence regions of different models and the dependencies between them.
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Giraud-Carrier, Christophe. "Combining Base-Learners into Ensembles." In Metalearning, 169–88. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-67024-5_9.

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AbstractThis chapter discusses ensembles of classification or regression models, because they represent an important area of machine learning. They have become popular as they tend to achieve high performance when compared with single models. Besides, they also play an essential role in data-streaming solutions. This chapter starts by introducing ensemble learning and presents an overview of some of its most well-known methods. These include bagging, boosting, stacking, cascade generalization, cascading, delegating, arbitrating and meta-decision trees.
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Abbasian, Houman, Chris Drummond, Nathalie Japkowicz, and Stan Matwin. "Inner Ensembles: Using Ensemble Methods Inside the Learning Algorithm." In Advanced Information Systems Engineering, 33–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40994-3_3.

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Banfield, Robert E., Lawrence O. Hall, Kevin W. Bowyer, and W. Philip Kegelmeyer. "A New Ensemble Diversity Measure Applied to Thinning Ensembles." In Multiple Classifier Systems, 306–16. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44938-8_31.

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Aggarwal, Charu C., and Saket Sathe. "An Introduction to Outlier Ensembles." In Outlier Ensembles, 1–34. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54765-7_1.

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Aggarwal, Charu C., and Saket Sathe. "Theory of Outlier Ensembles." In Outlier Ensembles, 35–74. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54765-7_2.

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Aggarwal, Charu C., and Saket Sathe. "Variance Reduction in Outlier Ensembles." In Outlier Ensembles, 75–161. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54765-7_3.

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Conference papers on the topic "Ensemblier"

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Sokolov, Andrey Pavlovich. "On expressive abilities of ensembles of decision trees." In Academician O.B. Lupanov 14th International Scientific Seminar "Discrete Mathematics and Its Applications". Keldysh Institute of Applied Mathematics, 2022. http://dx.doi.org/10.20948/dms-2022-72.

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Decision trees and their ensembles are widely used in machine learning, statistics and data analysis. Predictive models based on decision trees, show outstanding results in terms of quality and learning time. Especially on heterogeneous tabular data. Speed performance, simplicity and reliability make this family of predictive one of the most popular models in machine learning. important parameters when training ensembles of decision trees (random forest, gradient boosting, etc.) are: the number of trees and their maximum depth. These options are usually selected by a complete enumeration of all possible options on the training sample. The report will prove a theorem on expressive the possibilities of an ensemble of decision trees of limited depth. From of this result it follows that the depth of the decisive trees cannot be replaced by the size of the ensemble.
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Li, Jinyang, Gregório R. M. da Silva, Schuyler Kain, and Selim M. Shahriar. "Control of Orientation and Entanglement of Macroscopic Magnetization Using a Single Atom." In Frontiers in Optics. Washington, D.C.: Optica Publishing Group, 2022. http://dx.doi.org/10.1364/fio.2022.jw5a.73.

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We propose a technique that can control the macroscopic magnetization of an ensemble of atoms with a single atom. This technique can also be used for entangling two ensembles of atoms at a long distance.
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Omer, Zahi M., Abdulla Al Seiari, Zhaohui Cen, and Francisco Bilendo. "Machine Learning Regression Models for Turbofan Engines: A Comparative Study on Remaining Useful Life Prediction." In ASME Turbo Expo 2024: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2024. http://dx.doi.org/10.1115/gt2024-127544.

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Abstract This study conducts a systematic comparison of data-driven regression models for predicting the Remaining Useful Life (RUL) of turbofan engines in aviation Prognostics Health Management (PHM) field, utilizing the C-MAPSS dataset with advanced feature engineering, the paper evaluates models from simple linear to complex high-dimensional ensemble structures. Notably, the CatBoost model and other ensemble techniques demonstrated superior performance, with CatBoost achieving an R2 value of 99% and other ensembles averaging 92%, significantly outperforming other models with 65% to 77% values. This indicates the robustness of ensemble machine learning methods in precise RUL estimation.
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Georgiou, Ioannis T. "Intrinsic Spatio-Temporal Resolution Analysis of Nondestructive Impact Diagnostic Force Ensembles and Collocated Accelerations in a Composite Beam Structure." In ASME 2012 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/smasis2012-8083.

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Several ensembles of sweeping diagnostic impulsive forces were measured in a unidirectional carbon-epoxy composite beam modified locally with a soft viscoelastic patch. The spatial uniformity of the typical ensemble of diagnostic signals is addressed by a systematic spatio-temporal coherence analysis in terms of proper orthogonal decomposition (POD) modes. All samples of spanning ensembles are strongly dominated by the same POD mode characterized by a nearly uniform spatial modulation and a sharp triangular pulse time modulation. The higher POD modes have small amounts of energy. They possess an important statistics property: their spatial modulation mean value is nearly zero with standard deviation nearly identical to the nearly uniform value of the dominant POD mode. The nearly uniform spatial distribution of the dominant POD mode is a fuzzy picture of the ideal or nominal one where the impact-generated diagnostic forces should have a time waveform independent of the site of impact. Despite this energy content deficiency, the ensemble of acceleration signals acquired at a fixed point while the beam is excited by an ensemble of sweeping diagnostic forces has very robust POD modal structure. The POD modes show in a clear manner the presence of a soft viscoelastic patch simulating mass modifications. The POD-based coherence analysis of ensembles of diagnostic forces generated in this practical problem is potentially useful for a real-time verification-inspection of the integrity of networks of embedded and surface-mounted actuators and sensors.
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Nemani, Venkat, Adam Thelen, Chao Hu, and Steve Daining. "Dynamically Weighted Ensemble of Diverse Learners for Remaining Useful Life Prediction." In ASME 2022 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/detc2022-90252.

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Abstract A traditional ensemble approach to predicting the remaining useful life (RUL) of equipment and other assets has been constructing data-driven and model-based ensembles using identical estimators. This ensemble approach may perform well on quality data collected from a design-of-experiments test but may ultimately fail when deployed in the field because of higher-than-expected noise, missing measurements, and different degradation trends. In such work environments, the high similarity of the learners can lead to large under/overestimates of RUL, where the ensemble is only as accurate as the learner which under/overestimates RUL the least. In response to this, we investigate whether an ensemble of diverse learners might be able to predict RUL consistently and accurately by aggregating the predictions of various algorithms which are found to perform differently under the same conditions. We propose improving ensemble model performance by 1) using a combination of diverse learning algorithms which are found to perform differently under the same conditions and 2) training a data-driven model to adaptively estimate the prediction weight each learner receives. The proposed methods are evaluated using open-source run-to-failure datasets from two popular systems of prognostics research: lithium-ion batteries and rolling element bearings. Results for the lithium-ion battery case study indicate that the proposed method achieves a 12% improvement in RUL prediction accuracy and a 27% decrease in the average uncertainty calibration error when compared to ensembles without dynamic weighting, and an even larger improvement when compared to individual models.
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Alqallabi, Salahaldeen, Mohamed Tarik Gacem, Faisal Al-Jenaibi, Sofiane Tahir, Saeeda Mohamed Al Ameri, Djamel Eddine Ouzzane, Mustapha Adli, et al. "Key Learnings from Application of Ensemble-Based Modelling on Three Giant Carbonate Oilfields for Optimal Economic Decisions Under Static and Dynamic Uncertainties." In ADIPEC. SPE, 2022. http://dx.doi.org/10.2118/211409-ms.

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Abstract The scope of this publication is to capture the main learnings from the application of ensemble-based modelling of three giant geologically complex carbonate reservoirs onshore and offshore Abu Dhabi, simultaneously considering static and dynamic uncertainties. The ability to consider these uncertainties in prediction studies is highlighted, leading to optimal economic decisions to be taken in the subsequent stages of development of these critical assets. For each oilfield, an integrated static-to-simulation modelling workflow was built in collaboration with the relevant asset teams, capturing their knowledge and expertise in the generation of an ensemble of cases, equiprobable and plausible from the point-of-view of geology and dynamic characteristics. Each of them has specific geological and hydrodynamic challenges to be taken into account, from the spatial distribution of the static rock types and of their heterogeneous petrophysical properties, the impact on the flow of high permeability streaks and stylolites, to the behavior of the aquifers. Each ensemble of cases is subsequently utilized to assimilate the production data using an iterative ensemble Kalman method yielding ensembles with the ability to reproduce the observed reservoir dynamics. These calibrated ensembles can subsequently be used for predictions and economic evaluations considering all remaining static and dynamic uncertainties. After data assimilation, the ensembles showed reasonable match to field and well historical data for the three different studies. There were significant learnings in the static and dynamic updates and uncertainty reduction that occurred during the data assimilation. They provided statistical insights with respect to the reservoir characterization, such as increased high permeability streaks probability in specific zones or reduced uncertainty surrounding the porosity/permeability transforms for each rock type, and fluid dynamic, such as fault behavior. These learnings will benefit the team to further their understanding and improve future modelling activities. Multiple development scenarios were considered for each asset and the simulated ensemble results were brought for economic evaluation under static and dynamic uncertainties. This provided representative estimates of the net present value of each scenario and eventually, a complete understanding of the potential outcome, allowing for informed decisions. Finally, another important benefit of working with calibrated ensembles was shown in its ability to identify the most likely bypassed area from a probabilistic standpoint, allowing to take confident decisions for new target identifications to increase the ultimate field recovery. While addressing the future challenges of major carbonate oilfield developments and to ensure an optimal decision-making process, the asset team has to consider the complexity of the underlying geological environment, the dynamics of the fluid in the reservoir and their associated uncertainties. An integrated ensemble-based approach from static to simulation with fast data assimilation and economic evaluation of possible scenarios proved to be key to reach all of these objectives.
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Rezaveisi, Mohsen, Puji Pujiyono, Yvonne Gonzalez Hernandez, Geoffrey Bent, and Gopal Mohapatra. "Ensemble-Based Reservoir Simulation with 4D Seismic for Assessment of Infill Opportunities: Case Study in a Deepwater Gulf of Mexico Field." In SPE Oklahoma City Oil and Gas Symposium. SPE, 2025. https://doi.org/10.2118/224340-ms.

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Abstract We present the ensemble-based reservoir modeling and uncertainty study in Miocene reservoirs of a deepwater Gulf of Mexico (GoM) field with extensive production history. The purpose of the simulation study was to identify and characterize infill opportunities to arrest the field decline and maximize asset value. Static and dynamic uncertainty parameters were identified and built into the integrated geological and dynamic modeling workflow, where the full range of geologically consistent reservoir models are history matched and used as a basis for production forecasts. The ensemble modeling workflow (commercial software) includes: i) the creation of an initial ensemble of cases (e.g. 100 simulation cases) based on the initial set and range of uncertainty parameters; ii) defining the objective functions and post-processing/visualizing the results of initial ensemble modeling to ensure coverage of the objective functions; iii) defining the uncertainty variables to be optimized in the history matching process; and iv) running the iterations of the history matching ensembles. The ensembles with the best history match quality were then selected for forecasting and testing infill locations. In addition to reservoir pressure and production data, 4D seismic was also used as a history match objective function. Matching the 4D seismic with hydrocarbon pore thickness (HPT) difference was essential to constrain the range of aquifer size and oil in place as sources of reservoir energy. To achieve a good history match quality, first an approximate match was obtained for 4D seismic HPT difference by testing sensitivity to aquifer size range using a small number of ensembles (10-20 ensembles). After narrowing down the aquifer size range, the ensemble modeling history matching workflow was used to match production and pressure data while fine tuning the 4D - HPT difference match. Initial productivity index (PI) and PI degradation assumptions were the most impactful subsurface parameters on incremental Estimated Ultimate Recovery (EUR) for the tested infill well locations. The integrated geo-dynamic modeling workflow enabled a consistent assessment of the infill bottomhole locations (selected based on seismic attributes) using the simulator calculated initial PI for infills. This was achieved by: i) conditioning the geostatistical facies and property modeling workflow to seismic amplitudes, and ii) ensuring the simulator calculated well PI matched well test PI for the existing wells. The compelling results of the uncertainty evaluation influenced the decision to progress the maturation of a new infill well by the asset co-owners. We demonstrate the application of a fully integrated workflow for geological and dynamic modeling using ensemble-based reservoir simulation which uses 4D seismic to calibrate HPT difference as one of the objective functions to constrain the aquifer size uncertainty. The integrated reservoir modeling approach results in a consistent assessment of infill bottomhole locations where estimates of the initial well PI is driven by seismic attributes.
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Cooney, Alanna Y., and Van P. Carey. "Optimized Design and Control of a Multi-Temperature, Multi-Module Thermal Energy Storage Ensemble." In ASME 2023 Heat Transfer Summer Conference collocated with the ASME 2023 17th International Conference on Energy Sustainability. American Society of Mechanical Engineers, 2023. http://dx.doi.org/10.1115/ht2023-107406.

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Abstract Latent thermal energy storage (LTES) systems which store energy in a phase change material (PCM) are effective in bridging the gap between renewable energy supply and demand for applications such as concentrated solar plants and building heating and cooling systems. Cascaded LTES systems which use multiple PCMs with different melt temperatures can be used to improve performance by increasing energy and exergy efficiencies and increasing rates for charging and discharging. A multi-temperature, multi-module (MTMM) LTES ensemble controlled by an Artificial Neural Network (ANN) can further improve performance by allowing for various working fluid flow paths through the system which allow for the ensemble to adapt to dynamically changing and unforeseen operating conditions. The MTMM ensemble is optimized to control to dual objectives of meeting a target working fluid outlet temperature and minimizing the instantaneous rate of exergy destruction allowing it to both achieve operational reliability and maximize second law efficiencies. In this investigation, design optimization of the MTMM ensemble is explored by comparing various parallel and series configurations of modules and exploring the effects of TES module layout, PCM melt temperatures, and the overall heat transfer coefficient of individual modules on the performance of the ensemble. The results demonstrate the impact of optimization of design parameters on the performance of MTMM ensembles and the potential for the use of data driven optimizers such as genetic algorithms for design of MTMM ensembles. The performance of the MTMM ensemble with optimized design and adaptive ANN controller is demonstrated to achieve the exergy efficiency of a cascaded PCM system while also maintaining the flexibility needed to respond to dynamically changing operating conditions. This novel approach widens the potential applications of cascaded PCM by providing a means to standardize manufacturing to drive down system cost and allowing the system to adapt to dynamic operating conditions and unforeseen design parameters.
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Alabi, Oluwafemi S., Xunlei Wu, Jonathan M. Harter, Madhura Phadke, Lifford Pinto, Hannah Petersen, Steffen Bass, et al. "Comparative visualization of ensembles using ensemble surface slicing." In IS&T/SPIE Electronic Imaging, edited by Pak Chung Wong, David L. Kao, Ming C. Hao, Chaomei Chen, Robert Kosara, Mark A. Livingston, Jinah Park, and Ian Roberts. SPIE, 2012. http://dx.doi.org/10.1117/12.908288.

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S.V., Zavadskiy. "ADVANTAGES OF CONTROLLING ENSEMBLES OF TRAJECTORIES IN NONLINEAR CONTROL SYSTEM OPTIMIZATION." In Scientific research of the SCO countries: synergy and integration. Crossref, 2024. http://dx.doi.org/10.34660/inf.2024.12.79.052.

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It’s considered the advantages of controlling the trajectories ensembles for nonlinear control system optimization problems. This made it possible not only to estimate the quality of nonlinear stabilization in the presence of a set of initial and external disturbances, but also to optimize the dynamics of the entire ensemble of nonlinear motions.
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Reports on the topic "Ensemblier"

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Pedersen, Gjertrud. Symphonies Reframed. Norges Musikkhøgskole, August 2018. http://dx.doi.org/10.22501/nmh-ar.481294.

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Symphonies Reframed recreates symphonies as chamber music. The project aims to capture the features that are unique for chamber music, at the juncture between the “soloistic small” and the “orchestral large”. A new ensemble model, the “triharmonic ensemble” with 7-9 musicians, has been created to serve this purpose. By choosing this size range, we are looking to facilitate group interplay without the need of a conductor. We also want to facilitate a richness of sound colours by involving piano, strings and winds. The exact combination of instruments is chosen in accordance with the features of the original score. The ensemble setup may take two forms: nonet with piano, wind quartet and string quartet (with double bass) or septet with piano, wind trio and string trio. As a group, these instruments have a rich tonal range with continuous and partly overlapping registers. This paper will illuminate three core questions: What artistic features emerge when changing from large orchestral structures to mid-sized chamber groups? How do the performers reflect on their musical roles in the chamber ensemble? What educational value might the reframing unfold? Since its inception in 2014, the project has evolved to include works with vocal, choral and soloistic parts, as well as sonata literature. Ensembles of students and professors have rehearsed, interpreted and performed our transcriptions of works by Brahms, Schumann and Mozart. We have also carried out interviews and critical discussions with the students, on their experiences of the concrete projects and on their reflections on own learning processes in general. Chamber ensembles and orchestras are exponents of different original repertoire. The difference in artistic output thus hinges upon both ensemble structure and the composition at hand. Symphonies Reframed seeks to enable an assessment of the qualities that are specific to the performing corpus and not beholden to any particular piece of music. Our transcriptions have enabled comparisons and reflections, using original compositions as a reference point. Some of our ensemble musicians have had first-hand experience with performing the original works as well. Others have encountered the works for the first time through our productions. This has enabled a multi-angled approach to the three central themes of our research. This text is produced in 2018.
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Cobb, Kelly. Saori Ensemble. Ames: Iowa State University, Digital Repository, November 2015. http://dx.doi.org/10.31274/itaa_proceedings-180814-1253.

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Chae, May. Lucky Ensemble. Ames: Iowa State University, Digital Repository, September 2016. http://dx.doi.org/10.31274/itaa_proceedings-180814-1586.

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Wang, Xugueng. Ensemble Forecasting with the Ensemble Transform Kalman Filter. Fort Belvoir, VA: Defense Technical Information Center, August 2004. http://dx.doi.org/10.21236/ada429161.

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Pulley, Zoe, and kristin caskey. Zero Waste Ensemble. Ames: Iowa State University, Digital Repository, November 2015. http://dx.doi.org/10.31274/itaa_proceedings-180814-1202.

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Dixon, Matt C. Ensembles and Their Applications. Fort Belvoir, VA: Defense Technical Information Center, December 2000. http://dx.doi.org/10.21236/ada386942.

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Orzada, Belinda T. Denim and Silk Ensemble. Ames: Iowa State University, Digital Repository, 2017. http://dx.doi.org/10.31274/itaa_proceedings-180814-235.

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Mylne, Ken, Oak Wells, David Walters, Adam Scaife, Nigel Roberts, and Rosa Barciela. Glossary of terms for Ensembles. Met Office, November 2024. http://dx.doi.org/10.62998/gwwk6803.

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Buckley, R., S. Chiswell, R. Kurzeja, G. Maze, B. Viner, and D. Werth. Advanced Atmospheric Ensemble Modeling Techniques. Office of Scientific and Technical Information (OSTI), September 2017. http://dx.doi.org/10.2172/1395969.

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Orzada, Belinda T. Upcycle and Zero Waste Ensemble. Ames: Iowa State University, Digital Repository, 2017. http://dx.doi.org/10.31274/itaa_proceedings-180814-236.

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