Relevant bibliographies by topics / Variational tasks

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Variational tasks'

Author: Grafiati
Published: 28 May 2022
Last updated: 31 July 2025

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Journal articles on the topic "Variational tasks"

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Cervantes-Barraza, Jonathan Alberto, Shelsyn Johana Moreno Calvo, and Kattia Lucia de Arce Polo. "Mathematical Modelling and Argumentation: Designing a Task to Strengthen Variational Thinking by Integrating Data Science." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 16, no. 1 (2025): 25–43. https://doi.org/10.61841/turcomat.v16i1.14975.

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This study explores the significance of task design in fostering variational thinking through the principles of PyLVar and task design in mathematics education, particularly in the incorporation of technological and scientific tools to enhance modelling and argumentation skills among secondary school students. To develop and implement interactive mathematical tasks based on the PyLVar principle and task design, incorporating data science tools and STEAM. A qualitative-descriptive approach was employed to design, implement, and analyse interactive tasks centred on the principles of variational
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Xiang, Guofei, Songyi Dian, Shaofeng Du, and Zhonghui Lv. "Variational Information Bottleneck Regularized Deep Reinforcement Learning for Efficient Robotic Skill Adaptation." Sensors 23, no. 2 (2023): 762. http://dx.doi.org/10.3390/s23020762.

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Deep Reinforcement Learning (DRL) algorithms have been widely studied for sequential decision-making problems, and substantial progress has been achieved, especially in autonomous robotic skill learning. However, it is always difficult to deploy DRL methods in practical safety-critical robot systems, since the training and deployment environment gap always exists, and this issue would become increasingly crucial due to the ever-changing environment. Aiming at efficiently robotic skill transferring in a dynamic environment, we present a meta-reinforcement learning algorithm based on a variation
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Perez, Iker, and Giuliano Casale. "Variational inference for Markovian queueing networks." Advances in Applied Probability 53, no. 3 (2021): 687–715. http://dx.doi.org/10.1017/apr.2020.72.

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AbstractQueueing networks are stochastic systems formed by interconnected resources routing and serving jobs. They induce jump processes with distinctive properties, and find widespread use in inferential tasks. Here, service rates for jobs and potential bottlenecks in the routing mechanism must be estimated from a reduced set of observations. However, this calls for the derivation of complex conditional density representations, over both the stochastic network trajectories and the rates, which is considered an intractable problem. Numerical simulation procedures designed for this purpose do n
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Gatopoulos, Ioannis, and Jakub M. Tomczak. "Self-Supervised Variational Auto-Encoders." Entropy 23, no. 6 (2021): 747. http://dx.doi.org/10.3390/e23060747.

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Density estimation, compression, and data generation are crucial tasks in artificial intelligence. Variational Auto-Encoders (VAEs) constitute a single framework to achieve these goals. Here, we present a novel class of generative models, called self-supervised Variational Auto-Encoder (selfVAE), which utilizes deterministic and discrete transformations of data. This class of models allows both conditional and unconditional sampling while simplifying the objective function. First, we use a single self-supervised transformation as a latent variable, where the transformation is either downscalin
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Nielsen, Frank. "On a Variational Definition for the Jensen-Shannon Symmetrization of Distances Based on the Information Radius." Entropy 23, no. 4 (2021): 464. http://dx.doi.org/10.3390/e23040464.

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We generalize the Jensen-Shannon divergence and the Jensen-Shannon diversity index by considering a variational definition with respect to a generic mean, thereby extending the notion of Sibson’s information radius. The variational definition applies to any arbitrary distance and yields a new way to define a Jensen-Shannon symmetrization of distances. When the variational optimization is further constrained to belong to prescribed families of probability measures, we get relative Jensen-Shannon divergences and their equivalent Jensen-Shannon symmetrizations of distances that generalize the con
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Sheng, Xin, Linli Xu, Junliang Guo, Jingchang Liu, Ruoyu Zhao, and Yinlong Xu. "IntroVNMT: An Introspective Model for Variational Neural Machine Translation." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 8830–37. http://dx.doi.org/10.1609/aaai.v34i05.6411.

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We propose a novel introspective model for variational neural machine translation (IntroVNMT) in this paper, inspired by the recent successful application of introspective variational autoencoder (IntroVAE) in high quality image synthesis. Different from the vanilla variational NMT model, IntroVNMT is capable of improving itself introspectively by evaluating the quality of the generated target sentences according to the high-level latent variables of the real and generated target sentences. As a consequence of introspective training, the proposed model is able to discriminate between the gener
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Wang, Wenlin, Hongteng Xu, Zhe Gan, et al. "Graph-Driven Generative Models for Heterogeneous Multi-Task Learning." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (2020): 979–88. http://dx.doi.org/10.1609/aaai.v34i01.5446.

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We propose a novel graph-driven generative model, that unifies multiple heterogeneous learning tasks into the same framework. The proposed model is based on the fact that heterogeneous learning tasks, which correspond to different generative processes, often rely on data with a shared graph structure. Accordingly, our model combines a graph convolutional network (GCN) with multiple variational autoencoders, thus embedding the nodes of the graph (i.e., samples for the tasks) in a uniform manner, while specializing their organization and usage to different tasks. With a focus on healthcare appli
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Zhu, Hui, Shi Shu, and Jianping Zhang. "FAS-UNet: A Novel FAS-Driven UNet to Learn Variational Image Segmentation." Mathematics 10, no. 21 (2022): 4055. http://dx.doi.org/10.3390/math10214055.

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Solving variational image segmentation problems with hidden physics is often expensive and requires different algorithms and manually tuned model parameters. The deep learning methods based on the UNet structure have obtained outstanding performances in many different medical image segmentation tasks, but designing such networks requires many parameters and training data, which are not always available for practical problems. In this paper, inspired by the traditional multiphase convexity Mumford–Shah variational model and full approximation scheme (FAS) solving the nonlinear systems, we propo
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Dai, Weihang, Xiaomeng Li, and Kwang-Ting Cheng. "Semi-Supervised Deep Regression with Uncertainty Consistency and Variational Model Ensembling via Bayesian Neural Networks." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 6 (2023): 7304–13. http://dx.doi.org/10.1609/aaai.v37i6.25890.

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Deep regression is an important problem with numerous applications. These range from computer vision tasks such as age estimation from photographs, to medical tasks such as ejection fraction estimation from echocardiograms for disease tracking. Semi-supervised approaches for deep regression are notably under-explored compared to classification and segmentation tasks, however. Unlike classification tasks, which rely on thresholding functions for generating class pseudo-labels, regression tasks use real number target predictions directly as pseudo-labels, making them more sensitive to prediction
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Krishnan, Ranganath, Mahesh Subedar, and Omesh Tickoo. "Specifying Weight Priors in Bayesian Deep Neural Networks with Empirical Bayes." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 4477–84. http://dx.doi.org/10.1609/aaai.v34i04.5875.

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Stochastic variational inference for Bayesian deep neural network (DNN) requires specifying priors and approximate posterior distributions over neural network weights. Specifying meaningful weight priors is a challenging problem, particularly for scaling variational inference to deeper architectures involving high dimensional weight space. We propose MOdel Priors with Empirical Bayes using DNN (MOPED) method to choose informed weight priors in Bayesian neural networks. We formulate a two-stage hierarchical modeling, first find the maximum likelihood estimates of weights with DNN, and then set
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Dissertations / Theses on the topic "Variational tasks"

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Периг, А. В., И. А. Матвеев та Н. А. Лимаренко. "Постановка вариационных задач для равноканального пластического течения". Thesis, Видавництво СумДУ, 2012. http://essuir.sumdu.edu.ua/handle/123456789/27598.

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Sghaier, Maissa. "Clinical-task based reconstruction in Digital Breast Tomosynthesis." Electronic Thesis or Diss., université Paris-Saclay, 2020. http://www.theses.fr/2020UPASG040.

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La reconstruction en tomosynthèse numérique du sein est considérée comme un problème inverse, pour lequel les méthodes itératives régularisées permettent de fournir une bonne qualité d'image. Bien que la tâche clinique joue un rôle crucial lors de l’examen des images par le radiologue, elle n'a pas été jusqu'à présent directement prise en compte dans le processus de reconstruction des images de tomosynthèse. Dans cette thèse, nous introduisons une nouvelle formulation variationnelle de la reconstruction des images en tomosynthèse numérique du sein qui intègre la tâche clinique du radiologue, n
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Cornish, Kim M. "Variations in spatial cognition in adults and children : influence of handedness, familial sinistrality and sex." Thesis, Royal Holloway, University of London, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.242847.

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Tareen, Shahwali Khan. "Analyse moléculaire des canaux potassiques task dans l'aldostéronisme primaire humain." Thesis, Paris 6, 2014. http://www.theses.fr/2014PA066056.

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L'hyperaldostéronisme primaire (HAP) est la plus fréquente cause identifiable de l'hypertension, et résulte de la production autonome d'aldostérone par les glandes surrénales. Chez la souris, la délétion génétique des canaux TASK1 et TASK3 provoque des changements biochimiques qui imitent HAP humain. Ces canaux permet la sortie de K+ et polarise le potentiel de la membrane des cellules glomérules. Nous avons étudié la variation et l'expression de KCNK3 et 9 chez l'homme. Notre étude d'association à montré aucune association d'HAP avec n'importe quel SNP au niveau de l'ensemble du génome. Le sé
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Zybrands, Helena. "An investigation of linguistic and cultural variation in the understanding and execution of academic writing tasks." Thesis, Link to the online version, 2007. http://hdl.handle.net/10019/1989.

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Spaeder, Nancy Joan. "Adolescent communication strategies and patterns in a collaborative task : variations by gender /." free to MU campus, to others for purchase, 2000. http://wwwlib.umi.com/cr/mo/fullcit?p3025671.

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Rostamzadeh, Negar. "Video Scene Understanding: Semantic-based representation, Temporal Variation Modeling, Multi-Task Learning." Doctoral thesis, Università degli studi di Trento, 2017. https://hdl.handle.net/11572/368321.

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One of the major research topics in computer vision is automatic video scene understanding where the ultimate goal is to build artificial intelligence systems comparable with humans in understanding video contents. Automatic video scene understanding covers many applications including (i) semantic functional complex scene categorization, (ii) human body-pose estimation in videos, (iii) human fine-grained daily living action recognition, (vi) video retrieval, and genre recognition. In this thesis, we introduce computer vision and pattern analysis techniques that outperform the state of art of t
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Rostamzadeh, Negar. "Video Scene Understanding: Semantic-based representation, Temporal Variation Modeling, Multi-Task Learning." Doctoral thesis, University of Trento, 2017. http://eprints-phd.biblio.unitn.it/2043/1/NegarThesis.pdf.

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One of the major research topics in computer vision is automatic video scene understanding where the ultimate goal is to build artificial intelligence systems comparable with humans in understanding video contents. Automatic video scene understanding covers many applications including (i) semantic functional complex scene categorization, (ii) human body-pose estimation in videos, (iii) human fine-grained daily living action recognition, (vi) video retrieval, and genre recognition. In this thesis, we introduce computer vision and pattern analysis techniques that outperform the state of art of
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Tareen, Shahwali Khan. "Analyse moléculaire des canaux potassiques task dans l'aldostéronisme primaire humain." Electronic Thesis or Diss., Paris 6, 2014. http://www.theses.fr/2014PA066056.

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L'hyperaldostéronisme primaire (HAP) est la plus fréquente cause identifiable de l'hypertension, et résulte de la production autonome d'aldostérone par les glandes surrénales. Chez la souris, la délétion génétique des canaux TASK1 et TASK3 provoque des changements biochimiques qui imitent HAP humain. Ces canaux permet la sortie de K+ et polarise le potentiel de la membrane des cellules glomérules. Nous avons étudié la variation et l'expression de KCNK3 et 9 chez l'homme. Notre étude d'association à montré aucune association d'HAP avec n'importe quel SNP au niveau de l'ensemble du génome. Le sé
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Genao, Ana. "False memories evidence for unintentional processing of semantically related words using a Stroop variation task /." Diss., Connect to the thesis, 2005. http://thesis.haverford.edu/117/01/2005GenaoA.pdf.

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Books on the topic "Variational tasks"

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Kogan, Efim. Ordinary differential equations and calculus of variations. INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1058922.

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The textbook contains theoretical information in a volume of the lecture course are discussed in detail and examples of typical tasks and test tasks and tasks for independent work. 
 Designed for students enrolled in directions of preparation 15.03.03 "Applied mechanics" 01.03.02 "mathematics" (specialization "Mathematical modeling"), major 23.05.01 "Land transport and technological means" (specialization "Dynamics and strength of transport and technological systems"). Can be used by teachers for conducting practical classes.
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Berwick, Richard. Task variation and repair in English as a foreign language. Institute of Economic Research, Kobe University of Commerce, 1990.

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School, Harvard Business, ed. Fluid teams and fluid tasks: The impact of team familiarity and variation in experience. 2nd ed. Harvard Business School, 2009.

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Burianova, Hana. Predictability of interindividual variation across a variety of tasks and further implications of quantitative interindividual differences in underlying neural processes. National Library of Canada, 2003.

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Dubanov, Aleksandr. Computer simulation in pursuit problems. Publishing Center RIOR, 2022. http://dx.doi.org/10.29039/02102-6.

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Currently, computer simulation in virtual reality systems has a special status. In order for a computer model to meet the requirements of the tasks it models, it is necessary that the mathematical apparatus correctly describe the simulated phenomena.
 In this monograph, the simulation of pursuit problems is carried out. An adaptive modeling of the behavior of both pursuers and targets is carried out. An iterative calculation of the trajectories of the participants in the pursuit problem is carried out.
 The main attention is paid to the methods of pursuit and parallel rendezvous. The
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Schra, L. Influence of gust spectrum variations on fatigue crack growth behaviour of damage tolerant aluminium-lithium sheet materials (NLR contribution to BREU 3250, Task 4). National Aerospace Laboratory, 1992.

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Corporation, Radiological Assessments, and Centers for Disease Control and Prevention (U.S.), eds. The Fernald dosimetry reconstruction project: Responses to comments from the National Research Council related to task 4, "environmental pathways--models and variation" (issued as a draft report for comment). Radiological Assessments Corporation, 1995.

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Uittenhove, Kim, and Patrick Lemaire. Numerical Cognition during Cognitive Aging. Edited by Roi Cohen Kadosh and Ann Dowker. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199642342.013.045.

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This chapter provides an overview of age-related changes and stabilities in numerical cognition. For each component (i.e. approximate and exact number system, quantification, and arithmetic) of numerical cognition, we review changes in participants’ performance during normal and pathological aging in a wide variety of tasks (e.g. number comparison, subitizing, counting, and simple or complex arithmetic problem-solving). We discuss both behavioral and neural mechanisms underlying these performance variations. Moreover, we highlight the importance of taking into account strategic variations. Ind
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Culicover, Peter W. Language Change, Variation, and Universals. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198865391.001.0001.

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This volume is about how human languages get to be the way they are, why they are different from one another in some ways and not others, and why they change in the ways that they do. Given that language is a universal creation of the human mind, the puzzle is why there are different languages at all, why we don’t all speak the same language. And while there is considerable variation, there are ways in which grammars show consistent patterns. The solution to these puzzles, the author proposes, is a constructional one. Grammars consist of constructions that carry out the function of expressing
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Tomaskovic-Devey, Donald, and Dustin Avent-Holt. Relational Inequalities. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780190624422.001.0001.

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Relational Inequalities focuses on the organizational production of categorical inequalities, in the context of the intersectional complexity and institutional fluidity that characterize social life. Three generic inequality-generating mechanisms—exploitation, social closure, and claims-making—distribute organizational resources, rewards, and respect. The actual levels and contours of the inequalities produced by these three mechanisms are, however, profoundly contingent on the historical moments and institutional fields in which organizations operate. Organizational inequality regimes are com
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Book chapters on the topic "Variational tasks"

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Policicchio, Antonio, Alberto Acuto, Paola Barillà, Ludovico Bozzolo, and Matteo Conterno. "A Variational Quantum Soft Actor-Critic Algorithm for Continuous Control Tasks." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-81247-7_13.

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Wu, Jiawei, and Zhi Jin. "Unsupervised Variational Translator for Bridging Image Restoration and High-Level Vision Tasks." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-73113-6_13.

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Huq Chowdhury, Mashfiqul, Yuichi Hirose, Stephen Marsland, and Yuan Yao. "Deep Mixtures of Variational Autoencoders Model for Representation Learning and Clustering Tasks." In Lecture Notes in Computer Science. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-6579-2_14.

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Khanal, Bidur, Binod Bhattarai, Bishesh Khanal, Danail Stoyanov, and Cristian A. Linte. "M-VAAL: Multimodal Variational Adversarial Active Learning for Downstream Medical Image Analysis Tasks." In Medical Image Understanding and Analysis. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-48593-0_4.

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Falińska, Krystyna. "Spatial Variations." In Tasks for vegetation science. Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-011-3266-4_19.

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Reiz, Severin, Tobias Neckel, and Hans-Joachim Bungartz. "Neural Nets with a Newton Conjugate Gradient Method on Multiple GPUs." In Parallel Processing and Applied Mathematics. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-30442-2_11.

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AbstractTraining deep neural networks consumes increasing computational resource shares in many compute centers. Often, a brute force approach to obtain hyperparameter values is employed. Our goal is (1) to enhance this by enabling second-order optimization methods with fewer hyperparameters for large-scale neural networks and (2) to compare optimizers for specific tasks to suggest users the best one for their problem. We introduce a novel second-order optimization method that requires the effect of the Hessian on a vector only and avoids the huge cost of explicitly setting up the Hessian for
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Kakes, P. "The genetics and ecology of variation in secondary plant substances." In Tasks for vegetation science. Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-009-0599-3_21.

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Blondel, Jacques, and Paula Cristina Dias. "Summergreenness, evergreenness and life history variation in Mediterranean Blue Tits." In Tasks for vegetation science. Springer Netherlands, 1994. http://dx.doi.org/10.1007/978-94-011-0908-6_3.

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Ahmad, S., M. Ashraf, and M. D. Khan. "Intra-specific variation for salt tolerance in cotton (Gossypium hirsutum L.)." In Tasks for vegetation science. Springer Netherlands, 2002. http://dx.doi.org/10.1007/978-94-017-0067-2_22.

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Ashraf, M. "Exploitation of genetic variation for improvement of salt tolerance in spring wheat." In Tasks for vegetation science. Springer Netherlands, 2002. http://dx.doi.org/10.1007/978-94-017-0067-2_11.

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Conference papers on the topic "Variational tasks"

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Lytvynenko, Volodtmyr, Serge Olszewski, Volodymyr Osypenko, Iryna Lurie, Violeta Demchenko, and Dariia Dontsova. "Comparative Analysis of the Use of Variational Autoencoders and Generative Adversarial Networks for Anomalous Image Detection Tasks." In 2024 IEEE 7th International Conference on Actual Problems of Unmanned Aerial Vehicles Development (APUAVD). IEEE, 2024. https://doi.org/10.1109/apuavd64488.2024.10765911.

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Hamilton, Kathleen E., Emily Lynn, Tyler Kharazi, Titus Morris, Ryan S. Bennink, and Raphael C. Pooser. "Building scalable variational circuit training for machine learning tasks." In 2021 58th ACM/IEEE Design Automation Conference (DAC). IEEE, 2021. http://dx.doi.org/10.1109/dac18074.2021.9586171.

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Bacsa, Kiran. "Wind turbine structure health monitoring through zero-shot learning with supervised variational autoencoders." In Structural Health Monitoring. Materials Research Forum LLC, 2025. https://doi.org/10.21741/9781644903513-23.

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Abstract. Real-world Structural Health Monitoring (SHM) applications can be classified to Zero-Shot Learning (ZSL) tasks, since structural data hardly ever reveal or label the full extent of damage that may be incurred to the system. This aligns with the definition of ZSL tasks, where part of the classes (or even all but one class) of the problem are hidden during the training time. Thus, the model must be able to generalize to classes that it has not been exposed to during the training. In SHM, this translates to a model of the studied structure generalizing the states/damages that are not pr
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Riera, Carlos Boned, and Oriol Ramos Terrades. "Discriminative Neural Variational Model for Unbalanced Classification Tasks in Knowledge Graph." In 2022 26th International Conference on Pattern Recognition (ICPR). IEEE, 2022. http://dx.doi.org/10.1109/icpr56361.2022.9956220.

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Chow, Yinlam, Brandon Cui, Moonkyung Ryu, and Mohammad Ghavamzadeh. "Variational Model-based Policy Optimization." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/316.

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Model-based reinforcement learning (RL) algorithms allow us to combine model-generated data with those collected from interaction with the real system in order to alleviate the data efficiency problem in RL. However, designing such algorithms is often challenging because the bias in simulated data may overshadow the ease of data generation. A potential solution to this challenge is to jointly learn and improve model and policy using a universal objective function. In this paper, we leverage the connection between RL and probabilistic inference, and formulate such an objective function as a var
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Balzer, J., and T. Morwald. "Isogeometric finite-elements methods and variational reconstruction tasks in vision — A perfect match." In 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2012. http://dx.doi.org/10.1109/cvpr.2012.6247855.

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Yousefi, Ali, Reza Kakooee, Mohammad TH Beheshti, et al. "Predicting learning dynamics in Multiple-Choice Decision-Making Tasks using a variational Bayes technique." In 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2017. http://dx.doi.org/10.1109/embc.2017.8037536.

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Lyu, Zhaoyan, Gholamali Aminian, and Miguel R. D. Rodrigues. "Toward Minimal-Sufficiency in Regression Tasks: An Approach Based on a Variational Estimation Bottleneck." In 2021 IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2021. http://dx.doi.org/10.1109/mlsp52302.2021.9596368.

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Toledo, Rafael S., and Eric A. Antonelo. "Face Reconstruction with Variational Autoencoder and Face Masks." In Encontro Nacional de Inteligência Artificial e Computacional. Sociedade Brasileira de Computação - SBC, 2021. http://dx.doi.org/10.5753/eniac.2021.18282.

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Variational AutoEncoders (VAE) employ deep learning models to learn a continuous latent z-space that is subjacent to a high-dimensional observed dataset. With that, many tasks are made possible, including face reconstruction and face synthesis. In this work, we investigated how face masks can help the training of VAEs for face reconstruction, by restricting the learning to the pixels selected by the face mask. An evaluation of the proposal using the celebA dataset shows that the reconstructed images are enhanced with the face masks, especially when SSIM loss is used either with l1 or l2 loss f
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Shen, Dazhong, Chuan Qin, Chao Wang, Hengshu Zhu, Enhong Chen, and Hui Xiong. "Regularizing Variational Autoencoder with Diversity and Uncertainty Awareness." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/408.

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As one of the most popular generative models, Variational Autoencoder (VAE) approximates the posterior of latent variables based on amortized variational inference. However, when the decoder network is sufficiently expressive, VAE may lead to posterior collapse; that is, uninformative latent representations may be learned. To this end, in this paper, we propose an alternative model, DU-VAE, for learning a more Diverse and less Uncertain latent space, and thus the representation can be learned in a meaningful and compact manner. Specifically, we first theoretically demonstrate that it will resu
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Reports on the topic "Variational tasks"

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Han. L51585 Effects of Seasonal Variations on Requirements to Prevent Corrosion in Soils. Pipeline Research Council International, Inc. (PRCI), 1988. http://dx.doi.org/10.55274/r0010092.

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It has been widely recognized that the pipe-to-soil potential of pipelines under cathodic protection varies seasonally, depending primarily on the soil moisture content. In dry seasons, for instance, the pipe-to-soil potential becomes less negative so that meeting the -0.85 V (Cu/CuS04) criterion can be difficult if not impossible. The American Gas Association (AGA) through the Pipeline Research Council International, Inc. (PRCI), accordingly, initiated a research program at Battelle Columbus Division to study the effects of soil moisture content on corrosion and cathodic protection of undergr
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Huang, Lei, Meng Song, Hui Shen, et al. Deep learning methods for omics data imputation. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/48221.

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One common problem in omics data analysis is missing values, which can arise due to various reasons, such as poor tissue quality and insufficient sample volumes. Instead of discarding missing values and related data, imputation approaches offer an alternative means of handling missing data. However, the imputation of missing omics data is a non-trivial task. Difficulties mainly come from high dimensionality, non-linear or nonmonotonic relationships within features, technical variations introduced by sampling methods, sample heterogeneity, and the non-random missingness mechanism. Several advan
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Bhan, Gautam, Divya Ravindranath, Antara Rai Chowdhury, et al. Reproducing a Household: Recognising and Assessing Paid and Unpaid Domestic Work in Urban India. Indian Institute for Human Settlements, 2022. http://dx.doi.org/10.24943/rhrapudwui11.2022.

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The key question of this study is to ask: what does it take to reproduce a household in urban India? Using a series of time-use surveys, we measure the time taken for 33 different tasks within activity clusters such as domestic services (cleaning, food preparation, procurement, upkeep) and caregiving services (child care and elderly care). Within this, we assess both unpaid work done by members of the household and paid work done by an externally engaged domestic worker. We do so across 9,636 households in two large metropolitan Indian cities– Bengaluru and Chennai – with variations across soc
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Balda, Taras, та Halyna Kondryn. Дисидентський рух в Україні крізь призму діаспорної публіцистики. Ivan Franko National University of Lviv, 2023. http://dx.doi.org/10.30970/vjo.2023.52-53.11718.

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The article’s subject – journalism of Ukrainian authors in diaspora publications on the topic of the dissident movement in soviet Ukraine. Trends of diaspora environment’s perception of variations and forms of Ukrainian intelligentsia and public figures’ resistance to totalitarian system in 1960-1980 are emphasized in the article. The diaspora journalists’ attitude to dissident movement’s tasks and perspectives in Ukraine are considered in the article, as well as disagreements in evaluation of perspectives of Ukrainian statehood’s restoration are stated. Two diaspora views’ polemics are emphas
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Dinarte, Lelys, Pablo Egaña del Sol, and Claudia Martínez. When Emotion Regulation Matters: The Efficacy of Socio-Emotional Learning to Address School-Based Violence in Central America. Inter-American Development Bank, 2024. http://dx.doi.org/10.18235/0012854.

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After-school programs (ASP) that keep youth protected while engaging them in socio-emotional learning might address school-based violent behaviors. This paper experimentally studies the socio-emotional-learning component of an ASP targeted to teenagers in public schools in the most violent neighborhoods of El Salvador, Honduras, and Guatemala. Participant schools were randomly assigned to different ASP variations, some of them including psychology-based interventions. Results indicate that including psychology-based activities as part of the ASP increases by 23 percentage points the probabilit
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Hedrick, Jacob, and Timothy Jacobs. PR-457-14201-R02 Variable NG Composition Effects in LB 2S Compressor Engines Phase I Engine Response. Pipeline Research Council International, Inc. (PRCI), 2015. http://dx.doi.org/10.55274/r0010997.

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This is the final report summarizing work completed during the first phase of the PRCI study on large bore two stroke lean burn integral compressor engine response to variable natural gas compositions for the purposes of engine control development. Accomplished tasks include the completion of a detailed literature review covering the research topic, a parametric study of laminar flame speeds and ignition delays for binary methane and ethane mixtures up to 30%, and a GT-Power engine model of the Colorado State GMV-4 research engine with associated validation data from CSU�s variable ethane effe
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Cohen, Deborah J., Annette M. Totten, Robert L. Phillips, Jr., et al. Measuring Primary Healthcare Spending. Agency for Healthcare Research and Quality (AHRQ), 2024. http://dx.doi.org/10.23970/ahrqepctb44.

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Background. Policy leaders and researchers have identified a range of primary care spending conceptualizations, developed frameworks and methods for measuring primary care spending, and documented the pros and cons of different approaches. However, these efforts have not been comprehensive, particularly as the number of estimates has grown. We continue this work by identifying the definitions, data sources, and approaches used to estimate primary care spending in the United States. Our objective was to identify where there is and is not consensus across methods, and how initial steps toward a
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8

Burks, Thomas F., Victor Alchanatis, and Warren Dixon. Enhancement of Sensing Technologies for Selective Tree Fruit Identification and Targeting in Robotic Harvesting Systems. United States Department of Agriculture, 2009. http://dx.doi.org/10.32747/2009.7591739.bard.

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The proposed project aims to enhance tree fruit identification and targeting for robotic harvesting through the selection of appropriate sensor technology, sensor fusion, and visual servo-control approaches. These technologies will be applicable for apple, orange and grapefruit harvest, although specific sensor wavelengths may vary. The primary challenges are fruit occlusion, light variability, peel color variation with maturity, range to target, and computational requirements of image processing algorithms. There are four major development tasks in original three-year proposed study. First, s
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Diggs-McGee, Brandy, Eric Kreiger, Megan Kreiger, and Michael Case. Print time vs. elapsed time : a temporal analysis of a continuous printing operation. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/41422.

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In additive construction, ambitious goals to fabricate a concrete building in less than 24 hours are attempted. In the field, this goal relies on a metric of print time to make this conclusion, which excludes rest time and delays. The task to complete a building in 24 hours was put to the test with the first attempt at a fully continuous print of a structurally reinforced additively constructed concrete (ACC) building. A time series analysis was performed during the construction of a 512 ft2 (16’x32’x9.25’) building to explore the effect of delays on the completion time. This analysis included
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Mbabzi, Kikundwa Emma. Standardisation of Staff Training to Increase Efficiency. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317427.

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In any industry or organization, personnel training is emphasized with reference to National Regulatory Authorities (NRAs) guidelines and other globally accepted guidelines. In spite of many refresher training programs, the pharmaceutical industry still faces significant variations in individual/ team efficiency and productivity. Individuals/teams given the same task, SOPs, environment and materials continue to produce significantly different results reflecting the possibility of operating on different sets of theoretical and practical information, which may stem from differing trainer, traini
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