Relevant bibliographies by topics / Aggregated Learning

Contents

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

Academic literature on the topic 'Aggregated Learning'

Author: Grafiati
Published: 4 June 2021
Last updated: 15 February 2022

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Journal articles on the topic "Aggregated Learning"

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Mourtada, Jaouad, Stéphane Gaïffas, and Erwan Scornet. "AMF: Aggregated Mondrian forests for online learning." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 83, no. 3 (2021): 505–33. http://dx.doi.org/10.1111/rssb.12425.

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JIANG, JU, MOHAMED S. KAMEL, and LEI CHEN. "AGGREGATION OF MULTIPLE REINFORCEMENT LEARNING ALGORITHMS." International Journal on Artificial Intelligence Tools 15, no. 05 (2006): 855–61. http://dx.doi.org/10.1142/s0218213006002990.

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Reinforcement learning (RL) has been successfully used in many fields. With the increasing complexity of environments and tasks, it is difficult for a single learning algorithm to cope with complicated problems with high performance. This paper proposes a new multiple learning architecture, "Aggregated Multiple Reinforcement Learning System (AMRLS)", which aggregates different RL algorithms in each learning step to make more appropriate sequential decisions than those made by individual learning algorithms. This architecture was tested on a Cart-Pole system. The presented simulation results co
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Soflaei, Masoumeh, Hongyu Guo, Ali Al-Bashabsheh, Yongyi Mao, and Richong Zhang. "Aggregated Learning: A Vector-Quantization Approach to Learning Neural Network Classifiers." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 5810–17. http://dx.doi.org/10.1609/aaai.v34i04.6038.

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We consider the problem of learning a neural network classifier. Under the information bottleneck (IB) principle, we associate with this classification problem a representation learning problem, which we call “IB learning”. We show that IB learning is, in fact, equivalent to a special class of the quantization problem. The classical results in rate-distortion theory then suggest that IB learning can benefit from a “vector quantization” approach, namely, simultaneously learning the representations of multiple input objects. Such an approach assisted with some variational techniques, result in a
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Langone, Rocco, and Johan A. K. Suykens. "Supervised aggregated feature learning for multiple instance classification." Information Sciences 375 (January 2017): 234–45. http://dx.doi.org/10.1016/j.ins.2016.09.060.

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CHIANG, JUNG-HSIEN. "LEARNING DECISION FUNCTIONS IN THE FUZZY γ-MODELS". International Journal on Artificial Intelligence Tools 09, № 04 (2000): 459–71. http://dx.doi.org/10.1142/s021821300000029x.

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In this approach, we investigate the fuzzy γ-models for decision analysis and making. This methodology utilizes fuzzy γ-model as an information aggregation operator. It provides several advantages due to the fact that the input to each model is the evidence supplied by the degree of satisfaction of sub-criteria and the output is the aggregated evidence. We also generalize fuzzy γ-models as a hierarchical network in this work. Thus, the decision making process is to aggregate and propagate the evidence information through such a hierarchical network. This trainable network is able to perceive a
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LEVIN, N., and S. GLOBERSON. "Generating learning curves for individual products from aggregated data." International Journal of Production Research 31, no. 12 (1993): 2807–15. http://dx.doi.org/10.1080/00207549308956901.

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Jung, Hyungjoo, Youngjung Kim, Dongbo Min, Hyunsung Jang, Namkoo Ha, and Kwanghoon Sohn. "Learning Deeply Aggregated Alternating Minimization for General Inverse Problems." IEEE Transactions on Image Processing 29 (2020): 8012–27. http://dx.doi.org/10.1109/tip.2020.3010082.

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Wang, Jianhua, Chuanxia Zheng, Weihai Chen, and Xingming Wu. "Learning aggregated features and optimizing model for semantic labeling." Visual Computer 33, no. 12 (2016): 1587–600. http://dx.doi.org/10.1007/s00371-016-1302-4.

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Zhang, Chao, Jiahao Xie, Zebang Shen, Peilin Zhao, Tengfei Zhou, and Hui Qian. "Aggregated Gradient Langevin Dynamics." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 6746–53. http://dx.doi.org/10.1609/aaai.v34i04.6153.

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In this paper, we explore a general Aggregated Gradient Langevin Dynamics framework (AGLD) for the Markov Chain Monte Carlo (MCMC) sampling. We investigate the nonasymptotic convergence of AGLD with a unified analysis for different data accessing (e.g. random access, cyclic access and random reshuffle) and snapshot updating strategies, under convex and nonconvex settings respectively. It is the first time that bounds for I/O friendly strategies such as cyclic access and random reshuffle have been established in the MCMC literature. The theoretic results also indicate that methods in AGLD posse
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Succar, Bilal, and Willy Sher. "A Competency Knowledge-Base for BIM Learning." Australasian Journal of Construction Economics and Building - Conference Series 2, no. 2 (2014): 1. http://dx.doi.org/10.5130/ajceb-cs.v2i2.3883.

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Building Information Modelling (BIM) tools and workflows continue to proliferate within the Design, Construction and Operation (DCO) industry. To equip current and future industry professionals with the necessary knowledge and skills to engage in collaborative workflows and integrated project deliverables, it is important to identify the competencies that need to be taught at educational institutions or trained on the job. Expanding upon a collaborative BIM education framework pertaining to a national BIM initiative in Australia, this paper introduces a conceptual workflow to identify, classif
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Dissertations / Theses on the topic "Aggregated Learning"

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Soflaei, Shahrbabak Masoumeh. "Aggregated Learning: An Information Theoretic Framework to Learning with Neural Networks." Thesis, Université d'Ottawa / University of Ottawa, 2020. http://hdl.handle.net/10393/41399.

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Deep learning techniques have achieved profound success in many challenging real-world applications, including image recognition, speech recognition, and machine translation. This success has increased the demand for developing deep neural networks and more effective learning approaches. The aim of this thesis is to consider the problem of learning a neural network classifier and to propose a novel approach to solve this problem under the Information Bottleneck (IB) principle. Based on the IB principle, we associate with the classification problem a representation learning problem, which we
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Edwards, Adam Michael. "Precision Aggregated Local Models." Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/102125.

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Large scale Gaussian process (GP) regression is infeasible for larger data sets due to cubic scaling of flops and quadratic storage involved in working with covariance matrices. Remedies in recent literature focus on divide-and-conquer, e.g., partitioning into sub-problems and inducing functional (and thus computational) independence. Such approximations can speedy, accurate, and sometimes even more flexible than an ordinary GPs. However, a big downside is loss of continuity at partition boundaries. Modern methods like local approximate GPs (LAGPs) imply effectively infinite partitioning and
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Molitor, Torsten. "Coverage Prediction for Inter-Frequency Handover using Machine Learning with Aggregated Training Data." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-286676.

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An important application of Machine Learning (ML) in mobile networks is to predict if a user device has coverage on a frequency other than the current serving frequency. This is a use-case called Secondary Carrier Prediction (SCP). In this thesis we deduce if data across different cells and frequencies can be successfully combined when learning this task, thus reducing the number of models that require training. Aggregation of data involves several challenges, such as different prevalences and varying amounts of available data, but more importantly the possibility of achieving synergies in tra
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Guse, Eran A. "Essays on heterogeneity, learning dynamics, and aggregate fluctuations /." view abstract or download file of text, 2003. http://wwwlib.umi.com/cr/uoregon/fullcit?p3095249.

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Thesis (Ph. D.)--University of Oregon, 2003.<br>Typescript. Includes vita and abstract. Includes bibliographical references (leaves 139-142). Also available for download via the World Wide Web; free to University of Oregon users.
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Zhang, Shuai. "Learning from semantically heterogeneous aggregate data in a distributed environment." Thesis, University of Ulster, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.551564.

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Information cooperation, reuse and integration can be developed on the platform of rapidly growing open distributed environments and can support development of Ambient Intelligence. However, in such environments, information may be only partially observed due to the unreliability of data collection technologies and heterogeneity in the ontologies employed caused by distributed and independent system development. These challenges need to be overcome to facilitate intelligent data analysis. We focus on the use of large-scale databases such as statistical databases and data warehouses, where aggr
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Charnay, Clément. "Enhancing supervised learning with complex aggregate features and context sensitivity." Thesis, Strasbourg, 2016. http://www.theses.fr/2016STRAD025/document.

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Dans cette thèse, nous étudions l'adaptation de modèles en apprentissage supervisé. Nous adaptons des algorithmes d'apprentissage existants à une représentation relationnelle. Puis, nous adaptons des modèles de prédiction aux changements de contexte.En représentation relationnelle, les données sont modélisées par plusieurs entités liées par des relations. Nous tirons parti de ces relations avec des agrégats complexes. Nous proposons des heuristiques d'optimisation stochastique pour inclure des agrégats complexes dans des arbres de décisions relationnels et des forêts, et les évaluons sur des j
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Volonnino, Chiara. "A Reinforcement Learning approach to discriminate unsafe devices in aggregate computing systems." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/20488/.

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Reinforcement learning is a machine learning approach that has been studied for many years, but particularly nowadays the interest about this topic has exponentially grown. Its purpose is to create autonomous agents able to sense and act in their environment. They should learn to choose optimal actions to achieve their goals, in order to maximise a cumulative reward. Aggregate programming is a paradigm that supports the large-scale programming of adaptive systems by focusing on the behaviour of the cluster instead of the singles. One promising aggregate programming approach is based on the fi
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Barclay, McKeown Stephanie. "Assessing the multilevel validity of program-level inferences based on aggregate student perceptions about their general learning." Thesis, University of British Columbia, 2014. http://hdl.handle.net/2429/50737.

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Aggregate survey results are commonly used by universities in Canada to compare effective educational practices across program majors within a university and between equivalent majors across campuses. Despite this recurrent practice, many researchers neglect to examine the multilevel validity of inferences made from program-level responses. This study illustrates the importance of determining the multilevel validity of program-level inferences prior to making conclusions based on survey data. Survey responses regarding student perceptions about their general learning outcomes and rati
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White, Douglas. "Analyzing multicellular interactions: A hybrid computational and biological pattern recognition approach." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/54876.

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Pluripotent embryonic stem cells (ESCs) can differentiate into all somatic cell types, making them a useful platform for studying a variety of cellular phenomenon. Furthermore, ESCs can be induced to form aggregates called embryoid bodies (EBs) which recapitulate the dynamics of development and morphogenesis. However, many different factors such as gradients of soluble morphogens, direct cell-to-cell signaling, and cell-matrix interactions have all been implicated in directing ESC differentiation. Though the effects of individual factors have often been investigated independently, the inherent
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Bezerra, Agnes. "The Use of Artificial Intelligence for Assessing Damage in Concrete Affected by Alkali-Silica Reaction (ASR)." Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/42730.

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Over the last decades, numerous techniques have been proposed worldwide to assess the actual damage of critical concrete infrastructure. A method that has progressively been used in North America is a novel microscopic tool, the Damage Rating Index (DRI). This semi-quantitative petrographic tool was developed to reliably appraise both the nature and degree of damage in concrete affected by alkali-silica reaction (ASR), which may threaten the serviceability and the durability of concrete infrastructure around the world. Performing the DRI consists of counting numerous distress features (i.e. cl
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Books on the topic "Aggregated Learning"

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W, Cooper Russell. Learning by doing and aggregate fluctuations. National Bureau of Economic Research, 1999.

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Tremblay, Pierre. Body switching and related matters: Script elaborations and aggregate crime learning curves. Université de Montréal, 1999.

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Hernandez, Rebecca Skreslet. Authority by Aggregation and Abstraction. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198805939.003.0005.

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In addition to his views on ijtihād and tajdīd, al-Suyūṭī’s lasting influence in Islamic legal thought lies in the area of legal precepts (pithy maxims or questions that sum up areas of the law). Al-Suyūṭī’s al-Ashbāh wa-l-naẓāʾir stands as a core work in this genre of legal literature and is still a popular textbook for students at Egypt’s premier institution of religious learning, al-Azhar. Using the pragmatic theory of Grice and others, I argue that legal precepts fulfill a number of key discursive functions for the jurist. It is with al-Suyūṭī’s Ashbāh that he is most successful in asserti
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Souza, Aparecida Carina Alves de, Mônica Alves de Matos Pereira, Célia Maria Adão de Oliveira Aguiar de Sousa, et al. Caminhos Possíveis para Incluir: Educação, cultura, esporte e lazer. Brazil Publishing, 2021. http://dx.doi.org/10.31012/978-65-5861-390-9.

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This collection of articles aims to weave a critical reflection on education, sport, culture, and its inclusive principles while respecting social rights and presenting inclusion as the main point. It brings forth the perspective of education against barbarism in the search for human emancipation in counterpoint to a society that kills and trivializes exclusion. It also aims to examine a patchwork of the times in which we have been living, when the tendency of capitalist society is to equal everyone, making it impossible to legitimize social rights. From this perspective, our intention was to
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Book chapters on the topic "Aggregated Learning"

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Wojtusiak, Janusz, and Ancha Baranova. "Model Learning from Published Aggregated Data." In Learning Structure and Schemas from Documents. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22913-8_17.

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Zhao, Qi, Bo Zhang, Anli Yan, Shan Jing, and Chuan Zhao. "Towards Privacy-Preserving Aggregated Prediction from SPDZ." In Machine Learning for Cyber Security. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62223-7_22.

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Pham, Thang V., Marcel Worring, and Arnold W. M. Smeulders. "Face Detection by Aggregated Bayesian Network Classifiers." In Machine Learning and Data Mining in Pattern Recognition. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44596-x_21.

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Nicosia, Massimo, Gianni Barlacchi, and Alessandro Moschitti. "Learning to Rank Aggregated Answers for Crossword Puzzles." In Lecture Notes in Computer Science. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16354-3_61.

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Bhowmik, Avradeep, Minmin Chen, Zhengming Xing, and Suju Rajan. "EstImAgg: A Learning Framework for Groupwise Aggregated Data." In Proceedings of the 2019 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, 2019. http://dx.doi.org/10.1137/1.9781611975673.54.

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Grootendorst, Maarten, and Joaquin Vanschoren. "Beyond Bag-of-Concepts: Vectors of Locally Aggregated Concepts." In Machine Learning and Knowledge Discovery in Databases. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-46147-8_41.

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Zhang, Han, and Albert C. S. Chung. "Lesion Detection with Deep Aggregated 3D Contextual Feature and Auxiliary Information." In Machine Learning in Medical Imaging. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32692-0_6.

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Schirmer, Pascal A., and Iosif Mporas. "Binary versus Multiclass Deep Learning Modelling in Energy Disaggregation." In Springer Proceedings in Energy. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-63916-7_6.

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AbstractThis paper compares two different deep-learning architectures for the use in energy disaggregation and Non-Intrusive Load Monitoring. Non-Intrusive Load Monitoring breaks down the aggregated energy consumption into individual appliance consumptions, thus detecting device operation. In detail, the “One versus All” approach, where one deep neural network per appliance is trained, and the “Multi-Output” approach, where the number of output nodes is equal to the number of appliances, are compared to each other. Evaluation is done on a state-of-the-art baseline system using standard performance measures and a set of publicly available datasets out of the REDD database.
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Xiao, Gaoyu, and Anant Madabhushi. "Aggregated Distance Metric Learning (ADM) for Image Classification in Presence of Limited Training Data." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23626-6_5.

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Fujita, Mai. "Learning Models in Japanese and Vietnamese–Chinese Chains up to the Early 2000s: An Aggregated Analysis of Learning Events." In SpringerBriefs in Economics. Springer Japan, 2013. http://dx.doi.org/10.1007/978-4-431-54770-9_7.

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Conference papers on the topic "Aggregated Learning"

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Yu, Yao-Chi, Vignesh Narayanan, ShiNung Ching, and Jr-Shin Li. "Learning to Control Neurons using Aggregated Measurements." In 2020 American Control Conference (ACC). IEEE, 2020. http://dx.doi.org/10.23919/acc45564.2020.9147426.

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Csaji, Balazs Csanad, Andras Kovacs, and Jozsef Vancza. "Adaptive aggregated predictions for renewable energy systems." In 2014 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL). IEEE, 2014. http://dx.doi.org/10.1109/adprl.2014.7010625.

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Lazaris, Aggelos, and Viktor K. Prasanna. "Deep Learning Models For Aggregated Network Traffic Prediction." In 2019 15th International Conference on Network and Service Management (CNSM). IEEE, 2019. http://dx.doi.org/10.23919/cnsm46954.2019.9012669.

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Nie, Feng, Shuyan Zhou, Jing Liu, Jinpeng Wang, Chin-Yew Lin, and Rong Pan. "Aggregated Semantic Matching for Short Text Entity Linking." In Proceedings of the 22nd Conference on Computational Natural Language Learning. Association for Computational Linguistics, 2018. http://dx.doi.org/10.18653/v1/k18-1046.

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Paterakis, Nikolaos G., Elena Mocanu, Madeleine Gibescu, Bart Stappers, and Walter van Alst. "Deep learning versus traditional machine learning methods for aggregated energy demand prediction." In 2017 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe). IEEE, 2017. http://dx.doi.org/10.1109/isgteurope.2017.8260289.

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Huang, Xinting, Jianzhong Qi, Yu Sun, Rui Zhang, and Hai-Tao Zheng. "CARL: Aggregated Search with Context-Aware Module Embedding Learning." In 2019 International Joint Conference on Neural Networks (IJCNN). IEEE, 2019. http://dx.doi.org/10.1109/ijcnn.2019.8851716.

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Luo, Zhengxion, Zhicheng Wang, Yuanhao Cai, et al. "Efficient Human Pose Estimation by Learning Deeply Aggregated Representations." In 2021 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2021. http://dx.doi.org/10.1109/icme51207.2021.9428206.

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Niu, Yue, and Hongjie Zhang. "A Self-Aggregated Hierarchical Topic Model for Short Texts." In 2nd International Conference on Machine Learning, IOT and Blockchain (MLIOB 2021). Academy and Industry Research Collaboration Center (AIRCC), 2021. http://dx.doi.org/10.5121/csit.2021.111212.

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With the growth of the internet, short texts such as tweets from Twitter, news titles from the RSS, or comments from Amazon have become very prevalent. Many tasks need to retrieve information hidden from the content of short texts. So ontology learning methods are proposed for retrieving structured information. Topic hierarchy is a typical ontology that consists of concepts and taxonomy relations between concepts. Current hierarchical topic models are not specially designed for short texts. These methods use word co-occurrence to construct concepts and general-special word relations to constru
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Najafabadi, Maryam M., Taghi M. Khoshgoftaar, Chad Calvert, and Clifford Kemp. "Detection of SSH Brute Force Attacks Using Aggregated Netflow Data." In 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA). IEEE, 2015. http://dx.doi.org/10.1109/icmla.2015.20.

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Jiang, Ju, and Mohamed S. Kamel. "Pitch Control of an Aircraft with Aggregated Reinforcement Learning Algorithms." In 2007 International Joint Conference on Neural Networks. IEEE, 2007. http://dx.doi.org/10.1109/ijcnn.2007.4370928.

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Reports on the topic "Aggregated Learning"

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Kaffenberger, Michelle, and Lant Pritchett. Women’s Education May Be Even Better Than We Thought: Estimating the Gains from Education When Schooling Ain’t Learning. Research on Improving Systems of Education (RISE), 2020. http://dx.doi.org/10.35489/bsg-rise-wp_2020/049.

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Women’s schooling has long been regarded as one of the best investments in development. Using two different cross-nationally comparable data sets which both contain measures of schooling, assessments of literacy, and life outcomes for more than 50 countries, we show the association of women’s education (defined as schooling and the acquisition of literacy) with four life outcomes (fertility, child mortality, empowerment, and financial practices) is much larger than the standard estimates of the gains from schooling alone. First, estimates of the association of outcomes with schooling alone can
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Cooper, Russell, and Alok Johri. Learning by Doing and Aggregate Fluctuations. National Bureau of Economic Research, 1999. http://dx.doi.org/10.3386/w6898.

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Roschelle, Jeremy, Britte Haugan Cheng, Nicola Hodkowski, Julie Neisler, and Lina Haldar. Evaluation of an Online Tutoring Program in Elementary Mathematics. Digital Promise, 2020. http://dx.doi.org/10.51388/20.500.12265/94.

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Many students struggle with mathematics in late elementary school, particularly on the topic of fractions. In a best evidence syntheses of research on increasing achievement in elementary school mathematics, Pelligrini et al. (2018) highlighted tutoring as a way to help students. Online tutoring is attractive because costs may be lower and logistics easier than with face-to-face tutoring. Cignition developed an approach that combines online 1:1 tutoring with a fractions game, called FogStone Isle. The game provides students with additional learning opportunities and provides tutors with inform
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