Bibliografías temáticas / Multiple Aggregation Learning

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Literatura académica sobre el tema "Multiple Aggregation Learning"

Autor: Grafiati
Publicado: 25 de mayo de 2024

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Artículos de revistas sobre el tema "Multiple Aggregation Learning"

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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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Aydin, Bahadir, Yavuz Selim Yilmaz Yavuz Selim Yilmaz, Yaliang Li, Qi Li, Jing Gao, and Murat Demirbas. "Crowdsourcing for Multiple-Choice Question Answering." Proceedings of the AAAI Conference on Artificial Intelligence 28, no. 2 (2014): 2946–53. http://dx.doi.org/10.1609/aaai.v28i2.19016.

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We leverage crowd wisdom for multiple-choice question answering, and employ lightweight machine learning techniques to improve the aggregation accuracy of crowdsourced answers to these questions. In order to develop more effective aggregation methods and evaluate them empirically, we developed and deployed a crowdsourced system for playing the “Who wants to be a millionaire?” quiz show. Analyzing our data (which consist of more than 200,000 answers), we find that by just going with the most selected answer in the aggregation, we can answer over 90% of the questions correctly, but the success r
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Sinnott, Jennifer A., and Tianxi Cai. "Pathway aggregation for survival prediction via multiple kernel learning." Statistics in Medicine 37, no. 16 (2018): 2501–15. http://dx.doi.org/10.1002/sim.7681.

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Azizi, Fityan, and Wahyu Catur Wibowo. "Intermittent Demand Forecasting Using LSTM With Single and Multiple Aggregation." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 6, no. 5 (2022): 855–59. http://dx.doi.org/10.29207/resti.v6i5.4435.

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Intermittent demand data is data with infrequent demand with varying number of demand sizes. Intermittent demand forecasting is useful for providing inventory control decisions. It is very important to produce accurate forecasts. Based on previous research, deep learning models, especially MLP and RNN-based architectures, have not been able to provide better intermittent data forecasting results compared to traditional methods. This research will focus on analyzing the results of intermittent data forecasting using deep learning with several levels of aggregation and a combination of several l
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Liu, Wei, Xiaodong Yue, Yufei Chen, and Thierry Denoeux. "Trusted Multi-View Deep Learning with Opinion Aggregation." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 7 (2022): 7585–93. http://dx.doi.org/10.1609/aaai.v36i7.20724.

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Multi-view deep learning is performed based on the deep fusion of data from multiple sources, i.e. data with multiple views. However, due to the property differences and inconsistency of data sources, the deep learning results based on the fusion of multi-view data may be uncertain and unreliable. It is required to reduce the uncertainty in data fusion and implement the trusted multi-view deep learning. Aiming at the problem, we revisit the multi-view learning from the perspective of opinion aggregation and thereby devise a trusted multi-view deep learning method. Within this method, we adopt
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Wang, Zhiqiang, Xinyue Yu, Haoyu Wang, and Peiyang Xue. "A federated learning scheme for hierarchical protection and multiple aggregation." Computers and Electrical Engineering 117 (July 2024): 109240. http://dx.doi.org/10.1016/j.compeleceng.2024.109240.

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Li, Shikun, Shiming Ge, Yingying Hua, et al. "Coupled-View Deep Classifier Learning from Multiple Noisy Annotators." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 4667–74. http://dx.doi.org/10.1609/aaai.v34i04.5898.

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Typically, learning a deep classifier from massive cleanly annotated instances is effective but impractical in many real-world scenarios. An alternative is collecting and aggregating multiple noisy annotations for each instance to train the classifier. Inspired by that, this paper proposes to learn deep classifier from multiple noisy annotators via a coupled-view learning approach, where the learning view from data is represented by deep neural networks for data classification and the learning view from labels is described by a Naive Bayes classifier for label aggregation. Such coupled-view le
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Mansouri, Mohamad, Melek Önen, Wafa Ben Jaballah, and Mauro Conti. "SoK: Secure Aggregation Based on Cryptographic Schemes for Federated Learning." Proceedings on Privacy Enhancing Technologies 2023, no. 1 (2023): 140–57. http://dx.doi.org/10.56553/popets-2023-0009.

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Secure aggregation consists of computing the sum of data collected from multiple sources without disclosing these individual inputs. Secure aggregation has been found useful for various applications ranging from electronic voting to smart grid measurements. Recently, federated learning emerged as a new collaborative machine learning technology to train machine learning models. In this work, we study the suitability of secure aggregation based on cryptographic schemes to federated learning. We first provide a formal definition of the problem and suggest a systematic categorization of existing s
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Liu, Chang, Zhuocheng Zou, Yuan Miao, and Jun Qiu. "Light field quality assessment based on aggregation learning of multiple visual features." Optics Express 30, no. 21 (2022): 38298. http://dx.doi.org/10.1364/oe.467754.

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Light field imaging is a way to represent human vision from a computational perspective. It contains more visual information than traditional imaging systems. As a basic problem of light field imaging, light field quality assessment has received extensive attention in recent years. In this study, we explore the characteristics of light field data for different visual domains (spatial, angular, coupled, projection, and depth), study the multiple visual features of a light field, and propose a non-reference light field quality assessment method based on aggregation learning of multiple visual fe
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Price, Stanton R., Derek T. Anderson, Timothy C. Havens, and Steven R. Price. "Kernel Matrix-Based Heuristic Multiple Kernel Learning." Mathematics 10, no. 12 (2022): 2026. http://dx.doi.org/10.3390/math10122026.

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Kernel theory is a demonstrated tool that has made its way into nearly all areas of machine learning. However, a serious limitation of kernel methods is knowing which kernel is needed in practice. Multiple kernel learning (MKL) is an attempt to learn a new tailored kernel through the aggregation of a set of valid known kernels. There are generally three approaches to MKL: fixed rules, heuristics, and optimization. Optimization is the most popular; however, a shortcoming of most optimization approaches is that they are tightly coupled with the underlying objective function and overfitting occur
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Tesis sobre el tema "Multiple Aggregation Learning"

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Cheung, Chi-Wai. "Probabilistic rank aggregation for multiple SVM ranking /." View abstract or full-text, 2009. http://library.ust.hk/cgi/db/thesis.pl?CSED%202009%20CHEUNG.

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Tandon, Prateek. "Bayesian Aggregation of Evidence for Detection and Characterization of Patterns in Multiple Noisy Observations." Research Showcase @ CMU, 2015. http://repository.cmu.edu/dissertations/658.

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Effective use of Machine Learning to support extracting maximal information from limited sensor data is one of the important research challenges in robotic sensing. This thesis develops techniques for detecting and characterizing patterns in noisy sensor data. Our Bayesian Aggregation (BA) algorithmic framework can leverage data fusion from multiple low Signal-To-Noise Ratio (SNR) sensor observations to boost the capability to detect and characterize the properties of a signal generating source or process of interest. We illustrate our research with application to the nuclear threat detection
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Orazi, Filippo. "Quantum machine learning: development and evaluation of the Multiple Aggregator Quantum Algorithm." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022. http://amslaurea.unibo.it/25062/.

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Human society has always been shaped by its technology, so much that even ages and parts of our history are often named after the discoveries of that time. The growth of modern society is largely derived from the introduction of classical computers that brought us innovations like repeated tasks automatization and long-distance communication. However, this explosive technological advancement could be subjected to a heavy stop when computers reach physical limitations and the empirical law known as Moore Law comes to an end. Foreshadowing these limits and hoping for an even more powerful techno
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Mazari, Ahmed. "Apprentissage profond pour la reconnaissance d’actions en vidéos." Electronic Thesis or Diss., Sorbonne université, 2020. http://www.theses.fr/2020SORUS171.

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De nos jours, les contenus vidéos sont omniprésents grâce à Internet et les smartphones, ainsi que les médias sociaux. De nombreuses applications de la vie quotidienne, telles que la vidéo surveillance et la description de contenus vidéos, ainsi que la compréhension de scènes visuelles, nécessitent des technologies sophistiquées pour traiter les données vidéos. Il devient nécessaire de développer des moyens automatiques pour analyser et interpréter la grande quantité de données vidéo disponibles. Dans cette thèse, nous nous intéressons à la reconnaissance d'actions dans les vidéos, c.a.d au pr
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Jiang, Ju. "A Framework for Aggregation of Multiple Reinforcement Learning Algorithms." Thesis, 2007. http://hdl.handle.net/10012/2752.

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Aggregation of multiple Reinforcement Learning (RL) algorithms is a new and effective technique to improve the quality of Sequential Decision Making (SDM). The quality of a SDM depends on long-term rewards rather than the instant rewards. RL methods are often adopted to deal with SDM problems. Although many RL algorithms have been developed, none is consistently better than the others. In addition, the parameters of RL algorithms significantly influence learning performances. There is no universal rule to guide the choice of algorithms and the setting of parameters. To handle this difficulty,
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Capítulos de libros sobre el tema "Multiple Aggregation Learning"

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Khan, Muhammad Irfan, Mojtaba Jafaritadi, Esa Alhoniemi, Elina Kontio, and Suleiman A. Khan. "Adaptive Weight Aggregation in Federated Learning for Brain Tumor Segmentation." In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09002-8_40.

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Mächler, Leon, Ivan Ezhov, Suprosanna Shit, and Johannes C. Paetzold. "FedPIDAvg: A PID Controller Inspired Aggregation Method for Federated Learning." In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44153-0_20.

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Khan, Muhammad Irfan, Mohammad Ayyaz Azeem, Esa Alhoniemi, Elina Kontio, Suleiman A. Khan, and Mojtaba Jafaritadi. "Regularized Weight Aggregation in Networked Federated Learning for Glioblastoma Segmentation." In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44153-0_12.

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Singh, Gaurav. "A Local Score Strategy for Weight Aggregation in Federated Learning." In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44153-0_13.

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Wang, Yuan, Renuga Kanagavelu, Qingsong Wei, Yechao Yang, and Yong Liu. "Model Aggregation for Federated Learning Considering Non-IID and Imbalanced Data Distribution." In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44153-0_19.

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Sandhofer, Catherine, and Christina Schonberg. "Multiple Examples Support Children’s Word Learning: The Roles of Aggregation, Decontextualization, and Memory Dynamics." In Language and Concept Acquisition from Infancy Through Childhood. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-35594-4_8.

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Chen, Yanjia, Ziwang Huang, Hejun Wu, and Hao Cai. "Melanoma Classification with IoT Devices from Local and Global Aggregation by Multiple Instance Learning." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0416-7_39.

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Gharahighehi, Alireza, Celine Vens, and Konstantinos Pliakos. "Multi-stakeholder News Recommendation Using Hypergraph Learning." In ECML PKDD 2020 Workshops. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-65965-3_36.

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AbstractRecommender systems are meant to fulfil user preferences. Nevertheless, there are multiple examples where users are not the only stakeholder in a recommendation platform. For instance, in news aggregator websites apart from readers, one can consider magazines (news agencies) or authors as other stakeholders. A multi-stakeholder recommender system generates a ranked list of items taking into account the preferences of multiple stakeholders. In this study, news recommendation is handled as a hypergraph ranking task, where relations between multiple types of objects and stakeholders are modeled in a unified hypergraph. The obtained results indicate that ranking on hypergraphs can be utilized as a natural multi-stakeholder recommender system that is able to adapt recommendations based on the importance of stakeholders.
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Alharbi, Ebtisaam, Leandro Soriano Marcolino, Antonios Gouglidis, and Qiang Ni. "Robust Federated Learning Method Against Data and Model Poisoning Attacks with Heterogeneous Data Distribution." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2023. http://dx.doi.org/10.3233/faia230257.

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Federated Learning (FL) is essential for building global models across distributed environments. However, it is significantly vulnerable to data and model poisoning attacks that can critically compromise the accuracy and reliability of the global model. These vulnerabilities become more pronounced in heterogeneous environments, where clients’ data distributions vary broadly, creating a challenging setting for maintaining model integrity. Furthermore, malicious attacks can exploit this heterogeneity, manipulating the learning process to degrade the model or even induce it to learn incorrect patterns. In response to these challenges, we introduce RFCL, a novel Robust Federated aggregation method that leverages CLustering and cosine similarity to select similar cluster models, effectively defending against data and model poisoning attacks even amidst high data heterogeneity. Our experiments assess RFCL’s performance against various attacker numbers and Non-IID degrees. The findings reveal that RFCL outperforms existing robust aggregation methods and demonstrates the capability to defend against multiple attack types.
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Khanh, Phan Truong, Tran Thi Hong Ngoc, and Sabyasachi Pramanik. "Engineering, Geology, Climate, and Socioeconomic Aspects' Implications on Machine Learning-Dependent Water Pipe Collapse Prediction." In Methodologies, Frameworks, and Applications of Machine Learning. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-1062-5.ch009.

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From the impact of several corporeal, mechanized, ecological, and civic conditions, underground water pipelines degrade. A motivated administrative approach of the water supply network (WSN) depends on accurate pipe failure prediction that is difficult for the traditional physics-dependent model to provide. The research used data-directed machine learning approaches to forecast water pipe breakdowns using the extensive water supply network's historical maintenance data history. To include multiple contributing aspects to subterranean pipe degradation, a multi-source data-aggregation system was originally developed. The framework specified the requirements for integrating several data sources, such as the classical pipe leakage dataset, the soil category dataset, the geographic dataset, the population count dataset, and the climatic dataset. Five machine learning (ML) techniques are created for predicting pipe failure depending on the data: LightGBM, ANN, logistic regression, K-NN, and SVM algorithm. The best performance was discovered to be achieved with LightGBM. Analysis was done on the relative weight of the primary contributing variables to the breakdowns of the water pipes. It's interesting to note that pipe failure probabilities are shown to be influenced by a community's socioeconomic variables. This research suggests that trustworthy decision-making in WSN management may be supported by data-directed analysis, which incorporates ML methods and the suggested data aggregation architecture.
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Actas de conferencias sobre el tema "Multiple Aggregation Learning"

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Jiang, Zoe L., Hui Guo, Yijian Pan, Yang Liu, Xuan Wang, and Jun Zhang. "Secure Neural Network in Federated Learning with Model Aggregation under Multiple Keys." In 2021 8th IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)/2021 7th IEEE International Conference on Edge Computing and Scalable Cloud (EdgeCom). IEEE, 2021. http://dx.doi.org/10.1109/cscloud-edgecom52276.2021.00019.

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Yoshida, Takeshi, Kazuki Uehara, Hidenori Sakanashi, Hirokazu Nosato, and Masahiro Murakawa. "Multi-Scale Feature Aggregation Based Multiple Instance Learning for Pathological Image Classification." In 12th International Conference on Pattern Recognition Applications and Methods. SCITEPRESS - Science and Technology Publications, 2023. http://dx.doi.org/10.5220/0011615200003411.

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Wang, Qianru, Qingyang Li, Bin Guo, and Jiangtao Cui. "Efficient Federated Learning with Smooth Aggregation for Non-IID Data from Multiple Edges." In ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2024. http://dx.doi.org/10.1109/icassp48485.2024.10447506.

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Zhang, Jianxin, Cunqiao Hou, Wen Zhu, et al. "Attention multiple instance learning with Transformer aggregation for breast cancer whole slide image classification." In 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2022. http://dx.doi.org/10.1109/bibm55620.2022.9994848.

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Garcia Oliveira, Renata, and Wouter Caarls. "Comparing Action Aggregation Strategies in Deep Reinforcement Learning with Continuous Action." In Congresso Brasileiro de Automática - 2020. sbabra, 2020. http://dx.doi.org/10.48011/asba.v2i1.1547.

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Deep Reinforcement Learning has been very promising in learning continuous control policies. For complex tasks, Reinforcement Learning with minimal human intervention is still a challenge. This article proposes a study to improve performance and to stabilize the learning curve using the ensemble learning methods. Learning a combined parameterized action function using multiple agents in a single environment, while searching for a better way to learn, regardless of the quality of the parametrization. The action ensemble methods were applied in three environments: pendulum swing-up, cart pole an
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Wan, Wei, Shengshan Hu, jianrong Lu, Leo Yu Zhang, Hai Jin, and Yuanyuan He. "Shielding Federated Learning: Robust Aggregation with Adaptive Client Selection." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/106.

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Federated learning (FL) enables multiple clients to collaboratively train an accurate global model while protecting clients' data privacy. However, FL is susceptible to Byzantine attacks from malicious participants. Although the problem has gained significant attention, existing defenses have several flaws: the server irrationally chooses malicious clients for aggregation even after they have been detected in previous rounds; the defenses perform ineffectively against sybil attacks or in the heterogeneous data setting. To overcome these issues, we propose MAB-RFL, a new method for robust aggre
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Li, Wei, Tianzhao Yang, Xiao Wu, and Zhaoquan Yuan. "Learning Graph-based Residual Aggregation Network for Group Activity Recognition." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/154.

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Group activity recognition aims to understand the overall behavior performed by a group of people. Recently, some graph-based methods have made progress by learning the relation graphs among multiple persons. However, the differences between an individual and others play an important role in identifying confusable group activities, which have not been elaborately explored by previous methods. In this paper, a novel Graph-based Residual AggregatIon Network (GRAIN) is proposed to model the differences among all persons of the whole group, which is end-to-end trainable. Specifically, a new local
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Guruprasad, Kamalesh Kumar Mandakolathur, Gayatri Sunil Ambulkar, and Geetha Nair. "Federated Learning for Seismic Data Denoising: Privacy-Preserving Paradigm." In International Petroleum Technology Conference. IPTC, 2024. http://dx.doi.org/10.2523/iptc-23888-ms.

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Summary Federated Learning (FL) is a framework that empowers multiple clients to develop robust machine learning (ML) algorithms while safeguarding data privacy and security. This paper's primary goal is to investigate the capability of the FL framework in preserving privacy and to assess its efficacy for clients operating within the oil and gas industry. To demonstrate the practicality of this framework, we apply it to seismic denoising use cases incorporating data from clients with IID (independent & and identically distributed) and Non-IID (non-independent and non-identically distribute
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Li, Zizhuo, Shihua Zhang, and Jiayi Ma. "U-Match: Two-view Correspondence Learning with Hierarchy-aware Local Context Aggregation." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/130.

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Local context capturing has become the core factor for achieving leading performance in two-view correspondence learning. Recent advances have devised various local context extractors whereas typically adopting explicit neighborhood relation modeling that is restricted and inflexible. To address this issue, we introduce U-Match, an attentional graph neural network that has the flexibility to enable implicit local context awareness at multiple levels. Specifically, a hierarchy-aware graph representation (HAGR) module is designed and fleshed out by local context pooling and unpooling operations.
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Han, Zhizhong, Xiyang Wang, Chi Man Vong, Yu-Shen Liu, Matthias Zwicker, and C. L. Philip Chen. "3DViewGraph: Learning Global Features for 3D Shapes from A Graph of Unordered Views with Attention." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/107.

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Learning global features by aggregating information over multiple views has been shown to be effective for 3D shape analysis. For view aggregation in deep learning models, pooling has been applied extensively. However, pooling leads to a loss of the content within views, and the spatial relationship among views, which limits the discriminability of learned features. We propose 3DViewGraph to resolve this issue, which learns 3D global features by more effectively aggregating unordered views with attention. Specifically, unordered views taken around a shape are regarded as view nodes on a view g
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Informes sobre el tema "Multiple Aggregation Learning"

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Daudelin, Francois, Lina Taing, Lucy Chen, Claudia Abreu Lopes, Adeniyi Francis Fagbamigbe, and Hamid Mehmood. Mapping WASH-related disease risk: A review of risk concepts and methods. United Nations University Institute for Water, Environment and Health, 2021. http://dx.doi.org/10.53328/uxuo4751.

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The report provides a review of how risk is conceived of, modelled, and mapped in studies of infectious water, sanitation, and hygiene (WASH) related diseases. It focuses on spatial epidemiology of cholera, malaria and dengue to offer recommendations for the field of WASH-related disease risk mapping. The report notes a lack of consensus on the definition of disease risk in the literature, which limits the interpretability of the resulting analyses and could affect the quality of the design and direction of public health interventions. In addition, existing risk frameworks that consider diseas
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