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"

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 confirm our prediction and reveal that aggregation not only provides robustness and fault tolerance ability, but also produces more smooth learning curves and needs fewer learning steps than individual learning algorithms.

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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 rate of this technique plunges to 60% for the later/harder questions in the quiz show. To improve the success rates of these later/harder questions, we investigate novel weighted aggregation schemes for aggregating the answers obtained from the crowd. By using weights optimized for reliability of participants (derived from the participants’ confidence), we show that we can pull up the accuracy rate for the harder questions by 15%, and to overall 95% average accuracy. Our results provide a good case for the benefits of applying machine learning techniques for building more accurate crowdsourced question answering systems.

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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 levels of aggregation. In this research, the LSTM model is implemented into two traditional models that use aggregation techniques and are specifically used for intermittent data forecasting, namely ADIDA and MAPA. The result, based on tests on the six predetermined data, the LSTM model with aggregation and disaggregation is able to provide better test results than the LSTM model without aggregation and disaggregation.

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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 evidence theory to formulate the uncertainty of opinions as learning results from different data sources and measure the uncertainty of opinion aggregation as multi-view learning results through evidence accumulation. We prove that accumulating the evidences from multiple data views will decrease the uncertainty in multi-view deep learning and facilitate to achieve the trusted learning results. Experiments on various kinds of multi-view datasets verify the reliability and robustness of the proposed multi-view deep learning method.

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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 learning is converted to a supervised learning problem under the mutual supervision of the aggregated and predicted labels, and can be solved via alternate optimization to update labels and refine the classifiers. To alleviate the propagation of incorrect labels, small-loss metric is proposed to select reliable instances in both views. A co-teaching strategy with class-weighted loss is further leveraged in the deep classifier learning, which uses two networks with different learning abilities to teach each other, and the diverse errors introduced by noisy labels can be filtered out by peer networks. By these strategies, our approach can finally learn a robust data classifier which less overfits to label noise. Experimental results on synthetic and real data demonstrate the effectiveness and robustness of the proposed approach.

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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 solutions. We further investigate the specific challenges raised by federated learning and analyze the recent dedicated secure aggregation solutions based on cryptographic schemes. We finally share some takeaway messages that would help a secure design of federated learning and identify open research directions in this topic. Based on the takeaway messages, we propose an improved definition of secure aggregation that better fits federated learning.

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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 features. The proposed method has four key modules: multi-visual representation of a light field, feature extraction, feature aggregation, and quality assessment. It first extracts the natural scene statistics (NSS) features from the central view image in the spatial domain. It extracts gray-level co-occurrence matrix (GLCM) features both in the angular domain and in the spatial-angular coupled domain. Then, it extracts the rotation-invariant uniform local binary pattern (LBP) features of depth map in the depth domain, and the statistical characteristics of the local entropy (SDLE) features of refocused images in the projection domain. Finally, the multiple visual features are aggregated to form a visual feature vector for the light field. A prediction model is trained by support vector machines (SVM) to establish a light field quality assessment method based on aggregation learning of multiple visual features.

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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 occurs. Herein, we take a different approach to MKL. Specifically, we explore different divergence measures on the values in the kernel matrices and in the reproducing kernel Hilbert space (RKHS). Experiments on benchmark datasets and a computer vision feature learning task in explosive hazard detection demonstrate the effectiveness and generalizability of our proposed methods.

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Tesis sobre el tema "Multiple Aggregation Learning"

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 domain. Developed algorithms are applied to the problem of processing the large amounts of gamma ray spectroscopy data that can be produced in real-time by mobile radiation sensors. The thesis experimentally shows BA’s capability to boost sensor performance in detecting radiation sources of interest, even if the source is faint, partiallyoccluded, or enveloped in the noisy and variable radiation background characteristic of urban scenes. In addition, BA provides simultaneous inference of source parameters such as the source intensity or source type while detecting it. The thesis demonstrates this capability and also develops techniques to efficiently optimize these parameters over large possible setting spaces. Methods developed in this thesis are demonstrated both in simulation and in a radiation-sensing backpack that applies robotic localization techniques to enable indoor surveillance of radiation sources. The thesis further improves the BA algorithm’s capability to be robust under various detection scenarios. First, we augment BA with appropriate statistical models to improve estimation of signal components in low photon count detection, where the sensor may receive limited photon counts from either source and/or background. Second, we develop methods for online sensor reliability monitoring to create algorithms that are resilient to possible sensor faults in a data pipeline containing one or multiple sensors. Finally, we develop Retrospective BA, a variant of BA that allows reinterpretation of past sensor data in light of new information about percepts. These Retrospective capabilities include the use of Hidden Markov Models in BA to allow automatic correction of a sensor pipeline when sensor malfunction may be occur, an Anomaly- Match search strategy to efficiently optimize source hypotheses, and prototyping of a Multi-Modal Augmented PCA to more flexibly model background and nuisance source fluctuations in a dynamic environment.

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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 technology, forty years ago the branch of quantum computation was born. Quantum computation uses at its advantage the same quantum effects that could stop the progress of traditional computation and aim to deliver hardware and software capable of even greater computational power. In this context, this thesis presents the implementation of a quantum variational machine learning algorithm called quantum single-layer perceptron. We start by briefly explaining the foundation of quantum computing and machine learning, to later dive into the theoretical approach of the multiple aggregator quantum algorithms, and finally deliver a versatile implementation of the quantum counterparts of a single hidden layer perceptron. To conclude we train the model to perform binary classification using standard benchmark datasets, alongside three baseline quantum machine learning models taken from the literature. We then perform tests on both simulated quantum hardware and real devices to compare the performances of the various models.

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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 problème de l'attribution de catégories d'actions aux séquences vidéos. Cela peut être considéré comme un ingrédient clé pour construire la prochaine génération de systèmes visuels. Nous l'abordons avec des méthodes d'intelligence artificielle, sous le paradigme de l'apprentissage automatique et de l'apprentissage profond, notamment les réseaux de neurones convolutifs. Les réseaux de neurones convolutifs actuels sont de plus en plus profonds, plus gourmands en données et leur succès est donc tributaire de l'abondance de données d'entraînement étiquetées. Les réseaux de neurones convolutifs s'appuient également sur le pooling qui réduit la dimensionnalité des couches de sortie (et donc atténue leur sensibilité à la disponibilité de données étiquetées)<br>Nowadays, video contents are ubiquitous through the popular use of internet and smartphones, as well as social media. Many daily life applications such as video surveillance and video captioning, as well as scene understanding require sophisticated technologies to process video data. It becomes of crucial importance to develop automatic means to analyze and to interpret the large amount of available video data. In this thesis, we are interested in video action recognition, i.e. the problem of assigning action categories to sequences of videos. This can be seen as a key ingredient to build the next generation of vision systems. It is tackled with AI frameworks, mainly with ML and Deep ConvNets. Current ConvNets are increasingly deeper, data-hungrier and this makes their success tributary of the abundance of labeled training data. ConvNets also rely on (max or average) pooling which reduces dimensionality of output layers (and hence attenuates their sensitivity to the availability of labeled data); however, this process may dilute the information of upstream convolutional layers and thereby affect the discrimination power of the trained video representations, especially when the learned action categories are fine-grained

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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, a new multiple RL system - Aggregated Multiple Reinforcement Learning System (AMRLS) is developed. In AMRLS, each RL algorithm (learner) learns individually in a learning module and provides its output to an intelligent aggregation module. The aggregation module dynamically aggregates these outputs and provides a final decision. Then, all learners take the action and update their policies individually. The two processes are performed alternatively. AMRLS can deal with dynamic learning problems without the need to search for the optimal learning algorithm or the optimal values of learning parameters. It is claimed that several complementary learning algorithms can be integrated in AMRLS to improve the learning performance in terms of success rate, robustness, confidence, redundance, and complementariness. There are two strategies for learning an optimal policy with RL methods. One is based on Value Function Learning (VFL), which learns an optimal policy expressed as a value function. The Temporal Difference RL (TDRL) methods are examples of this strategy. The other is based on Direct Policy Search (DPS), which directly searches for the optimal policy in the potential policy space. The Genetic Algorithms (GAs)-based RL (GARL) are instances of this strategy. A hybrid learning architecture of GARL and TDRL, HGATDRL, is proposed to combine them together to improve the learning ability. AMRLS and HGATDRL are tested on several SDM problems, including the maze world problem, pursuit domain problem, cart-pole balancing system, mountain car problem, and flight control system. Experimental results show that the proposed framework and method can enhance the learning ability and improve learning performance of a multiple RL system.

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Capítulos de libros sobre el tema "Multiple Aggregation Learning"

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"

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 and half cheetah. Their results demonstrated that action ensemble can improve performance with respect to the grid search technique. This article also presents as contribution the comparison of the effectiveness of the aggregation techniques, the analysis considers the use of the separate or the combined policies during training. The latter presents better learning results when used with the data center aggregation strategy.

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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 aggregation in FL. By modelling the client selection as an extended multi-armed bandit (MAB) problem, we propose an adaptive client selection strategy to choose honest clients that are more likely to contribute high-quality updates. We then propose two approaches to identify malicious updates from sybil and non-sybil attacks, based on which rewards for each client selection decision can be accurately evaluated to discourage malicious behaviors. MAB-RFL achieves a satisfying balance between exploration and exploitation on the potential benign clients. Extensive experimental results show that MAB-RFL outperforms existing defenses in three attack scenarios under different percentages of attackers.

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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 residual relation module is explicitly proposed to capture the local spatiotemporal differences of relevant persons, which is further combined with the multi-graph relation networks. Moreover, a weighted aggregation strategy is devised to adaptively select multi-level spatiotemporal features from the appearance-level information to high level relations. Finally, our model is capable of extracting a comprehensive representation and inferring the group activity in an end-to-end manner. The experimental results on two popular benchmarks for group activity recognition clearly demonstrate the superior performance of our method in comparison with the state-of-the-art methods.

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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 distributed) or domain-shifted data distributions. The FL setup is implemented using the well-established Flower framework. The experiment involves injecting noise into 3D seismic data and subsequently employing various ML algorithms to eliminate this noise. All experiments were conducted using both IID and Non-IID data, employing both traditional and FL approaches, various tests considering different types of noise, noise factors, number of 2D seismic slices, diverse models, number of clients, and aggregations strategies. We tested different model aggregation strategies, such as FedAvg, FedProx, and Fedcyclic, alongside client selection strategies that consider model divergence, convergence trend similarity, and client weight analysis to improve the aggregation process. We also incorporated batch normalization into the network architecture to reduce data discrepancies among clients. The denoising process was evaluated using metrics like mean-square-error (MSE), signal-to-noise ratio (SNR), and peak signal-to-noise ratio (PSNR). A comparison between conventional methods and FL demonstrated that FL exhibited a reduced error rate, especially when dealing with larger datasets. Furthermore, FL harnessed the power of parallel computing, resulting in a notable 30% increase in processing speed, enhanced resource utilization, and a remarkable 99% reduction in communication costs. To sum it up, this study underscores the potential of FL in the context of seismic denoising, safeguarding data privacy, and enhancing overall performance. We addressed the associated challenges by experimenting with various approaches for client selection and aggregation within a privacy-preserving framework. Notably, among these aggregation strategies, FedCyclic stands out as it offers faster convergence, achieving performance levels comparable to FedAvg and FedProx with fewer training iterations.

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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. The former encodes local context by adaptively sampling a set of nodes to form a coarse-grained graph, while the latter decodes local context by recovering the coarsened graph back to its original size. Moreover, an orthogonal fusion module is proposed for the collaborative use of HAGR module, which integrates complementary local and global information into compact feature representations without redundancy. Extensive experiments on different visual tasks prove that our method significantly surpasses the state-of-the-arts. In particular, U-Match attains an AUC at 5 degree threshold of 60.53% on the challenging YFCC100M dataset without RANSAC, outperforming the strongest prior model by 8.61 absolute percentage points. Our code is publicly available at https://github.com/ZizhuoLi/U-Match.

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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 graph. 3DViewGraph first learns a novel latent semantic mapping to project low-level view features into meaningful latent semantic embeddings in a lower dimensional space, which is spanned by latent semantic patterns. Then, the content and spatial information of each pair of view nodes are encoded by a novel spatial pattern correlation, where the correlation is computed among latent semantic patterns. Finally, all spatial pattern correlations are integrated with attention weights learned by a novel attention mechanism. This further increases the discriminability of learned features by highlighting the unordered view nodes with distinctive characteristics and depressing the ones with appearance ambiguity. We show that 3DViewGraph outperforms state-of-the-art methods under three large-scale benchmarks.

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Informes sobre el tema "Multiple Aggregation Learning"

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 disease incidence separately from community vulnerability have conceptual overlap in their components and conflate the probability and severity of disease risk into a single component. The report identifies four methods used to develop risk maps, i) observational, ii) index-based, iii) associative modelling and iv) mechanistic modelling. Observational methods are limited by a lack of historical data sets and their assumption that historical outcomes are representative of current and future risks. The more general index-based methods offer a highly flexible approach based on observed and modelled risks and can be used for partially qualitative or difficult-to-measure indicators, such as socioeconomic vulnerability. For multidimensional risk measures, indices representing different dimensions can be aggregated to form a composite index or be considered jointly without aggregation. The latter approach can distinguish between different types of disease risk such as outbreaks of high frequency/low intensity and low frequency/high intensity. Associative models, including machine learning and artificial intelligence (AI), are commonly used to measure current risk, future risk (short-term for early warning systems) or risk in areas with low data availability, but concerns about bias, privacy, trust, and accountability in algorithms can limit their application. In addition, they typically do not account for gender and demographic variables that allow risk analyses for different vulnerable groups. As an alternative, mechanistic models can be used for similar purposes as well as to create spatial measures of disease transmission efficiency or to model risk outcomes from hypothetical scenarios. Mechanistic models, however, are limited by their inability to capture locally specific transmission dynamics. The report recommends that future WASH-related disease risk mapping research: - Conceptualise risk as a function of the probability and severity of a disease risk event. Probability and severity can be disaggregated into sub-components. For outbreak-prone diseases, probability can be represented by a likelihood component while severity can be disaggregated into transmission and sensitivity sub-components, where sensitivity represents factors affecting health and socioeconomic outcomes of infection. -Employ jointly considered unaggregated indices to map multidimensional risk. Individual indices representing multiple dimensions of risk should be developed using a range of methods to take advantage of their relative strengths. -Develop and apply collaborative approaches with public health officials, development organizations and relevant stakeholders to identify appropriate interventions and priority levels for different types of risk, while ensuring the needs and values of users are met in an ethical and socially responsible manner. -Enhance identification of vulnerable populations by further disaggregating risk estimates and accounting for demographic and behavioural variables and using novel data sources such as big data and citizen science. This review is the first to focus solely on WASH-related disease risk mapping and modelling. The recommendations can be used as a guide for developing spatial epidemiology models in tandem with public health officials and to help detect and develop tailored responses to WASH-related disease outbreaks that meet the needs of vulnerable populations. The report’s main target audience is modellers, public health authorities and partners responsible for co-designing and implementing multi-sectoral health interventions, with a particular emphasis on facilitating the integration of health and WASH services delivery contributing to Sustainable Development Goals (SDG) 3 (good health and well-being) and 6 (clean water and sanitation).

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