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检索条件"主题词=Expectation maximization algorithm"

共 648 条记录，以下是421-430 订阅

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An expectation-Realization Model for Metaphor Detection 4

An Expectation-Realization Model for Metaphor Detection

引用

4th Workshop on Figurative Language Processing, FigLang 2024

作者： Uduehi, Oseremen O. Bunescu, Razvan C. School of EECS Ohio University AthensOH45701 United States Department of Computer Science University of North Carolina at Charlotte CharlotteNC28223 United States

ISBN: (纸本)9798891761100

We propose a new model for metaphor detection in which an expectation component estimates representations of expected word meanings in a given context, whereas a realization component computes representations of target word meanings in context. We also introduce a systematic evaluation methodology that estimates generalization performance in three settings: within distribution, a new strong out of distribution setting, and a novel out-of-pretraining setting. Across all settings, the expectation-realization model obtains results that are competitive with or better than previous metaphor detection models. © 2024 Association for Computational Linguistics.

关键词： expectation maximization algorithm

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Learning Input Driven Dynamic Bayesian Networks with Measurement Noise 23

Learning Input Driven Dynamic Bayesian Networks with Measure...

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23rd IEEE International Conference on Machine Learning and Applications, ICMLA 2024

作者： Veres, David Li, Ping Kadirkamanathan, Visakan The University of Sheffield Department of Automatic Control and Systems Engineering Sheffield United Kingdom

ISBN: (纸本)9798350374889

Dynamic Bayesian Networks (DBNs) are useful tools for modelling complex systems whose network representations can be elicited a priori or learnt from data. In this paper, a maximum likelihood Doubly-Iterative expectation maximization (DI-EM) algorithm is developed for the identification of grey-box ARMAX state-space model representations of DBNs involving known, noisy measurement processes. The grey-box model incorporates network dependencies among time series variables and exploits time series data of low longitudinal and high cross-sectional dimensions. A network learning procedure is developed using a score-based structure-selection method to find the underlying network of an input-driven dynamical system. By computing a finite data version of the Bayesian Information Criterion (BIC) for small sample sizes, the proposed method's performance is investigated on simulated and real-world data. The algorithm recovers the underlying ground-truth networks of simulated systems under finite data criteria with Jaccard Coefficient values of up to 0.84, and selects structures with improved weighted mean-squared error loss over a baseline black-box model fit on real-world data. © 2024 IEEE.

关键词： expectation maximization algorithm

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The Forward-Reverse Greedy algorithm for Distributed Submodular maximization 63

The Forward-Reverse Greedy Algorithm for Distributed Submodu...

引用

63rd IEEE Conference on Decision and Control, CDC 2024

作者： Tackett, Justin Grimsman, David Brigham Young University Provo Computer Science Department UT United States

ISBN: (纸本)9798350316339

This work addresses submodular maximization problems, a widely-used mathematical tool to model many real-world decisions. Though this set of problems is NP-Hard, a well-known result is that a distributed greedy algorithm, wherein agents sequentially make greedy decisions, is guaranteed to approximate the optimal solution with a multiplicative factor of 1/2. This work explores whether running the greedy algorithm with different agent orderings can provide stronger guarantees. In particular, we show that for two-agent systems, running the distributed greedy algorithm with both possible sequences ensures a guarantee of 2/3. We also show that this method does not benefit systems of more than two agents, but opens the door to future work for exploring other ways to sequence these systems. © 2024 IEEE.

关键词： expectation maximization algorithm

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Multiple Target Detection in Radar Systems: A Hybrid Approach Combining EM Clustering and Sparsity-based Reconstruction

Multiple Target Detection in Radar Systems: A Hybrid Approac...

引用

2024 IEEE International Workshop on Technologies for Defense and Security, TechDefense 2024

作者： Sun, Jiarui Hao, Chengpeng Yan, Linjie Orlando, Danilo Institute of Acoustics Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Università di Pisa Dipartimento di Ingegneria dell'Informazione Pisa Italy

ISBN: (纸本)9798331505585

This paper addresses the challenge of detecting multiple targets embedded in Gaussian noise with unknown range positions and angles of arrival. To this end, we introduce the signal classification model of interest resorting to the latent variable model. Meanwhile, an estimation procedure is proposed, combining the expectation-maximization algorithm with sparsity-based reconstruction method. The expectationmaximization algorithm classifies targets using the maximum a posteriori criterion, while the sparsity-based reconstruction method estimates the angles of arrival of targets utilizing sparsitypromoting prior. The estimated parameters are then applied to a decision scheme based on the Likelihood Ratio Test. Finally, numerical examples illustrate the effectiveness of the proposed method for classification and detection. © 2024 IEEE.

关键词： expectation maximization algorithm

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Learning Staged Trees from Incomplete Data 12

Learning Staged Trees from Incomplete Data

引用

12th International Conference on Probabilistic Graphical Models, PGM 2024

作者： Carter, Jack Storror Leonelli, Manuele Riccomagno, Eva Varando, Gherardo Dipartimento di Matematica Università degli Studi di Genova Genova Italy School of Science and Technology IE University Madrid Spain Image Processing Laboratory Universitat de València València Spain

Staged trees are probabilistic graphical models capable of representing any class of non-symmetric independence via a coloring of their vertices. Several structural learning routines have been defined and implemented to learn staged trees from data, under the frequentist or Bayesian paradigm. They assume a data set has been observed fully and, in practice, observations with missing entries are either dropped or imputed before learning the model. Here, we introduce the first algorithms for staged trees that handle missingness within the learning of the model. To this end, we characterize the likelihood of staged tree models in the presence of missing data and discuss pseudo-likelihoods that approximate it. A structural expectation-maximization algorithm estimating the model directly from the full likelihood is also implemented and evaluated. A computational experiment showcases the performance of the novel learning algorithms, demonstrating that it is feasible to account for different missingness patterns when learning staged trees. © 2024 J.S. Carter, M. Leonelli, E. Riccomagno & G. Varando.

关键词： expectation maximization algorithm

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Lunar Surface Target Detection Based on Improved YOLOv8 5

Lunar Surface Target Detection Based on Improved YOLOv8

引用

5th International Conference on Intelligent Computing and Human-Computer Interaction, ICHCI 2024

作者： Yang, Yu Wang, Zhen-Wei Zhao, Han Zhang, Zi-Yu School of Astronautics and Aeronautics University of Electronic Science & Technology of China Chengdu611731 China

ISBN: (纸本)9798350368284

In lunar exploration and research, the accurate identification and localization of lunar surface features is of great scientific significance and application value. However, target detection in lunar surface images faces a series of difficulties. Firstly, the lunar surface rock and soil is relatively monotonous and lacks texture information. Secondly,amount of image data on the lunar surface is less compared to most of the image data and there are overlapping areas between the images. Therefore, manually labeling lunar surface features is susceptible to significant errors. Thirdly, the size of the target in the lunar surface images is small and the resolution of the panorama spliced images of the lunar surface is high, leading to small target detection on high-resolution images a major technical difficulty. Aiming at the difficulty of data annotation of lunar surface images, this paper proposes a data annotation method based on panoramic spliced images. Then an improved YOLOv8 target detection algorithm is proposed by introducing an EMA(expectation-maximization-Attention) attention mechanisms, an adaptive feature fusion module and a sliding window based slice detection strategy, which solves the difficulty of detecting small targets on high-resolution images and avoids the problems of false detection and missed detection. © 2024 IEEE.

关键词： expectation maximization algorithm

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DeepCensored: Deep-Learning Based Probabilistic Forecasting Framework for Censored Data 23

DeepCensored: Deep-Learning Based Probabilistic Forecasting ...

引用

23rd IEEE International Conference on Machine Learning and Applications, ICMLA 2024

作者： Tian, Jiahao Porter, Michael D. School of Data Science University of Virginia CharlottesvilleVA United States School of Data Science University of Virginia Dept. of Systems and Information Engineering CharlottesvilleVA United States

ISBN: (纸本)9798350374889

Accurate time series forecasting is critical in various fields, including resource allocation and crime prevention. While traditional approaches often focus on continuous data, count data forecasting, especially with partially observed (censored) data, remains challenging. This paper introduces DeepCensored, a novel deep learning-based framework that combines the expectation-maximization (EM) algorithm with deep neural networks to deliver robust probabilistic forecasts. DeepCensored naturally handles interval-censored event data, where exact event times are unknown but fall within a specific interval. Through extensive simulations and real-world crime data analysis, our method significantly outperforms traditional forecasting approaches, reducing the Mean Absolute Error (MAE) by 50% and better detection of emerging crime trends. These results highlight DeepCensored's potential to provide actionable insights for law enforcement and public safety by predicting crime intensity and enabling resource-efficient policing strategies. © 2024 IEEE.

关键词： expectation maximization algorithm

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How to Design a R-D Model for UAV Video Transmission? 15

How to Design a R-D Model for UAV Video Transmission?

引用

15th IEEE International Conference on Wireless Communications and Signal Processing, WCSP 2023

作者： Yu, Zongyang Yang, Peng Cao, Xianbin Zheng, Dezhi Quek, Tony Q. S. Oliver Wu, Dapeng Beihang University School of Electronic Information Engineering Beijing China Peng Cheng Laboratory Department of Mathematics and Theories Shenzhen China Beijing Institute of Technology Advanced Research Institute of Multidisciplinary Sciences Beijing China Singapore University of Technology and Design Information Systems Technology and Design Singapore City University of Hong Kong Department of Computer Science Kowloon Hong Kong

ISBN: (纸本)9798350324662

Unmanned aerial vehicle (UAV) video transmission has been extensively applied in many crucial fields. However, the problem of designing a rate-distortion (R-D) model for UAV video transmission, which is essential for theoretical analysis of video coding and optimization of video transmission quality, is under-studied. The designed R-D model is desired to be simple, accurate, and generic owing to the limited capability of the UAV. Observing the key role of transformed residuals in the R-D model, this paper elaborately discusses the modeling of the transformed residual distribution and proposes an estimation algorithm to estimate the statistical parameter of the distribution. Specifically, considering the stringent requirements for low complexity and high generalization capability, we first design a linear parameter estimator by mining and analyzing UAV video statistical characteristics. Further, we develop a bias update scheme to improve the accuracy of the estimator. Test results on multiple real video sequences taken by UAVs show that the proposed estimation algorithm is more accurate than benchmarks. © 2023 IEEE.

关键词： expectation maximization algorithm R-D model rate control video coding Video transmission

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Graph Neural Networks with a Distribution of Parametrized Graphs 41

Graph Neural Networks with a Distribution of Parametrized Gr...

引用

41st International Conference on Machine Learning, ICML 2024

作者： Lee, See Hian Ji, Feng Xia, Kelin Tay, Wee Peng School of Electrical and Electronic Engineering Nanyang Technological University Singapore School of Physical and Mathematical Sciences Nanyang Technological University Singapore

Traditionally, graph neural networks have been trained using a single observed graph. However, the observed graph represents only one possible realization. In many applications, the graph may encounter uncertainties, such as having erroneous or missing edges, as well as edge weights that provide little informative value. To address these challenges and capture additional information previously absent in the observed graph, we introduce latent variables to parameterize and generate multiple graphs. The parameters follow an unknown distribution to be estimated. We propose a formulation in terms of maximum likelihood estimation of the network parameters. Therefore, it is possible to devise an algorithm based on expectation-maximization (EM). Specifically, we iteratively determine the distribution of the graphs using a Markov Chain Monte Carlo (MCMC) method, incorporating the principles of PAC-Bayesian theory. Numerical experiments demonstrate improvements in performance against baseline models on node classification for both heterogeneous and homogeneous graphs. Copyright 2024 by the author(s)

关键词： expectation maximization algorithm

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EM-assisted Quantization of Absolute Cleavage Timing and Symmetry for Blastomere

EM-assisted Quantization of Absolute Cleavage Timing and Sym...

引用

2024 IEEE International Conference on E-Health Networking, Application and Services, HealthCom 2024

作者： Chang, Huai-Wen Wu, Rong-Yu Wang, Ting-Yuan Chen, Ming-Jer Yi, Yu-Chiao Wang, Jung-Hua Department of Electrical Engineering National Taiwan Ocean University Keelung20224 Taiwan Industrial Technology Research Institute Hsinchu County 310401 Taiwan Division of Infertility LeeWomen’s Hospital Taichung40705 Taiwan Taichung40705 Taiwan

ISBN: (纸本)9798350350548

This study addresses the issue of blastomere cleavage timing, which is traditionally deemed the most important indicator in embryo quality assessment. We propose a novel quantization method for analyzing Time-lapse Incubator (TLI) images of embryos. This method incorporates five image classifiers, an instance segmentor, and the expectation-maximization (EM) algorithm to provide a cost-effective and reliable tool and to offer interpretable data for further research in the reproductive medicine field. The method is characterized in that the EM algorithm is employed to enhance the performance of classifiers and segmentor in fitting different patient data sources. The method enables (1) to identify the absolute cleavage timing of blastomere, i.e., tPNa, tPNf, t2, t3, t4, t5, t6, t7, and t8, (2) tracking the cleavage process of blastomere to highlight the irregular cleavage (IRC) patterns, (3) to quantitatively assess the symmetry degree of blastomere. Our method reduces the average time difference between Ground Truth and CTP to just 2.9 hours, a significant improvement over the 6.13 hours reported by Vitrolife™. Moreover, our method identifies up to eight cleavage time points, surpassing the six cleavage time points provided by Vitrolife™. © 2024 IEEE.

关键词： expectation maximization algorithm

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