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检索条件"主题词=Expectation maximization algorithm"

共 648 条记录，以下是401-410 订阅

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Efficient Dual-Confounding Eliminating for Weakly-supervised Temporal Action Localization 24

Efficient Dual-Confounding Eliminating for Weakly-supervised...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Li, Ao Liu, Huijun Sheng, Jinrong Chen, Zhongming Ge, Yongxin School of Big Data and Software Engineering Chongqing University Chongqing China College of Computer Science Chongqing University Chongqing China

ISBN: (纸本)9798400706868

Weakly-supervised Temporal Action Localization (WTAL) following a localization-by-classification paradigm has achieved significant results, yet still grapples with confounding arising from ambiguous snippets. Previous works have attempted to distinguish these ambiguous snippets from action snippets without investigating the underlying causes of their formation, thus failing to effectively eliminate the bias on both action-context and action-content. In this paper, we revisit WTAL from the perspective of structural causal model to identify the true origins of confounding, and propose an efficient dual-confounding eliminating framework to alleviate these biases. Specifically, we construct a Substituted Confounder Set (SCS) to eliminate the confounding bias on action-context by leveraging the modal disparity between RGB and FLOW. Then, a Multi-level Consistency Mining (MCM) method is designed to mitigate the confounding bias on action-content by utilizing the consistency between discriminative snippets and corresponding proposals at both the feature and label levels. Notably, SCS and MCM could be seamlessly integrated into any two-stream models without additional parameters by expectation-maximization (EM) algorithm. Extensive experiments on two challenging benchmarks including THUMOS14 and ActivityNet-1.2 demonstrate the superior performance of our method. © 2024 ACM.

关键词： expectation maximization algorithm

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Multi-Agent Trajectory Planning with NUV Priors

Multi-Agent Trajectory Planning with NUV Priors

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2024 American Control Conference, ACC 2024

作者： Van Erp, Bart Bagaev, Dmitry Podusenko, Albert Senoz, Ismail De Vries, Bert Eindhoven University of Technology Department of Electrical Engineering Eindhoven Netherlands Lazy Dynamics Eindhoven Netherlands GN Hearing Eindhoven Netherlands

ISBN: (纸本)9798350382655

This paper presents a probabilistic model-based approach to centralized multi-agent trajectory planning. This approach allows for incorporating uncertainty of the state and dynamics of the agents directly in the model. Probabilistic inference is then efficiently automated using message passing. The recently introduced normal-with-unknown-variance (NUV) priors are used to prevent collisions between agents and obstacles. Furthermore, a new expectation-maximization inference scheme is derived for box and half-space NUV priors, which takes state uncertainty into account when avoiding collisions. © 2024 AACC.

关键词： expectation maximization algorithm

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Enhanced Channel Estimation for Multi-tag Ambient Backscatter Communication Systems 7

Enhanced Channel Estimation for Multi-tag Ambient Backscatte...

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7th International Conference on Signal Processing and Information Security, ICSPIS 2024

作者： Abdallah, Saeed Saad, Mohamed Albreem, Mahmoud University of Sharjah Dept. of Electrical Engineering Sharjah United Arab Emirates University of Sharjah Dept. of Computer Engineering Sharjah United Arab Emirates

ISBN: (纸本)9798350368673

Ambient backscatter communication (AmBC) is a highly promising communication paradigm for the next generation, energy efficient Internet-of-Things (IoT) applications. In this work, we consider the problem of channel estimation for multi-tag AmBC systems. Recent works have proposed pilot-based solutions for this problem, which incur a significant training overhead. In this work, we propose a novel semi-blind algorithm for channel estimation in multi-tag AmBCs. The proposed algorithm is based on the expectation maximization framework and incorporates both pilot sequences and data symbols into the estimation process. Our simulations demonstrate the mean-squared error performance of the proposed scheme, showing that it yields substantially higher estimation accuracy than the state of the art. © 2024 IEEE.

关键词： expectation maximization algorithm

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Optimality Gap of Decentralized Submodular maximization under Probabilistic Communication 63

Optimality Gap of Decentralized Submodular Maximization unde...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Vendrell, Joan Kia, Solmaz University of California Irvie Engineering Department IrvineCA United States

ISBN: (纸本)9798350316339

This paper considers the problem of decentralized submodular maximization subject to partition matroid constraint using a sequential greedy algorithm with probabilistic inter-agent message-passing. We propose a communication-aware framework where the probability of successful communication between connected devices is considered. Our analysis introduces the notion of the probabilistic optimality gap, highlighting its potential influence on determining the message-passing sequence based on the agent's broadcast reliability and strategic decisions regarding agents that can broadcast their messages multiple times in a resource-limited environment. This work not only contributes theoretical insights but also has practical implications for designing and analyzing decentralized systems in uncertain communication environments. A numerical example demonstrates the impact of our results. © 2024 IEEE.

关键词： expectation maximization algorithm

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Joint learning of Gaussian graphical models in heterogeneous dependencies of high-dimensional transcriptomic data 16

Joint learning of Gaussian graphical models in heterogeneous...

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16th Asian Conference on Machine Learning, ACML 2024

作者： Nguyen, Dung Ngoc Li, Zitong CSIRO Agriculture and Food CanberraACT Australia

In biology, constructing gene co-expression networks presents a significant research challenge, largely due to the high dimensionality of the data and the heterogeneity of the samples. Furthermore, observations from two or more groups sharing the same biological variables require the comparison of gene co-expression patterns with some commonalities between the groups. In this context, we propose a mixture of Gaussian graphical models for paired data to estimate heterogeneous dependencies and uncover sub-population networks within complex biological datasets, incorporating sparsity and symmetry constraints between two groups of dependent variables. We develop an efficient generalized expectation-maximization (EM) algorithm for penalized maximum likelihood estimation with the fusion of a graphical lasso penalty. As a result, our simulation studies highlight the numerical performance of the proposed method, demonstrating its superior model fitting compared to the classical graphical lasso approach. We further demonstrate the practical application of our approach by estimating gene networks on a high-dimensional ecological transcriptomics data set of the nine-spined stickleback. Our new approach identified similarities and differences between groups of genes from the brain and liver tissues of samples collected from two habitats. These results show the efficiency of our approach to the identification of complicated interactions from high-dimensional and heterogeneous gene expression data. © 2024 D.N. Nguyen & Z. Li.

关键词： expectation maximization algorithm

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SimPro: A Simple Probabilistic Framework Towards Realistic Long-Tailed Semi-Supervised Learning 41

SimPro: A Simple Probabilistic Framework Towards Realistic L...

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41st International Conference on Machine Learning, ICML 2024

作者： Du, Chaoqun Han, Yizeng Huang, Gao Department of Automation BNRist Tsinghua University Beijing China

Recent advancements in semi-supervised learning have focused on a more realistic yet challenging task: addressing imbalances in labeled data while the class distribution of unlabeled data remains both unknown and potentially mismatched. Current approaches in this sphere often presuppose rigid assumptions regarding the class distribution of unlabeled data, thereby limiting the adaptability of models to only certain distribution ranges. In this study, we propose a novel approach, introducing a highly adaptable framework, designated as SimPro, which does not rely on any predefined assumptions about the distribution of unlabeled data. Our framework, grounded in a probabilistic model, innovatively refines the expectation-maximization (EM) algorithm by explicitly decoupling the modeling of conditional and marginal class distributions. This separation facilitates a closed-form solution for class distribution estimation during the maximization phase, leading to the formulation of a Bayes classifier. The Bayes classifier, in turn, enhances the quality of pseudo-labels in the expectation phase. Remarkably, the SimPro framework not only comes with theoretical guarantees but also is straightforward to implement. Moreover, we introduce two novel class distributions broadening the scope of the evaluation. Our method showcases consistent state-of-the-art performance across diverse benchmarks and data distribution scenarios. Our code is available at https://***/LeapLabTHU/SimPro. Copyright 2024 by the author(s)

关键词： expectation maximization algorithm

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EM-based Batch algorithm for Trajectory Estimation of Multiple Targets 2

EM-based Batch Algorithm for Trajectory Estimation of Multip...

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2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024

作者： Yan, Linjie Orlando, Danilo Addabbo, Pia Fiscante, Nicomino Hao, Chengpeng Giunta, Gaetano Institute of Acoustics Chinese Academy of Sciences Beijing China Università Di Pisa Rome Italy Università Degli Studi 'Giustino Fortunato' Benevento Italy University of Roma Tre Rome Italy

ISBN: (纸本)9798331515669

This study investigates the trajectory clustering problem in the presence of multiple moving targets which are approximated by straight motion over the observation interval and monitored by a radar sensor network. The assumption is that the nodes in the network lack the knowledge of the measurements' association with each target, causing challenging estimation and classification tasks. To solve this problem, a novel unsupervised classifier based on expectation-maximization algorithm is devised by exploiting the latent variable model combined with cyclic estimation methods to obtain closed-form updates for the unknown parameters and build up a maximum a posteriori rule allowing for target measurement clustering. Finally, we analyze the effectiveness of the proposed algorithm through simulation experiments in a scenario with trajectory intersections. © 2024 IEEE.

关键词： expectation maximization algorithm

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Automatic Stock Profit maximization via Deep Reinforcement Learning and Correlation Analysis 15

Automatic Stock Profit Maximization via Deep Reinforcement L...

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15th International Conference on Information and Communication Technology Convergence, ICTC 2024

作者： Park, Soohyun Joo, Minjoo Sookmyung Women's University Division of Computer Science Seoul Korea Republic of

ISBN: (纸本)9798350364637

In this paper, we propose a novel Deep Deterministic Policy Gradient (DDPG)-based stock profit maximization approach with enhanced predictability and strategic decision-making by integrating correlation analysis between stocks. The proposed algorithm is designed to consider user budgets as well as evaluated to utilize real-world stock market data. Lastly, the performance of the proposed method is evaluated and verified that our proposed method outperforms the others. © 2024 IEEE.

关键词： expectation maximization algorithm

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Label Propagation algorithm Based on 2-Hop Neighbor and Weighted Path for Community Detection 7

Label Propagation Algorithm Based on 2-Hop Neighbor and Weig...

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7th International Conference on Data Science and Information Technology, DSIT 2024

作者： Feng, Boya Shanghai Jiao Tong University Department of Electronic Engineering Shanghai China

ISBN: (纸本)9798350384093

Community detection is a research focus in the field of complex networks and has wide applications in various domains. However, traditional label propagation algorithms and modularity maximization algorithms often fail to achieve optimal results in many community detection tasks. By observing the characteristics of real-world social networks, this paper proposes a label propagation algorithm based on 2-hop neighbor and weighted path (LPANW) for community detection. The proposed algorithm is compared with three other algorithms on both synthetic networks and real-world datasets, demonstrating better NMI index and robustness. © 2024 IEEE.

关键词： expectation maximization algorithm

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Choosing Behind the Veil: Tight Bounds for Identity-Blind Online algorithms 24

Choosing Behind the Veil: Tight Bounds for Identity-Blind On...

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25th Conference on Economics and Computation, EC 2024

作者： Ezra, Tomer Feldman, Michal Tang, Zhihao Gavin Harvard University Cambridge United States Tel Aviv University Microsoft ILDC Tel Aviv Israel ITCS Key Laboratory of Interdisciplinary Research of Computation and Economics Shanghai University of Finance and Economics Shanghai China

ISBN: (纸本)9798400707049

In Bayesian online settings, every element is associated with a value drawn from a known underlying distribution. This distribution, representing the population from which the element is drawn, is referred to as the element’s identity. The elements arrive sequentially, with their values being revealed in an online manner. Most previous work has assumed that, upon the arrival of a new element, the online algorithm observes its value and its identity. However, practical scenarios frequently require algorithms to make decisions based solely on the element’s value, disregarding its identity. This necessity emerges either from the algorithm’s lack of knowledge about the element’s identity or in the pursuit of fairness, aiming for bias-free decisions across varying identities. We call such algorithms identity-blind algorithms, and propose the identity-blindness gap as a metric to evaluate the performance loss in online algorithms caused by identity-blindness. This gap is defined as the maximum ratio between the expected performance of an identity-blind online algorithm and an optimal online algorithm that knows the arrival order, thus also the identities. We study the identity-blindness gap in the paradigmatic prophet inequality problem, under the two common objectives of maximizing the expected value, and maximizing the probability to obtain the highest value. We provide tight bounds with respect to both objectives. For the max-expectation objective, the celebrated prophet inequality establishes a single-threshold (thus identity-blind) algorithm that gives at least 1/2 of the offline optimum, thus also an identity-blindness gap of at least 1/2. We show that this bound is tight with respect to the identity-blindness gap, even with respect to randomized algorithms. For the max-probability objective, we provide a deterministic single-threshold (thus identity-blind) algorithm that gives an identity-blindness gap of ∼ 0.562 (assuming the absence of large point masses). Moreov

关键词： expectation maximization algorithm

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