检索结果-内蒙古大学图书馆

1st Annual IEEE Conference on Control Technology and Applications

作者： Lin, Tsen-Chang Liu, Yen-Chen Natl Cheng Kung Univ Dept Mech Engn Tainan 70101 Taiwan

ISBN: (纸本)9781509021826

This paper proposes a direct learning controller for wireless sensor and robot network to coverage an environment by utilizing expectation maximization algorithm. In addition to sensors, including high-level and low-level sensors, mounted on mobile robots, low-level stationary sensors are considered to provide information for enhance the performance of coverage control. The main objective is to maximize the information quantity of high-level sensors from the sensing density generated by a group of low-level sensors. This direct method uses the parameter of basis function to design controller based on expectation maximization (EM) algorithm. Moreover, the proposed estimation and learning law are also used in indirect method for other coverage controller. Subsequently, this paper proposes a transformation method based on EM algorithm for coverage problem with complicated sensing function such as Gaussian function. Numerical examples are introduced to demonstrate the performance of the proposed direct coverage control in wireless sensor and robot network.

关键词： wireless sensor and robot network coverage control direct density estimation expectation maximization algorithm

来源：评论

学校读者我要写书评

暂无评论

Bilinear Hybrid expectation maximization and expectation Propagation for Semi-Blind Channel Estimation 32

Bilinear Hybrid Expectation Maximization and Expectation Pro...

引用

32nd European Signal Processing Conference (EUSIPCO)

作者： Zhao, Zilu Slock, Dirk EURECOM Commun Syst Dept Biot France

ISBN: (纸本)9789464593617;9798331519773

This paper discusses channel estimation during uplink transmission in Cell-Free (CF) Massive MIMO (MaMIMO) systems. We model the problem as a semi-blind estimation problem with independent and identically distributed (i.i.d.) Gaussian input. Two hybrid expectation maximization (EM) and expectation Propagation (EP) algorithms are proposed to improve convergence behavior. The first algorithm, EM-EP, adopts a vector-level EP approach by treating the per-user channel coefficients and data sequence as EP variables. To make the algorithm tractable, we use the central limit theorem (CLT) to approximate the interference terms and employ EM to construct a majorizer function for the likelihood of the received data, leading to majorization minimization. To further enhance convergence behavior, we propose a matrix-level loop-free EM-EP algorithm. In this algorithm, we treat the channel coefficients and data sequences corresponding to users using the same pilot as EP variables. This method is an alternating minimization algorithm, ensuring convergence. Our simulations verify the effectiveness of the two proposed algorithms.

关键词： expectation maximization algorithm

来源：评论

学校读者我要写书评

暂无评论

From Biased Selective Labels to Pseudo-Labels: An expectation-maximization Framework for Learning from Biased Decisions 41

From Biased Selective Labels to Pseudo-Labels: An Expectatio...

引用

41st International Conference on Machine Learning (ICML)

作者： Chang, Trenton Wiens, Jenna Univ Michigan Div Comp Sci & Engn Ann Arbor MI 48109 USA

Selective labels occur when label observations are subject to a decision-making process;e.g., diagnoses that depend on the administration of laboratory tests. We study a clinically-inspired selective label problem called disparate censorship, where labeling biases vary across subgroups and unlabeled individuals are imputed as "negative" (i.e., no diagnostic test = no illness). Machine learning models naively trained on such labels could amplify labeling bias. Inspired by causal models of selective labels, we propose Disparate Censorship expectation-maximization (DCEM), an algorithm for learning in the presence of disparate censorship. We theoretically analyze how DCEM mitigates the effects of disparate censorship on model performance. We validate DCEM on synthetic data, showing that it improves bias mitigation (area between ROC curves) without sacrificing discriminative performance (AUC) compared to baselines. We achieve similar results in a sepsis classification task using clinical data.

关键词： expectation maximization algorithm

来源：评论

学校读者我要写书评

暂无评论

Optimization of Iterative Blind Detection Based on expectation maximization and Belief Propagation 58

Optimization of Iterative Blind Detection Based on Expectati...

引用

58th Asilomar Conference on Signals, Systems and Computers, ACSSC 2024

作者： Schmid, Luca Raviv, Tomer Shlezinger, Nir Schmalen, Laurent Karlsruhe Institute of Technology Communications Engineering Lab Karlsruhe76187 Germany School of Ece Ben-Gurion University of the Negev Be'er-Sheva Israel

ISBN: (纸本)9798350354058

We study iterative blind symbol detection for block-fading linear inter-symbol interference channels. Based on the factor graph framework, we design a joint channel estimation and detection scheme that combines the expectation maximization (EM) algorithm and the ubiquitous belief propagation (BP) algorithm. Interweaving the iterations of both schemes signifi-cantly reduces the EM algorithm's computational burden while retaining its excellent performance. To this end, we apply simple yet effective model-based learning methods to find a suitable parameter update schedule by introducing momentum in both the EM parameter updates as well as in the BP message passing. Numerical simulations verify that the proposed method can learn efficient schedules that generalize well and even outperform coherent BP detection in high signal-to-noise scenarios. © 2024 IEEE.

关键词： expectation maximization algorithm

来源：评论

学校读者我要写书评

暂无评论

Explicit Memory Learning with expectation maximization

Explicit Memory Learning with Expectation Maximization

引用

2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Yin, Zhangyue Sun, Qiushi Guo, Qipeng Zeng, Zhiyuan Cheng, Qinyuan Qiu, Xipeng Huang, Xuanjing School of Computer Science Fudan University China The University of Hong Kong Hong Kong Shanghai AI Laboratory China

ISBN: (纸本)9798891761643

Large Language Models (LLMs) have revolutionized the landscape of natural language processing, demonstrating remarkable abilities across various complex tasks. However, their stateless nature limits the capability to retain information across interactions, hindering performance in scenarios requiring historical context recall. To mitigate this, current approaches primarily use explicit memory to allow LLMs to store useful information, which is accessible, readable, and interpretable. Nevertheless, explicit memory lacks the reliable learning mechanisms of implicit memory, which can be optimized end-to-end. To harness the benefits of both, we introduce EM2, a novel framework enhancing explicit memory updates via the expectation-maximization (EM) algorithm. EM2 treats memory as a latent variable, ensuring continual learning and improvement during updates. Experimental results on streaming inference tasks demonstrate that EM2 outperforms existing methods without memory or with static external memory. Our in-depth analysis highlights that EM2 significantly enhances performance across various backbones and memory strategies, providing a robust solution for advancing LLM memory management and enabling explicit memory to learn and improve similarly to implicit memory. © 2024 Association for Computational Linguistics.

关键词： expectation maximization algorithm

来源：评论

学校读者我要写书评

暂无评论

System Identification for Linear Dynamics with Bilinear Observation Models: An expectation-maximization Approach 63

System Identification for Linear Dynamics with Bilinear Obse...

引用

63rd IEEE Conference on Decision and Control, CDC 2024

作者： Liu, Diyou Khosravi, Mohammad Delft University of Technology Delft Center for Systems and Control Delft Netherlands

ISBN: (纸本)9798350316339

In this paper, we study the system identification problem for linear time-invariant dynamics with bilinear observation models. Accordingly, we consider a suitable parametric description for the system model and formulate the identification problem as estimating the parameters characterizing the mathematical representation of the system through input-output measurement data. To this end, we employ a probabilistic framework aiming to obtain the maximum likelihood estimates of the parameters. Accordingly, we propose utilizing the expectation-maximization approach to improve the tractability of the identification procedure. Through the numerical experiments, we verify the efficacy of the proposed scheme and demonstrate its performance. © 2024 IEEE.

关键词： expectation maximization algorithm

来源：评论

学校读者我要写书评

暂无评论

Fuzzy expectation maximization Phoneme Prediction in Diffusion Model-based Dysarthria Voice Conversion

Fuzzy Expectation Maximization Phoneme Prediction in Diffusi...

引用

2024 International Conference on Fuzzy Theory and Its Applications, iFUZZY 2024

作者： Lin, Guang-Tao Hsu, Wen-Shin Liu, Guan-Tsen Chen, Su-Juan Chung Shan Medical University Department of Medical Information Taichung Taiwan Chung Shan Medical University & Chung Shan Medical University Hospital Department of Medical Information & Informatics Office Technology Taichung Taiwan

ISBN: (纸本)9798350352788

In this paper proposed an effective fuzzy expectation-maximization phoneme prediction method in diffusion model-based dysarthria voice conversion (FEMPPDM-DVC) which is accessible to (i) training without parallel data (ii) converting a longer duration of the audio data (iii) preserves speaker identity. By integrating Fuzzy expectation-maximization (FEM) clustering and Diffusion model-based dysarthria voice conversion approach, the proposed method is able gradually generate normal utterances. Feature extraction is performed using Mel-frequency cepstral coefficients (MFCCs), a robust method in acoustic feature extraction in Text-to-Speech systems. Ensures the effectiveness and accuracy, through repeated parameter adjustment using the FEM clustering algorithm. The conversion network is a diffusion model-based structure, which can convert normal utterances to dysarthria utterances in the forward diffusion process. After forward diffusion process, the dysarthria utterance will go through a reverse diffusion process to convert dysarthria voice to normal utterance. Once converted, a GAN-based vocoder is applied to convert mel-frequency spectrogram to waveform. Objective evaluation is conducted on the Saarbrücken Voice Database (SVD) dataset show that FEMDDPM-DVC is able to improve the intelligibility and naturalness of dysarthria utterances in 15 kinds of dysarthria voice. The proposed method is compared with five other dysarthria voice conversion methods, show that the proposed method has the most performance among other method. © 2024 IEEE.

关键词： expectation maximization algorithm

来源：评论

学校读者我要写书评

暂无评论

Towards Multimodal-augmented Pre-trained Language Models via Self-balanced expectation-maximization Iteration 24

Towards Multimodal-augmented Pre-trained Language Models via...

引用

32nd ACM International Conference on Multimedia, MM 2024

作者： Zhuang, Xianwei Cheng, Xuxin Zhu, Zhihong Chen, Zhanpeng Li, Hongxiang Zou, Yuexian Sece Peking University Shenzhen China

ISBN: (纸本)9798400706868

Pre-trained language models (PLMs) that rely solely on textual corpus may present limitations in multimodal semantics comprehension. Existing studies attempt to alleviate this issue by incorporating additional modal information through image retrieval or generation. However, these methods: (1) inevitably encounter modality gaps and noise;(2) treat all modalities indiscriminately;and (3) ignore visual or acoustic semantics of key entities. To tackle these challenges, we propose a novel principled iterative framework for multimodal-augmented PLMs termed MASE, which achieves efficient and balanced injection of multimodal semantics under the proposed expectation maximization (EM) based iterative algorithm. Initially, MASE utilizes multimodal proxies instead of explicit data to enhance PLMs, which avoids noise and modality gaps. In E-step, MASE adopts a novel information-driven self-balanced strategy to estimate allocation weights. Furthermore, MASE employs heterogeneous graph attention to capture entity-level fine-grained semantics on the proposed multimodal-semantic scene graph. In M-step, MASE injects global multimodal knowledge into PLMs through a cross-modal contrastive loss. Experimental results show that MASE consistently outperforms competitive baselines on multiple tasks across various architectures. More impressively, MASE is compatible with existing efficient parameter fine-tuning methods, such as prompt learning. © 2024 ACM.

关键词： expectation maximization algorithm

来源：评论

学校读者我要写书评

暂无评论

Adaptive Radar Detection in joint Range and Azimuth based on the Hierarchical Latent Variable Model

arXiv

引用

arXiv 2025年

作者： Yan, Linjie Hao, Chengpeng Han, Sudan Ricci, Giuseppe Hu, Zhanhao Orlando, Danilo Institute of Acoustics Chinese Academy of Sciences Beijing100864 China National Innovation Institute of Defense Technology Beijing China Dipartimento di Ingegneria dell’Innovazione Università del Salento Via Monteroni Lecce73100 Italy Dipartimento di Ingegneria dell’ Informazione Università di Pisa Via Caruso 16 Pisa56122 Italy

This paper focuses on the design of a robust decision scheme capable of operating in target-rich scenarios with unknown signal signatures (including their range positions, angles of arrival, and number) in a background of Gaussian disturbance. To solve the problem at hand, a novel estimation procedure is conceived resorting to the expectation-maximization algorithm in conjunction with the hierarchical latent variable model that are exploited to come up with a maximum a posteriori rule for reliable signal classification and angle of arrival estimation. The estimates returned by the procedure are then used to build up an adaptive detection architecture in range and azimuth based on the likelihood ratio test with enhanced detection performance. Remarkably, it is shown that the new decision scheme can maintain constant the false alarm rate when the interference parameters vary in the considered range of values. The performance assessment, conducted by means of Monte Carlo simulation, highlights that the proposed detector exhibits superior detection performance in comparison with the existing GLRT-based competitors. Copyright © 2025, The Authors. All rights reserved.

关键词： expectation maximization algorithm

来源：评论

学校读者我要写书评

暂无评论

A Bayesian network approach to traffic flow forecasting

引用

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2006年第1期7卷 124-132页

作者： Sun, SL Zhang, CS Yu, GQ Tsinghua Univ Dept Automat State Key Lab Intelligent Technol & Syst Beijing 100084 Peoples R China NuTech Co Ltd Beijing 100084 Peoples R China

A new approach based on Bayesian networks for traffic flow forecasting is proposed. In this paper, traffic flows among adjacent road links in a transportation network are modeled as a Bayesian network. The joint probability distribution between the cause nodes (data utilized for forecasting) and the effect node (data to be forecasted) in a constructed Bayesian network is described as a Gaussian mixture model (GMM) whose parameters are estimated via the competitive expectation maximization (CEM) algorithm. Finally, traffic flow forecasting is performed under the criterion of minimum mean square error (mmse). The approach departs from many existing traffic flow forecasting models in that it explicitly includes information from adjacent road links to analyze the trends of the current link statistically. Furthermore, it also encompasses the issue of traffic flow forecasting when incomplete data exist Comprehensive experiments on urban vehicular traffic flow data of Beijing and comparisons with several other methods show that the Bayesian network is a very promising and effective approach for traffic flow modeling and forecasting, both for complete data and incomplete data.

关键词： Bayesian network expectation maximization algorithm Gaussian mixture model traffic flow forecasting

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：