Search Results - complex noisy expectation maximization algorithm

Noise Benefits in Complex-Valued Neural Networks by Ren, Lei, Liu, Chunyang, Zhang, Ying

“… We first establish the equivalence between the complex backpropagation algorithm and the complex expectation maximization algorithm from the perspective of maximum probability likelihood estimation…”
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Learning Diffusion Model from Noisy Measurement using Principled Expectation-Maximization Method by Bai, Weimin, Tang, Weiheng, Ye, Enze, Chen, Siyi, Chen, Wenzheng, Sun, He

“… In this paper, we propose a principled expectation-maximization (EM) framework that iteratively learns diffusion models from noisy data with arbitrary corruption types…”
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Nonlinear Causal Discovery via Dynamic Latent Variables by Yang, Xing, Lan, Tian, Qiu, Hao, Zhang, Chen

“… and the validity of scientific inferences. Traditional experimental designs, such as randomized trials, often fall short in complex systems where variables interact in a high-dimensional space with limited data…”
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Two Model-Based EM Algorithms for Blind Source Separation in Noisy Environments by Schwartz, Boaz, Gannot, Sharon, Habets, Emanuel A. P.

“… Blind estimation of the acoustic parameters and the individual source signals is carried out by applying the expectation-maximization (EM) algorithm…”
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Gaussian Mixture—Expectation Maximization–Based Learned AMP Network With CNN Deep Residual Network for Millimeter Wave Communication by K, Shoukath Ali, Philip, Sajan P, T, Perarasi

“…‐LAMP algorithm incorporates the expectation maximization (EM) algorithm to recover channel information…”
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Blind speech separation employing directional statistics in an Expectation Maximization framework by Dang Hai Tran Vu, Haeb-Umbach, R

“… Estimation formulas are derived for these parameters by employing the Expectation Maximization (EM) algorithm…”
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Unsupervised Change Detection for Satellite Images Using Dual-Tree Complex Wavelet Transform by Celik, T., Kai-Kuang Ma

“… The algorithm exploits the inherent multiscale structure of the dual-tree complex wavelet transform (DT-CWT…”
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An Algebraic-Geometric Approach for Linear Regression Without Correspondences by Tsakiris, Manolis C., Peng, Liangzu, Conca, Aldo, Kneip, Laurent, Shi, Yuanming, Choi, Hayoung

“…Linear regression without correspondences is the problem of performing a linear regression fit to a dataset for which the correspondences between the…”
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XRCT image processing for sand fabric reconstruction by Tan, Peng, Wijesuriya, Hasitha Sithadara, Sitar, Nicholas

“…We explore computationally efficient techniques to improve the XRCT image processing of low resolution and very noisy images for use in reconstruction of the fabric of densely packed, natural sand deposits…”
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Audio-Visual Speech Enhancement Using Conditional Variational Auto-Encoders by Sadeghi, Mostafa, Leglaive, Simon, Alameda-Pineda, Xavier, Girin, Laurent, Horaud, Radu

“…Variational auto-encoders (VAEs) are deep generative latent variable models that can be used for learning the distribution of complex data…”
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Robust Graph Classification with Instance Independent Noisy Labels: An EM Approach using Kolmogorov-Arnold Networks by Pathan, Arif Khan, Kulkarni, Raghavendra, Batreddy, Subbareddy, Pamisetty, Giridhar, Verma, Priya, Babu, Ch Sobhan

“… We propose an Expectation-Maximization (EM)-based algorithm integrated with a Kolmogorov-Arnold Network (KAN…”
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Low-rank Gaussian mixture modeling of space-snapshot representation of microphone array measurements for acoustic imaging in a complex noisy environment by Yu, Liang, Antoni, Jerome, Deng, Jiayu, Li, Cong, Jiang, Weikang

“…•Space-snapshot representation for acoustic imaging in a complex, noisy environment is investigated…”
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Constrained Gaussian mixture model framework for automatic segmentation of MR brain images by Greenspan, H., Ruf, A., Goldberger, J.

“… The expectation-maximization (EM) algorithm is utilized to learn the parameter-tied, constrained Gaussian mixture model…”
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Parameter Estimation of the Reduced RUM Using the EM Algorithm by Feng, Yuling, Habing, Brian T., Huebner, Alan

“…). Using a maximum likelihood estimation algorithm, this article addresses the estimation issue of a complex compensatory DCM, the reduced reparameterized unified model (rRUM…”
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Blur Removal Via Blurred-Noisy Image Pair by Gu, Chunzhi, Lu, Xuequan, He, Ying, Zhang, Chao

“… We compute patch correspondences by analyzing the optical flow between the two images. The Expectation Maximization (EM…”
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Convergence analysis of the loping OS-EM algorithm for noisy data by Guo, Yao, Fu, Min, Wang, Jinping

“…In this paper, we modify the OS-EM algorithm with an overrelaxation. For noisy data, we focus on the convergence analysis for loping OS-EM algorithm and extend the noisy data in -norm space…”
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FER Estimation in a Memoryless BSC With Variable Frame Length and Unreliable ACK/NAK Feedback by Rico-Alvarino, Alberto, Lopez-Valcarce, Roberto, Mosquera, Carlos, Heath, Robert W.

“… Whereas FER estimation is relatively straightforward if all observations correspond to packets with equal length, the problem becomes considerably more complex when this is…”
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Learning Input Driven Dynamic Bayesian Networks with Measurement Noise by Veres, David, Li, Ping, Kadirkamanathan, Visakan

“… 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…”
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Surface electromyography as a tool to assess the responses of car passengers to lateral accelerations: Part I. Extraction of relevant muscular activities from noisy recordings by Farah, G., Hewson, D.J., Duchêne, J.

“…) recordings under difficult experimental conditions with a poor signal to noise ratio. High amplitude artifacts, the QRS complex, low frequency noise and white noise significantly alter EMG characteristics…”
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Joint Network Reconstruction and Community Detection from Rich but Noisy Data by Hu, Jie, Chen, Xiao, Chen, Yu, Zhang, Weiping

“… A generalized expectation-maximization (EM) algorithm is developed for computing the maximum likelihood estimates, and an information criterion is introduced to consistently select the number of communities…”
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