Results 31 to 40 of about 88,242 (260)
Mathematics, 2023 The mixture of experts (ME) model is effective for multimodal data in statistics and machine learning. To treat non-stationary probabilistic regression, the mixture of Gaussian processes (MGP) model has been proposed, but it may not perform well in some ...
Yurong Xie, Di Wu, Zhe Qiang
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Gaussian Mixture Solvers for Diffusion Models
Advances in Neural Information Processing Systems 36, 2023 Recently, diffusion models have achieved great success in generative tasks. Sampling from diffusion models is equivalent to solving the reverse diffusion stochastic differential equations (SDEs) or the corresponding probability flow ordinary differential equations (ODEs).
Hanzhong Guo +6 more
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Responsible Gaussian Model: Matrix-Based Approximation of Gaussian Mixture Model
IEEE AccessMechanisms of deep learning are often viewed as a unclear structure and are difficult to interpret or control precisely using mathematical or engineering principles.
Wataru Obayashi +2 more
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Improved Bearings-Only Multi-Target Tracking with GM-PHD Filtering
Sensors, 2016 In this paper, an improved nonlinear Gaussian mixture probability hypothesis density (GM-PHD) filter is proposed to address bearings-only measurements in multi-target tracking.
Qian Zhang, Taek Lyul Song
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OPTICAL-TO-SAR IMAGE REGISTRATION BASED ON GAUSSIAN MIXTURE MODEL [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2012 Image registration is a fundamental in remote sensing applications such as inter-calibration and image fusion. Compared to other multi sensor image registration problems such as optical-to-IR, the registration for SAR and optical images has its specials.
H. Wang +8 more
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Diffusion model conditioning on Gaussian mixture model and negative Gaussian mixture gradient
NeurocomputingDiffusion models (DMs) are a type of generative model that has a huge impact on image synthesis and beyond. They achieve state-of-the-art generation results in various generative tasks. A great diversity of conditioning inputs, such as text or bounding boxes, are accessible to control the generation.
Weiguo Lu +5 more
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IMAGE SEGMENTATION USING GAUSSIAN MIXTURE MODEL
International Journal of Industrial Engineering and Production Research, 2008 Stochastic models such as mixture models, graphical models, Markov random fields and hidden Markov models have key role in probabilistic data analysis. In this paper, we have learned Gaussian mixture model to the pixels of an image. The parameters of the
Rahman Farnoosh, Behnam Zarpak
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House Prices Segmentation Using Gaussian Mixture Model-Based Clustering
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 2022 House is a place for humans to live and a main necessity for humans. For years, the need for houses is increasing and varied so that it affects the selling price of the house. Therefore, more research is needed to learn about the selling price of houses.
Muhammad Hafidh Raditya +2 more
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An Improved Gaussian Mixture CKF Algorithm under Non-Gaussian Observation Noise
Discrete Dynamics in Nature and Society, 2016 In order to solve the problems that the weight of Gaussian components of Gaussian mixture filter remains constant during the time update stage, an improved Gaussian Mixture Cubature Kalman Filter (IGMCKF) algorithm is designed by combining a Gaussian ...
Hongjian Wang, Cun Li
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Optimized Data Association Based on Gaussian Mixture Model
IEEE Access, 2020 Data association is the foundation of state estimation in mobile robot simultaneous localization and mapping. Aiming at the problems of false association, high computational complexity in joint compatible branch and bound algorithm, we propose an ...
Xiaogang Ruan +3 more
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