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pro vyhledávání: '"multi-modal distributions"'
Over the past few years, several approaches utilizing score-based diffusion have been proposed to sample from probability distributions, that is without having access to exact samples and relying solely on evaluations of unnormalized densities. The r
Externí odkaz:
http://arxiv.org/abs/2410.19449
Autor:
Wu, Dongze, Xie, Yao
Sampling from high dimensional, multimodal distributions remains a fundamental challenge across domains such as statistical Bayesian inference and physics based machine learning. In this paper, we propose Annealing Flow, a continuous normalizing flow
Externí odkaz:
http://arxiv.org/abs/2409.20547
Autor:
Yu, Quanfu, Xu, Jun
Publikováno v:
In Mechanical Systems and Signal Processing 1 May 2023 190
We propose an adaptively weighted stochastic gradient Langevin dynamics algorithm (SGLD), so-called contour stochastic gradient Langevin dynamics (CSGLD), for Bayesian learning in big data statistics. The proposed algorithm is essentially a \emph{sca
Externí odkaz:
http://arxiv.org/abs/2010.09800
Autor:
Pandeva, Teodora, Schubert, Matthias
Over the past years, Generative Adversarial Networks (GANs) have shown a remarkable generation performance especially in image synthesis. Unfortunately, they are also known for having an unstable training process and might loose parts of the data dis
Externí odkaz:
http://arxiv.org/abs/1911.06663
This paper proposes a novel approach for predicting the motion of pedestrians interacting with others. It uses a Generative Adversarial Network (GAN) to sample plausible predictions for any agent in the scene. As GANs are very susceptible to mode col
Externí odkaz:
http://arxiv.org/abs/1904.09507
Publikováno v:
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol V-4-2022, Pp 91-98 (2022)
Long-term human path forecasting in crowds is critical for autonomous moving platforms (like autonomous driving cars and social robots) to avoid collision and make high-quality planning. It is not easy for prediction systems to successfully take into
Externí odkaz:
https://doaj.org/article/bf1637415a024c7e9e1480f298c3432e
A key task in Bayesian statistics is sampling from distributions that are only specified up to a partition function (i.e., constant of proportionality). However, without any assumptions, sampling (even approximately) can be #P-hard, and few works hav
Externí odkaz:
http://arxiv.org/abs/1710.02736
Akademický článek
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Autor:
Hannah Horng, Apurva Singh, Bardia Yousefi, Eric A. Cohen, Babak Haghighi, Sharyn Katz, Peter B. Noël, Russell T. Shinohara, Despina Kontos
Publikováno v:
Scientific Reports, Vol 12, Iss 1, Pp 1-12 (2022)
Abstract Radiomic features have a wide range of clinical applications, but variability due to image acquisition factors can affect their performance. The harmonization tool ComBat is a promising solution but is limited by inability to harmonize multi
Externí odkaz:
https://doaj.org/article/66d9fc5006474697810d5d04f5965eab