Results 51 to 60 of about 5,051,769 (292)
Mixed Variational Inference [PDF]
2019 International Joint Conference on Neural Networks (IJCNN), 2019 The Laplace approximation has been one of the workhorses of Bayesian inference. It often delivers good approximations in practice despite the fact that it does not strictly take into account where the volume of posterior density lies. Variational approaches avoid this issue by explicitly minimising the Kullback-Leibler divergence DKL between a ...
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Topic-Document Inference With the Gumbel-Softmax Distribution
IEEE Access, 2021 Topic modeling is an important application of natural language processing (NLP) that can automatically identify the set of main topics of a given, typically large, collection of documents.
Amit Kumar +2 more
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CoRR, 2016 NIPS 2016 Workshop, Advances in Approximate Bayesian ...
Michael Figurnov +2 more
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A variational algorithm to detect the clonal copy number substructure of tumors from scRNA-seq data
Nature Communications, 2023 The inference of clonal architectures in cancer using single-cell RNA-seq data remains challenging. Here, the authors develop SCEVAN, a variational algorithm for copy number-based clonal structure inference in single-cell RNA-seq data that can ...
Antonio De Falco +4 more
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Extensible Gaussian Mixture Model for Image Prior Modeling [PDF]
Jisuanji gongcheng, 2020 To address the inextensible fixed number of components in image prior modeling based on Gaussian Mixture Model(GMM),this paper proposes an extensible GMM model based on Dirichlet Process(DP).Through the addition and merging mechanism of cluster ...
ZHANG Mohua, PENG Jianhua
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Variational inference at glacier scale
Journal of Computational Physics, 2022 We characterize the complete joint posterior distribution over spatially-varying basal traction and and ice softness parameters of an ice sheet model from observations of surface speed by using stochastic variational inference combined with natural gradient descent to find an approximating variational distribution.
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Variational Inference over Nonstationary Data Streams for Exponential Family Models
Mathematics, 2020 In many modern data analysis problems, the available data is not static but, instead, comes in a streaming fashion. Performing Bayesian inference on a data stream is challenging for several reasons.
Andrés R. Masegosa +4 more
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, 2015 We develop a general variational inference method that preserves dependency among the latent variables. Our method uses copulas to augment the families of distributions used in mean-field and structured approximations. Copulas model the dependency that is not captured by the original variational distribution, and thus the augmented variational family ...
Dustin Tran +2 more
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Variational inference for visual tracking [PDF]
2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings., 2003 The likelihood models used in probabilistic visual tracking applications are often complex non-linear and/or non-Gaussian functions, leading to analytically intractable inference. Solutions then require numerical approximation techniques, of which the particle filter is a popular choice.
Jaco Vermaak +2 more
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Variational Inference of Kalman Filter and Its Application in Wireless Sensor Networks
International Journal of Distributed Sensor Networks, 2013 An improved Kalman filter algorithm by using variational inference (VIKF) is proposed. With variational method, the joint posterior distribution of the states is approximately decomposed into several relatively independent posterior distributions.
Zijian Dong, Tiecheng Song
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