Results 31 to 40 of about 11,193 (260)
Variationally Inferred Sampling through a Refined Bound
Entropy, 2021 In this work, a framework to boost the efficiency of Bayesian inference in probabilistic models is introduced by embedding a Markov chain sampler within a variational posterior approximation.
Víctor Gallego, David Ríos Insua
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Proximity Variational Inference
CoRR, 2017 Variational inference is a powerful approach for approximate posterior inference. However, it is sensitive to initialization and can be subject to poor local optima. In this paper, we develop proximity variational inference (PVI). PVI is a new method for optimizing the variational objective that constrains subsequent iterates of the variational ...
Jaan Altosaar +2 more
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CoRR, 2016 NIPS 2016 Workshop, Advances in Approximate Bayesian ...
Michael Figurnov +2 more
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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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Boosting Variational Inference
CoRR, 2016 Variational inference (VI) provides fast approximations of a Bayesian posterior in part because it formulates posterior approximation as an optimization problem: to find the closest distribution to the exact posterior over some family of distributions.
Fangjian Guo +4 more
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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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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 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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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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