Results 21 to 30 of about 5,051,769 (292)
Learning on Large-scale Text-attributed Graphs via Variational Inference [PDF]
International Conference on Learning Representations, 2022 This paper studies learning on text-attributed graphs (TAGs), where each node is associated with a text description. An ideal solution for such a problem would be integrating both the text and graph structure information with large language models and ...
Jianan Zhao +7 more
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Reliable amortized variational inference with physics-based latent distribution correction [PDF]
Geophysics, 2022 Bayesian inference for high-dimensional inverse problems is computationally costly and requires selecting a suitable prior distribution. Amortized variational inference addresses these challenges by pretraining a neural network that acts as a surrogate ...
Ali Siahkoohi +3 more
semanticscholar +1 more source
Sampling the Variational Posterior with Local Refinement
Entropy, 2021 Variational inference is an optimization-based method for approximating the posterior distribution of the parameters in Bayesian probabilistic models. A key challenge of variational inference is to approximate the posterior with a distribution that is ...
Marton Havasi +4 more
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Amortized Variational Inference: A Systematic Review [PDF]
Journal of Artificial Intelligence Research, 2022 The core principle of Variational Inference (VI) is to convert the statistical inference problem of computing complex posterior probability densities into a tractable optimization problem. This property enables VI to be faster than several sampling-based
Ankush Ganguly +2 more
semanticscholar +1 more source
International Journal of Interactive Multimedia and Artificial Intelligence, 2022 The finite invert Beta-Liouville mixture model (IBLMM) has recently gained some attention due to its positive data modeling capability. Under the conventional variational inference (VI) framework, the analytically tractable solution to the optimization ...
Yongfa Ling +4 more
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HypoSVI: Hypocenter inversion with Stein variational inference and Physics Informed Neural Networks [PDF]
arXiv.org, 2021 High resolution earthquake hypocentral locations are of critical importance for understanding the regional context driving seismicity. We introduce a scheme to reliably approximate a hypocenter posterior in a continuous domain that relies on recent ...
Jonathan D. Smith +3 more
semanticscholar +1 more source
Evaluating probabilistic programming and fast variational Bayesian inference in phylogenetics [PDF]
PeerJ, 2019 Recent advances in statistical machine learning techniques have led to the creation of probabilistic programming frameworks. These frameworks enable probabilistic models to be rapidly prototyped and fit to data using scalable approximation methods such ...
Mathieu Fourment, Aaron E. Darling
doaj +2 more sources
Variational Inference: A Review for Statisticians [PDF]
arXiv.org, 2016 One of the core problems of modern statistics is to approximate difficult-to-compute probability densities. This problem is especially important in Bayesian statistics, which frames all inference about unknown quantities as a calculation involving the ...
D. Blei, A. Kucukelbir, Jon D. McAuliffe
semanticscholar +1 more source
Gauging Variational Inference [PDF]
Journal of Statistical Mechanics: Theory and Experiment, 2017 Abstract Computing of partition function is the most important statistical inference task arising in applications of graphical models (GM). Since it is computationally intractable, approximate methods have been used in practice, where mean-field (MF) and belief propagation (BP) are arguably the most popular and successful approaches ...
Sungsoo Ahn +2 more
openaire +3 more sources
Variational Inference MPC using Tsallis Divergence [PDF]
Robotics: Science and Systems, 2021 In this paper, we provide a generalized framework for Variational Inference-Stochastic Optimal Control by using thenon-extensive Tsallis divergence.
Ziyi Wang +6 more
semanticscholar +1 more source