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Geometric Variational Inference [PDF]
Entropy, 2021 Efficiently accessing the information contained in non-linear and high dimensional probability distributions remains a core challenge in modern statistics.
Philipp Frank +2 more
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Gradient Regularization as Approximate Variational Inference [PDF]
Entropy, 2021 We developed Variational Laplace for Bayesian neural networks (BNNs), which exploits a local approximation of the curvature of the likelihood to estimate the ELBO without the need for stochastic sampling of the neural-network weights.
Ali Unlu, Laurence Aitchison
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Variational Inference via Rényi Bound Optimization and Multiple-Source Adaptation [PDF]
Entropy, 2023 Variational inference provides a way to approximate probability densities through optimization. It does so by optimizing an upper or a lower bound of the likelihood of the observed data (the evidence).
Dana Zalman (Oshri), Shai Fine
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Advances in Variational Inference [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019 Many modern unsupervised or semi-supervised machine learning algorithms rely on Bayesian probabilistic models. These models are usually intractable and thus require approximate inference. Variational inference (VI) lets us approximate a high-dimensional Bayesian posterior with a simpler variational distribution by solving an optimization problem.
Judith Bütepage +2 more
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Facial expression recognition via variational inference [PDF]
Scientific ReportsFacial expressions in the wild are rarely discrete; they often manifest as compound emotions or subtle variations that challenge the discriminative capabilities of conventional models.
Gang Lv, JunLing Zhang, Chiki Tsoi
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Lightweight Deep Neural Network Embedded with Stochastic Variational Inference Loss Function for Fast Detection of Human Postures [PDF]
Entropy, 2023 Fusing object detection techniques and stochastic variational inference, we proposed a new scheme for lightweight neural network models, which could simultaneously reduce model sizes and raise the inference speed.
Feng-Shuo Hsu +7 more
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Variational Inference for Nonlinear Structural Identification [PDF]
Journal of Applied and Computational Mechanics, 2021 Research interest in predictive modeling within the structural engineering community has recently been focused on Bayesian inference methods, with particular emphasis on analytical and sampling approaches. In this study, we explore variational inference,
Alana Lund +2 more
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Overview of Research on Bayesian Inference and Parallel Tempering [PDF]
Jisuanji kexue, 2023 Bayesian inference is one of the main problems in statistics.It aims to update the prior knowledge of the probability distribution model based on the observation data.For the posterior probability that cannot be observed or is difficult to directly ...
ZHAN Jin, WANG Xuefei, CHENG Yurong, YUAN Ye
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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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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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