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
doaj +6 more sources
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
doaj +2 more sources
A scalable variational inference approach for increased mixed-model association power. [PDF]
Nat GenetThe rapid growth of modern biobanks is creating new opportunities for large-scale genome-wide association studies (GWASs) and the analysis of complex traits. However, performing GWASs on millions of samples often leads to trade-offs between computational
Loya H +3 more
europepmc +2 more sources
A representation learning model based on variational inference and graph autoencoder for predicting lncRNA-disease associations. [PDF]
BMC Bioinformatics, 2021 Numerous studies have demonstrated that long non-coding RNAs are related to plenty of human diseases. Therefore, it is crucial to predict potential lncRNA-disease associations for disease prognosis, diagnosis and therapy.
Shi Z, Zhang H, Jin C, Quan X, Yin Y.
europepmc +2 more sources
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
doaj +2 more sources
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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Forward-backward Gaussian variational inference via JKO in the Bures-Wasserstein Space [PDF]
International Conference on Machine Learning, 2023 Variational inference (VI) seeks to approximate a target distribution $\pi$ by an element of a tractable family of distributions. Of key interest in statistics and machine learning is Gaussian VI, which approximates $\pi$ by minimizing the Kullback ...
Michael Diao +3 more
semanticscholar +1 more source
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
doaj +1 more source
Provable convergence guarantees for black-box variational inference [PDF]
Neural Information Processing Systems, 2023 Black-box variational inference is widely used in situations where there is no proof that its stochastic optimization succeeds. We suggest this is due to a theoretical gap in existing stochastic optimization proofs: namely the challenge of gradient ...
Justin Domke +2 more
semanticscholar +1 more source