Results 51 to 60 of about 11,193 (260)
A primer on Variational Laplace (VL)
NeuroImage, 2023 This article details a scheme for approximate Bayesian inference, which has underpinned thousands of neuroimaging studies since its introduction 15 years ago.
Peter Zeidman, Karl Friston, Thomas Parr
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Pediatric Blood &Cancer, EarlyView.ABSTRACT Background Neurotoxicity is a rare, often dose‐limiting adverse effect of methotrexate (MTX) therapy that disproportionally affects Latino children. Factors contributing to the observed disparity are not well understood. This study leveraged admixture mapping to identify genetic regions associated with MTX‐related neurotoxicity susceptibility ...
Rachel D. Harris +24 more
wiley +1 more source
AdamB: Decoupled Bayes by Backprop With Gaussian Scale Mixture Prior
IEEE Access, 2022 Overfitting of neural networks to training data is one of the most significant problems in machine learning. Bayesian neural networks (BNNs) are known to be robust against overfitting owing to their ability to model parameter uncertainty.
Keigo Nishida, Makoto Taiji
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Pediatric Blood &Cancer, EarlyView.ABSTRACT Background Sickle cell disease (SCD) has undergone major changes in the last decades. Its prevalence has been steadily increasing and numerous advances have been made in the management of the disease. However, the effect in real‐life setting of these major changes is unknown, particularly in a Canadian environment. Procedure We aimed to assess
Maude Cigna +16 more
wiley +1 more source
Quantized Variational Inference
CoRR, 2020 We present Quantized Variational Inference, a new algorithm for Evidence Lower Bound maximization. We show how Optimal Voronoi Tesselation produces variance free gradients for ELBO optimization at the cost of introducing asymptotically decaying bias. Subsequently, we propose a Richardson extrapolation type method to improve the asymptotic bound.
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f-Divergence Variational Inference
CoRR, 2020 This paper introduces the $f$-divergence variational inference ($f$-VI) that generalizes variational inference to all $f$-divergences. Initiated from minimizing a crafty surrogate $f$-divergence that shares the statistical consistency with the $f$-divergence, the $f$-VI framework not only unifies a number of existing VI methods, e.g.
Neng Wan, Dapeng Li, Naira Hovakimyan
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Therapeutic Apheresis and Dialysis, EarlyView.ABSTRACT Background Maintenance hemodialysis (MHD) patients frequently suffer from frailty, characterized by reduced physical function and poor prognosis. Myokines, such as myonectin, secreted by muscle, are emerging regulators of systemic health. This study investigated the relationship between serum myonectin, adipokines (adiponectin, omentin), and ...
Kenichi Kono +7 more
wiley +1 more source
Automatic structured variational inference [PDF]
, 2020 Contains fulltext : 233662.pdf (Publisher’s version ) (Open Access)
Ambrogioni, L. +6 more
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Therapeutic Apheresis and Dialysis, EarlyView.ABSTRACT Background Chronic micro‐inflammation in patients with end‐stage renal disease (ESRD) is a significant driver of cardiovascular complications and diminished quality of life. While standard hemodialysis (SHD) effectively manages small‐molecule clearance, its ability to remove medium‐to‐large uremic toxins—the primary catalysts of systemic ...
Hongwei Zuo +5 more
wiley +1 more source
Disentangling Reasoning Factors for Natural Language Inference
Big Data Mining and AnalyticsNatural Language Inference (NLI) seeks to deduce the relations of two texts: a premise and a hypothesis. These two texts may share similar or different basic contexts, while three distinct reasoning factors emerge in the inference from premise to ...
Xixi Zhou +5 more
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