Results 111 to 120 of about 5,051,769 (292)
Variational Information Bottleneck for Unsupervised Clustering: Deep Gaussian Mixture Embedding
Entropy, 2020 In this paper, we develop an unsupervised generative clustering framework that combines the variational information bottleneck and the Gaussian mixture model.
Yiğit Uğur +2 more
doaj +1 more source
Stochastic Collapsed Variational Inference for Sequential Data
, 2015 Stochastic variational inference for collapsed models has recently been successfully applied to large scale topic modelling. In this paper, we propose a stochastic collapsed variational inference algorithm in the sequential data setting. Our algorithm is
Blunsom, Phil, Wang, Pengyu
core
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Glioma recurrence severely impacts patient prognosis, with current treatments showing limited efficacy. Traditional methods struggle to analyze recurrence mechanisms due to challenges in assessing tumor heterogeneity, spatial dynamics, and gene networks.
Lei Qiu +10 more
wiley +1 more source
Toward Variational Structural Learning of Bayesian Networks
IEEE AccessThis study presents a novel variational framework for structural learning in Bayesian networks (BNs), addressing the key limitation of existing Bayesian methods: their lack of scalability to large graphs with many variables.
Andres R. Masegosa, Manuel Gomez-Olmedo
doaj +1 more source
On some provably correct cases of variational inference for topic models
, 2015 Variational inference is a very efficient and popular heuristic used in various forms in the context of latent variable models. It's closely related to Expectation Maximization (EM), and is applied when exact EM is computationally infeasible.
Awasthi, Pranjal, Risteski, Andrej
core
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Background SOX1 antibody‐positive paraneoplastic neurological syndromes (PNS) exhibit significant population‐specific clinical heterogeneity. While Western cohorts predominantly manifest Lambert‐Eaton myasthenic syndrome (65%–80%), comprehensive clinical characterization and treatment response data in Asian populations remain critically ...
Jin‐Long Ye +11 more
wiley +1 more source
Boosting Variational Inference: an Optimization Perspective
, 2018 Variational inference is a popular technique to approximate a possibly intractable Bayesian posterior with a more tractable one. Recently, boosting variational inference has been proposed as a new paradigm to approximate the posterior by a mixture of ...
Ghosh, Joydeep +3 more
core
Universal Boosting Variational Inference
CoRR, 2019 Boosting variational inference (BVI) approximates an intractable probability density by iteratively building up a mixture of simple component distributions one at a time, using techniques from sparse convex optimization to provide both computational scalability and approximation error guarantees.
Trevor Campbell, Xinglong Li
openaire +3 more sources
CX3CL1 in Early Detection of Alzheimer's Disease: Plasma Dynamics Across Age and Disease Stages
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Backgrounds Alzheimer's disease (AD) is characterized by amyloid‐beta plaques, tau tangles, and neuroinflammation. C‐X3‐C motif chemokine ligand 1 (CX3CL1, also known as fractalkine), a neuroimmune chemokine implicated in AD pathogenesis, shows inconsistent alterations in plasma/serum across studies.
Ling Wang +6 more
wiley +1 more source
Variational Bayesian Variable Selection for High-Dimensional Hidden Markov Models
MathematicsThe Hidden Markov Model (HMM) is a crucial probabilistic modeling technique for sequence data processing and statistical learning that has been extensively utilized in various engineering applications.
Yao Zhai +3 more
doaj +1 more source