Variational inference of ice shelf rheology with physics-informed machine learning
Sign Up to like & getrecommendations! Published in 2023 at "Journal of Glaciology"
DOI: 10.1017/jog.2023.8
Abstract: Floating ice shelves that fringe the coast of Antarctica resist the flow of grounded ice into the ocean. One of the key factors governing the amount of flow resistance an ice shelf provides is the… read more here.
Keywords: rigidity; ice; physics; rheology ... See more keywords
Variational inference as an alternative to MCMC for parameter estimation and model selection
Sign Up to like & getrecommendations! Published in 2022 at "Publications of the Astronomical Society of Australia"
DOI: 10.1017/pasa.2021.64
Abstract: Abstract Most applications of Bayesian Inference for parameter estimation and model selection in astrophysics involve the use of Monte Carlo techniques such as Markov Chain Monte Carlo (MCMC) and nested sampling. However, these techniques are… read more here.
Keywords: model selection; estimation; variational inference; inference ... See more keywords
Stochastic Variational Inference for Bayesian Phylogenetics: A Case of CAT Model
Sign Up to like & getrecommendations! Published in 2019 at "Molecular Biology and Evolution"
DOI: 10.1093/molbev/msz020
Abstract: Abstract The pattern of molecular evolution varies among gene sites and genes in a genome. By taking into account the complex heterogeneity of evolutionary processes among sites in a genome, Bayesian infinite mixture models of… read more here.
Keywords: inference; inference bayesian; stochastic variational; variational inference ... See more keywords
Transformer-Based Online Bayesian Neural Networks for Grant-Free Uplink Access in CRAN With Streaming Variational Inference
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Internet of Things Journal"
DOI: 10.1109/jiot.2021.3113679
Abstract: We propose a model-driven Bayesian deep learning framework for multiple access uplink systems in Multiuser MIMO systems. Utilizing tools from Streaming Variational Inference, we combine graphical models with neural networks to enable fast online machine… read more here.
Keywords: neural networks; streaming variational; transformer based; access ... See more keywords
VariFi: Variational Inference for Indoor Pedestrian Localization and Tracking Using IMU and WiFi RSS
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Internet of Things Journal"
DOI: 10.1109/jiot.2022.3232740
Abstract: Accurate indoor pedestrian localization and tracking are crucial in many practical applications. One efficient yet low-cost sensing scheme is the integration of inertial measurement unit and WiFi received signal strength (RSS) due to the popularity… read more here.
Keywords: localization; indoor pedestrian; pedestrian localization; rss ... See more keywords
Advanced NOMA Receivers From a Unified Variational Inference Perspective
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Journal on Selected Areas in Communications"
DOI: 10.1109/jsac.2020.3018834
Abstract: Non-orthogonal multiple access (NOMA) on shared resources has been identified as a promising technology in 5G to improve resource efficiency and support massive access in all kinds of transmission modes. Power domain and code domain… read more here.
Keywords: unified variational; noma receivers; inference perspective; perspective ... See more keywords
Design of Communication Systems Using Deep Learning: A Variational Inference Perspective
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Transactions on Cognitive Communications and Networking"
DOI: 10.1109/tccn.2020.2985371
Abstract: Recent research in the design of end to end communication system using deep learning has produced models which can outperform traditional communication schemes. Most of these architectures leveraged autoencoders to design the encoder at the… read more here.
Keywords: communication; variational inference; communication systems; using deep ... See more keywords
Bayesian K-SVD Using Fast Variational Inference
Sign Up to like & getrecommendations! Published in 2017 at "IEEE Transactions on Image Processing"
DOI: 10.1109/tip.2017.2681436
Abstract: Recent work in signal processing in general and image processing in particular deals with sparse representation related problems. Two such problems are of paramount importance: an overriding need for designing a well-suited overcomplete dictionary containing… read more here.
Keywords: bayesian svd; sparse representation; variational inference; svd using ... See more keywords
Bayesian Estimation of Inverted Beta Mixture Models With Extended Stochastic Variational Inference for Positive Vector Classification.
Sign Up to like & getrecommendations! Published in 2022 at "IEEE transactions on neural networks and learning systems"
DOI: 10.1109/tnnls.2022.3213518
Abstract: The finite inverted beta mixture model (IBMM) has been proven to be efficient in modeling positive vectors. Under the traditional variational inference framework, the critical challenge in Bayesian estimation of the IBMM is that the… read more here.
Keywords: inverted beta; beta mixture; framework; bayesian estimation ... See more keywords
Gradient Regularization as Approximate Variational Inference
Sign Up to like & getrecommendations! Published in 2021 at "Entropy"
DOI: 10.3390/e23121629
Abstract: 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. The… read more here.
Keywords: regularization approximate; approximate variational; gradient regularization; variational inference ... See more keywords
A Modified Stein Variational Inference Algorithm with Bayesian and Gradient Descent Techniques
Sign Up to like & getrecommendations! Published in 2022 at "Symmetry"
DOI: 10.3390/sym14061188
Abstract: This paper introduces a novel variational inference (VI) method with Bayesian and gradient descent techniques. To facilitate the approximation of the posterior distributions for the parameters of the models, the Stein method has been used… read more here.
Keywords: gradient descent; descent techniques; bayesian gradient; inference ... See more keywords