Results 41 to 50 of about 114,074 (272)

Sparse Bayesian Learning Based Direction-of-Arrival Estimation under Spatially Colored Noise Using Acoustic Hydrophone Arrays

Journal of Marine Science and Engineering, 2021
Direction-of-arrival (DOA) estimation in a spatially isotropic white noise background has been widely researched for decades. However, in practice, such as underwater acoustic ambient noise in shallow water, the ambient noise can be spatially colored ...
Guolong Liang, Zhibo Shi, Longhao Qiu, Sibo Sun, Tian Lan   +4 more
doaj   +1 more source

Correlated Sparse Bayesian Learning for Recovery of Block Sparse Signals With Unknown Borders

IEEE Open Journal of Signal Processing
We consider the problem of recovering complex-valued block sparse signals with unknown borders. Such signals arise naturally in numerous applications. Several algorithms have been developed to solve the problem of unknown block partitions.
Didem Dogan, Geert Leus
doaj   +1 more source

Sparse Stochastic Inference for Latent Dirichlet allocation

, 2012
We present a hybrid algorithm for Bayesian topic models that combines the efficiency of sparse Gibbs sampling with the scalability of online stochastic inference.
Blei, David, Hoffman, Matt, Mimno, David
core   +2 more sources

Cramer Rao-Type Bounds for Sparse Bayesian Learning

, 2012
In this paper, we derive Hybrid, Bayesian and Marginalized Cram\'{e}r-Rao lower bounds (HCRB, BCRB and MCRB) for the single and multiple measurement vector Sparse Bayesian Learning (SBL) problem of estimating compressible vectors and their prior ...
Murthy, Chandra R., Prasad, Ranjitha
core   +1 more source

Sparse Bayesian Learning via Stepwise Regression

CoRR, 2021
Sparse Bayesian Learning (SBL) is a powerful framework for attaining sparsity in probabilistic models. Herein, we propose a coordinate ascent algorithm for SBL termed Relevance Matching Pursuit (RMP) and show that, as its noise variance parameter goes to zero, RMP exhibits a surprising connection to Stepwise Regression.
Sebastian E. Ament, Carla P. Gomes
openaire   +3 more sources

Active Learning‐Accelerated Discovery of Fibrous Hydrogels with Tissue‐Mimetic Viscoelasticity

Advanced Functional Materials, EarlyView.
Active learning accelerates the design of fibrous hydrogels that mimic the viscoelasticity of native tissues. By integrating multi‐objective optimization and closed‐loop experimentation, this approach efficiently identifies optimal formulations from thousands of possibilities and decouples elasticity and viscosity. The resulting hydrogels offer tunable
Zhengkun Chen, Zheyuan Miao, Gary Tom, Austin H. Cheng, Elizaveta Gusarova, Benjamen Zondag, Tianyi Wu, Dianoosh Kalhori, Boris Hinz, Milad Abolhasani, Alán Aspuru‐Guzik, Eugenia Kumacheva   +11 more
wiley   +1 more source

Impulsive Noise Mitigation in Powerline Communications Using Sparse Bayesian Learning

, 2013
Additive asynchronous and cyclostationary impulsive noise limits communication performance in OFDM powerline communication (PLC) systems. Conventional OFDM receivers assume additive white Gaussian noise and hence experience degradation in communication ...
Evans, Brian L., Lin, Jing, Nassar, Marcel   +2 more
core   +1 more source

Machine Learning‐Assisted Inverse Design of Soft and Multifunctional Hybrid Liquid Metal Composites

Advanced Functional Materials, EarlyView.
A machine learning framework is presented for inverse design of synthesizable multifunctional composites containing both liquid metal and solid inclusions. By integrating physics‐based modeling, data‐driven prediction, and Bayesian optimization, the approach enables intelligent design of experiments to identify optimal compositions and realize these ...
Lijun Zhou, Yunsik Ohm, Ren‐Mian Chin, Olivia Kerr, Krithika Manohar, Mohammad H. Malakooti   +5 more
wiley   +1 more source

Sparse Linear Identifiable Multivariate Modeling [PDF]

, 2011
In this paper we consider sparse and identifiable linear latent variable (factor) and linear Bayesian network models for parsimonious analysis of multivariate data.
Aapo Hyvärinen, Dtu Informatics, Ole Winther, Ricardo Henao, Richard Petersens Plads   +4 more
core   +1 more source

Leveraging Crowdsourcing Data For Deep Active Learning - An Application: Learning Intents in Alexa

, 2018
This paper presents a generic Bayesian framework that enables any deep learning model to actively learn from targeted crowds. Our framework inherits from recent advances in Bayesian deep learning, and extends existing work by considering the targeted ...
Damianou, Andreas, Drake, Thomas, Maarek, Yoelle, Yang, Jie   +3 more
core   +1 more source