Results 231 to 240 of about 114,557 (285)

A Deep Learning-Enhanced Adaptive Kalman Filter with Multi-Scale Temporal Attention for Airborne Gravity Denoising. [PDF]

Sensors (Basel)
Li L, Liu J, Ma G, Jiang Z.
europepmc   +1 more source

Subtyping insomnia disorder with a population graph attention autoencoder: revealing two distinct biotypes. [PDF]

Front Neurosci
Zhang H, Deng H, Zhai Y, Zhang J, Zhao Z, Gong L.   +5 more
europepmc   +1 more source

fastMETA: a fast and efficient tool for multivariate meta-analysis of GWAS. [PDF]

Front Genet
Manios GA, Kandylas D, Kylonis A, Bagos PG, Kontou PI.   +4 more
europepmc   +1 more source

MUSIC-Based Multi-Channel Forward-Scatter Radar Using OFDM Signals. [PDF]

Sensors (Basel)
Qin Y, Ajorloo A, Colone F.
europepmc   +1 more source

Diagonal Acceleration for Covariance Matrix Adaptation Evolution Strategies

Diagonal Acceleration for Covariance Matrix Adaptation Evolution Strategies
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Toward a Matrix-Free Covariance Matrix Adaptation Evolution Strategy

IEEE Transactions on Evolutionary Computation, 2020
In this paper, we discuss a method for generating new individuals such that their mean vector and the covariance matrix are defined by formulas analogous to the covariance matrix adaptation evolution strategy (CMA-ES). In contrast to CMA-ES, which generates new individuals using multivariate Gaussian distribution with an explicitly defined covariance ...
Jaroslaw Arabas, Dariusz Jagodzinski
openaire   +3 more sources

Simplify Your Covariance Matrix Adaptation Evolution Strategy

IEEE Transactions on Evolutionary Computation, 2017
The standard covariance matrix adaptation evolution strategy (CMA-ES) comprises two evolution paths, one for the learning of the mutation strength and one for the rank-1 update of the covariance matrix. In this paper, it is shown that one can approximately transform this algorithm in such a manner that one of the evolution paths and the covariance ...
Hans-Georg Beyer, Bernhard Sendhoff
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Fast Covariance Matrix Adaptation for Large-Scale Black-Box Optimization

IEEE Transactions on Cybernetics, 2020
Covariance matrix adaptation evolution strategy (CMA-ES) is a successful gradient-free optimization algorithm. Yet, it can hardly scale to handle high-dimensional problems. In this paper, we propose a fast variant of CMA-ES (Fast CMA-ES) to handle large-scale black-box optimization problems.
Zhenhua Li, Qingfu Zhang, Xi Lin, Hui-Ling Zhen   +3 more
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Covariance Matrix Adaptation Particle Filter

2014 Iranian Conference on Intelligent Systems (ICIS), 2014
Based on Covariance Matrix Adaptation Evolution Strategy (CMA-ES) and Particle Filter (PF), an intelligent particle filter, namely Covariance matrix adaptation particle filter (CMA-PF), is proposed in this paper. Search abilities of CMA-ES are utilized within proposed method to perform Prior Regularization, which helps the particle filter to generate ...
S. Mostapha Kalami Heris, Hamid Khaloozadeh   +1 more
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