Zobrazeno 1 - 10
of 52
pro vyhledávání: '"Lei, Mengying"'
Autor:
Lei, Mengying, Sun, Lijun
Real-world datasets often contain missing or corrupted values. Completing multidimensional tensor-structured data with missing entries is essential for numerous applications. Smoothness-constrained low-rank factorization models have shown superior pe
Externí odkaz:
http://arxiv.org/abs/2412.07041
Autor:
Lei, Mengying, Sun, Lijun
Bayesian optimization (BO) primarily uses Gaussian processes (GP) as the key surrogate model, mostly with a simple stationary and separable kernel function such as the squared-exponential kernel with automatic relevance determination (SE-ARD). Howeve
Externí odkaz:
http://arxiv.org/abs/2302.14510
Probabilistic modeling of multidimensional spatiotemporal data is critical to many real-world applications. As real-world spatiotemporal data often exhibits complex dependencies that are nonstationary and nonseparable, developing effective and comput
Externí odkaz:
http://arxiv.org/abs/2208.09978
Spatiotemporal kriging is an important application in spatiotemporal data analysis, aiming to recover/interpolate signals for unsampled/unobserved locations based on observed signals. The principle challenge for spatiotemporal kriging is how to effec
Externí odkaz:
http://arxiv.org/abs/2109.12144
Publikováno v:
Bayesian Analysis (2024)
As a regression technique in spatial statistics, the spatiotemporally varying coefficient model (STVC) is an important tool for discovering nonstationary and interpretable response-covariate associations over both space and time. However, it is diffi
Externí odkaz:
http://arxiv.org/abs/2109.00046
Publikováno v:
In Journal of Drug Delivery Science and Technology May 2024 95
Publikováno v:
IEEE Transactions on Intelligent Transportation Systems (2022)
Spatiotemporal traffic time series (e.g., traffic volume/speed) collected from sensing systems are often incomplete with considerable corruption and large amounts of missing values, preventing users from harnessing the full power of the data. Missing
Externí odkaz:
http://arxiv.org/abs/2104.14936
Publikováno v:
In Food and Chemical Toxicology February 2024 184
Autor:
Li, Yang, Lei, Mengying, Zhang, Xianrui, Cui, Weigang, Guo, Yuzhu, Huang, Ting-Wen, Wei, Hua-Liang
Decoding EEG signals of different mental states is a challenging task for brain-computer interfaces (BCIs) due to nonstationarity of perceptual decision processes. This paper presents a novel boosted convolutional neural networks (ConvNets) decoding
Externí odkaz:
http://arxiv.org/abs/1810.10353
Objective: This study proposes a new parametric TF (time frequency) CGC (conditional Granger causality) method for high precision connectivity analysis over time and frequency in multivariate coupling nonstationary systems, and applies it to scalp an
Externí odkaz:
http://arxiv.org/abs/1810.09119