Zobrazeno 1 - 10
of 54
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