X Liu, X Sang, J Chang, Y Zheng - Water Resources Management, 2021 - Springer
The water supply in megacities can be affected by the living habits and population mobility, so the fluctuation degree of daily water supply data is acute, which presents a great …
We propose an unsupervised machine-learning checkpoint-restart (CR) algorithm for particle-in-cell (PIC) algorithms using Gaussian mixtures (GM). The algorithm compresses …
We propose an Anderson Acceleration (AA) scheme for the adaptive Expectation- Maximization (EM) algorithm for unsupervised learning a finite mixture model from …
Y Wu, Z Chen, H Guo, J Li, H Yi, J Yu… - Journal of the Optical …, 2024 - opg.optica.org
Dynamic fluorescence molecular tomography (DFMT) is a promising imaging method that can furnish three-dimensional information regarding the absorption, distribution, and …
Recommender Systems received a big push forward with the adoption of the technology by Amazon. com at the end of the 1990's. Their own implementation is based on the similarities …
The demand for high-quality customized products compels manufacturers to adopt batch production. With the ability to accurately estimate batch production yield rates in advance …
C Yu, M Xu, M Pu, SS Zhang - 2024 - opg.optica.org
Freeform optics has emerged as a significant design tool over the past decade. Nonrotationally symmetric optical surfaces enable high-performance imaging systems to …
M Papež, T Pevný, V Šmídl - arxiv preprint arxiv:2110.04776, 2021 - arxiv.org
Traditional methods for unsupervised learning of finite mixture models require to evaluate the likelihood of all components of the mixture. This becomes computationally prohibitive …