One of the current hot research topics is the combination of two of the most recent technological breakthroughs: machine learning and quantum computing.

Feature extraction is a critical component of signal processing which aims to extract useful information from signals. Over the past several years, an increasing number of researchers have chosen to ...

This study thus aimed to develop an ensemble model based on STL decomposition, a machine learning model, and an ensemble method to improve prediction of the Yellow River inflow into the sea under ...

Article citations More>> Wang, X. and Zhang, J. (2017) Complex Variational Mode Decomposition for Signal Analysis. Journal of Signal Processing Advances, 22, 290-302. has been cited by the following ...

Finally, we apply XGBoost, a robust machine learning algorithm, to predict the entire reflectance spectra from the reduced data set. The combination of Bayesian optimization for data selection, ...

Converting all inputs into string representations enables general-purpose regression for Bayesian Optimization across diverse tasks like synthetic, combinatorial, and hyperparameter optimization.

In conclusion, tensor networks offer a breakthrough in addressing the efficiency-interpretability challenge in artificial intelligence, especially in quantum-inspired machine learning.

Tensor completion is a vital task in multi-dimensional signal processing and machine learning. To recover the missing data in a tensor, various low-rank structures of a tensor can be assumed, and ...