Abstract: Inverse reinforcement learning (IRL) is a technique for automatic reward acquisition, however, it is difficult to apply to high-dimensional problems with unknown dynamics. This article ...

Bayesian regression with linear basis function models. Introduction to Bayesian linear regression. Implementation with plain NumPy and scikit-learn. See also PyMC3 implementation. Gaussian processes.

The Multi-Output Gaussian Process Toolkit is a Python toolkit for multichannel time series analysis. MOGPTK implements multioutput Gaussian process models with different covariance architectures, ...

The generation of synthetic market data is widely seen as one of the most promising applications of sophisticated artificial intelligence models, such as generative adversarial networks (GANs) and ...

State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang Liaoning 110819, China College of Information Science and Engineering, Northeastern University, ...

Many complex data analysis problems within and beyond the scientific domain involve discovering graphical structures and functional relationships within data. Nonlinear variance decomposition with ...

Abstract: Reinforcement learning (RL) still suffers from the problem of sample inefficiency and struggles with the exploration issue, particularly in situations with long-delayed rewards, sparse ...

Center for Superfunctional Materials, Department of Chemistry, Ulsan National Institute of Science and Technology, 50 UNIST-gil, Ulsan 44919, Korea Article Views are the COUNTER-compliant sum of full ...

The rapid development of ultrasound medical imaging technology has greatly broadened the scope of application of ultrasound, which has been widely used in the screening, diagnosis of breast diseases ...