Journal of chemical theory and computation
Jul 20, 2022
We introduce an unsupervised clustering algorithm to improve training efficiency and accuracy in predicting energies using molecular-orbital-based machine learning (MOB-ML). This work determines clusters via the Gaussian mixture model (GMM) in an ent...
IEEE transactions on neural networks and learning systems
Jul 6, 2022
Causal discovery from observational data is a fundamental problem in science. Though the linear non-Gaussian acyclic model (LiNGAM) has shown promising results in various applications, it still faces the following challenges in the data with multiple...
Medical decision making : an international journal of the Society for Medical Decision Making
Jun 23, 2022
PURPOSE: Metamodels are simplified approximations of more complex models that can be used as surrogates for the original models. Challenges in using metamodels for policy analysis arise when there are multiple correlated outputs of interest. We devel...
Many authors have been working on approaches that can be applied to social robots to allow a more realistic/comfortable relationship between humans and robots in the same space. This paper proposes a new navigation strategy for social environments by...
Computational intelligence and neuroscience
Jun 3, 2022
With the rapid development of computer graphics, 3D animation has been applied to all fields of people's lives, especially in the industries of film and television works, games, and entertainment. The wide application of animation technology makes it...
Neural networks : the official journal of the International Neural Network Society
Jun 2, 2022
Despite the successful use of Gaussian-binary restricted Boltzmann machines (GB-RBMs) and Gaussian-binary deep belief networks (GB-DBNs), little is known about their theoretical approximation capabilities to represent distributions of continuous rand...
Physical chemistry chemical physics : PCCP
Jun 1, 2022
There has been increasing attention in using machine learning technologies, such as neural networks (NNs) and Gaussian process regression (GPR), to model multi-dimensional potential energy surfaces (PESs). A PES constructed using NNs features high ac...
Momentous increase in the popularity of explainable machine learning models coupled with the dramatic increase in the use of synthetic data facilitates us to develop a cost-efficient machine learning model for fast intrusion detection and prevention ...
The development of computational modeling and simulation have immensely benefited the study of cardiac disease mechanisms and facilitated the optimal disease diagnosis and treatment design. The dynamic propagation of cardiac electrical signals are of...
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