Intelligent edge computing based on machine learning for smart city Machine learningMECArtificial intelligenceStackelberg principle-subordinate game theoryADMMTo alleviate the huge computing pressure caused by the single mobile edge... ZL A,DC A,RL A,... - 《Future Generation Computer Systems》 被引...
machine learningoccupant profilerenewable resourcessmart gridThe smart grid will allow substantial electricity savings and peak demand savings by potentially supplying utility power for direct load management, the calculation in support of competitive pricing, and even the granular data required for energy ...
Machine learning systems can also analyze symptoms, genetic information, and other patient data to suggest tests for conditions such as cancer, diabetes, and heart disease. The key features of machine learning are the Automatic discovery of patterns Prediction of likely outcomes Creation of actionable...
Machine learned interatomic interaction potentials have enabled efficient and accurate molecular simulations of closed systems. However, external fields, which can greatly change the chemical structure and/or reactivity, have been seldom included in current machine learning models. This work proposes a univ...
to identify patterns and detect abnormalities that may not be visible to the human eye or that an overworked diagnostician might miss. Machine learning systems can also analyze symptoms, genetic information, and other patient data to suggest tests for conditions such as cancer, diabetes, and heart...
Having huge power grids successfully integrate sustainable energy sources requires a smart and flexible power grid management system. Such smart systems ha
Federated Reinforcement Learning: Linear Speedup Under Markovian Sampling Geogia Institute of Technology Orchestra: Unsupervised Federated Learning via Globally Consistent Clustering University of Michigan code Resilient and Communication Efficient Learning for Heterogeneous Federated Systems Michigan State University...
Most applications of machine learning in heterogeneous catalysis thus far have used black-box models to predict computable physical properties (descriptors), such as adsorption or formation energies, that can be related to catalytic performance (that is,
Semi-supervised machine learningaddresses the problem of not having enough labeled data to fully train a model. For instance, you might have large training data sets but don’t want to incur the time and cost of labeling the entire set. By using a combination of supervised and unsupervised me...
which promises to reach the accuracy of quantum mechanical computations at a substantially reduced computational cost. This Review will summarize the basic principles of the underlying machine learning methods, the data acquisition process and active learning procedures. We highlight multiple recent applicat...