论文题目:Knowledge-Driven Service OffloadingDecision forVehicular Edge Computing: ADeep Reinforcement LearningApproach(IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2018 Q1 5年4.864) 摘要--“智能车辆架构-车联网(IOV)课提供各种服务,虽然
Deep learning algorithmsUrbanizationRegional cultural vibrancyBased on spatial interaction theory and relevant economic paradigms, in conjunction with the principles of crafting an evaluation framework, this study meticulously selects socio-economic impact indicators that adeptly characterize the course of ...
To make full use of the advantages of traditional physical models and machine learning, we construct a novel LST retrieval method based on model-data-knowledge-driven and deep learning, called the MDK-DL method. First, we perform geophysical logical reasoning (GLR) based on RTE with the help ...
Deep learning-based video quality assessment (deep VQA) has demonstrated significant potential in surpassing conventional metrics, with promising improvements in terms of correlation with human perception. However, the practical deployment of such deep VQA models is often limited due to their high ...
Driven by the fast advancements of deep learning techniques, deep neural network has been recently adopted to design knowledge tracing (KT) models for achieving better prediction performance. However, the lack of interpretability of these models has painfully impeded their practical applications, as the...
Deep learning-based molecular generation has extensive applications in many fields, particularly drug discovery. However, the majority of current deep generative models are ligand-based and do not consider chemical knowledge in the molecular generation process, often resulting in a relatively low success...
Deep Reinforcement Learning (RL) is increasingly used for developing financial trading agents for a wide range of tasks. However, optimizing deep RL agents is notoriously difficult and unstable, especially in noisy financial environments, significantly hindering the performance of trading agents. In this...
Due to the abundant neurophysiological information in the electroencephalogram (EEG) signal, EEG signals integrated with deep learning methods have gained substantial traction across numerous real-world tasks. However, the development of supervised learning methods based on EEG signals has been hindered by...
For example, prerequisite-driven deep knowledge tracing (PDKT-C) [8], structure-based knowledge tracing (SKT) [9], PQRLKA [10], and AKT [4] highlight the importance of knowledge structures or the need to learn embedding representations with plentiful domain knowledge but assess learners’ ...
M. Chemical pressure-driven enhancement of the hydrogen evolving activity of Ni2P from nonmetal surface doping interpreted via machine learning. J. Am. Chem. Soc. 140, 4678–4683 (2018). Article CAS PubMed Google Scholar Wexler, R. B., Qiu, T. & Rappe, A. M. Automatic prediction of...