0000000001011919

AUTHOR

Li Meng

showing 3 related works from this author

Comparison of the subjective satisfaction of the donor site morbidity : free radial forearm flap versus anterolateral thigh flap for reconstruction i…

2018

Background The purpose of the study was to compare the differences of the subjective satisfaction of the donor site morbidity between the free radial forearm flap (FRFF) and anterolateral thigh flap (ALTF) for tongue reconstruction. Material and Methods One hundred and nineteen patients underwent FRFF or ALTF reconstruction were retrospectively evaluated by a standardized self-established donor site morbidity questionnaire which included 5 domains, sensibility, movement disabilities, cosmetics, social activities and general impacts on the quality of life. Results The Cronbach’s coefficient alpha of the questionnaire was 0.707. The exploratory factor analysis revealed that the 5 items of the…

AdultMalemedicine.medical_specialtySurgical Flaps03 medical and health sciencesYoung Adult0302 clinical medicinePatient satisfactionPostoperative ComplicationsQuality of lifeCronbach's alphaTongueTongueSurveys and QuestionnairesmedicineHumansGeneral DentistryAgedRetrospective StudiesRadial forearm flapbusiness.industryResearchCancerReproducibility of ResultsRetrospective cohort study030206 dentistryAnterolateral thighMiddle AgedPlastic Surgery Proceduresmedicine.disease:CIENCIAS MÉDICAS [UNESCO]SurgeryTongue NeoplasmsForearmmedicine.anatomical_structureOtorhinolaryngologyThighPatient SatisfactionUNESCO::CIENCIAS MÉDICASQuality of LifeSurgeryFemaleMorbidityOral Surgerybusiness
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Improving the Diversity of Bootstrapped DQN by Replacing Priors With Noise

2022

Authors accepted manuscript Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Q-learning is one of the most well-known Reinforcement Learning algorithms. There have been tremendous efforts to develop this algorithm using neural networks. Bootstrapped Deep Q-Learning Network is amongst them. It utilizes multiple neural network heads to introduce diversity into Q-learning. Dive…

FOS: Computer and information sciencesComputer Science - Machine LearningVDP::Teknologi: 500Artificial Intelligence (cs.AI)Artificial IntelligenceControl and Systems EngineeringComputer Science - Artificial IntelligenceElectrical and Electronic EngineeringSoftwareMachine Learning (cs.LG)
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Expert Q-learning: Deep Reinforcement Learning with Coarse State Values from Offline Expert Examples

2022

In this article, we propose a novel algorithm for deep reinforcement learning named Expert Q-learning. Expert Q-learning is inspired by Dueling Q-learning and aims at incorporating semi-supervised learning into reinforcement learning through splitting Q-values into state values and action advantages. We require that an offline expert assesses the value of a state in a coarse manner using three discrete values. An expert network is designed in addition to the Q-network, which updates each time following the regular offline minibatch update whenever the expert example buffer is not empty. Using the board game Othello, we compare our algorithm with the baseline Q-learning algorithm, which is a…

FOS: Computer and information sciencesImitation LearningComputer Science - Machine LearningArtificial Intelligence (cs.AI)Deep LearningComputer Science - Artificial IntelligenceSemi-supervised LearningGeneral MedicineVDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550Reinforcement LearningMachine Learning (cs.LG)
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