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A self-supervised method for treatment recommendation in sepsis Research Articles
Sihan Zhu, Jian Pu,jianpu@fudan.edu.cn
Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 7, Pages 926-939 doi: 10.1631/FITEE.2000127
Keywords: 治疗推荐;脓毒症;自监督学习;强化学习;电子病历
Pre-training with asynchronous supervised learning for reinforcement learning based autonomous driving Research Articles
Yunpeng Wang, Kunxian Zheng, Daxin Tian, Xuting Duan, Jianshan Zhou,ypwang@buaa.edu.cn,zhengkunxian@buaa.edu.cn,dtian@buaa.edu.cn,duanxuting@buaa.edu.cn
Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 5, Pages 615-766 doi: 10.1631/FITEE.1900637
Keywords: 自主驾驶;自动驾驶车辆;强化学习;监督学习
Self-supervised graph learning with target-adaptive masking for session-based recommendation Research Article
Yitong WANG, Fei CAI, Zhiqiang PAN, Chengyu SONG,wangyitong20@nudt.edu.cn,caifei08@nudt.edu.cn,panzhiqiang@nudt.edu.cn,songchengyu@nudt.edu.cn
Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 1, Pages 73-87 doi: 10.1631/FITEE.2200137
Keywords: Session-based recommendation Self-supervised learning Graph neural networks Target-adaptive masking
Learning to select pseudo labels: a semi-supervised method for named entity recognition Research Articles
Zhen-zhen Li, Da-wei Feng, Dong-sheng Li, Xi-cheng Lu,lizhenzhen14@nudt.edu.cn,davyfeng.c@gmail.com,dsli@nudt.edu.cn,xclu@nudt.edu.cn
Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 6, Pages 809-962 doi: 10.1631/FITEE.1800743
Keywords: 命名实体识别;无标注数据;深度学习;半监督学习方法
Interactive medical image segmentation with self-adaptive confidence calibration
沈楚云,李文浩,徐琪森,胡斌,金博,蔡海滨,朱凤平,李郁欣,王祥丰
Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 9, Pages 1332-1348 doi: 10.1631/FITEE.2200299
Keywords: Medical image segmentation Interactive segmentation Multi-agent reinforcement learning Confidence learning Semi-supervised learning
NGAT: attention in breadth and depth exploration for semi-supervised graph representation learning Research Articles
Jianke HU, Yin ZHANG,yinzh@zju.edu.cn
Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 3, Pages 409-421 doi: 10.1631/FITEE.2000657
Keywords: Graph learning Semi-supervised learning Node classification Attention
Federated unsupervised representation learning Research Article
Fengda ZHANG, Kun KUANG, Long CHEN, Zhaoyang YOU, Tao SHEN, Jun XIAO, Yin ZHANG, Chao WU, Fei WU, Yueting ZHUANG, Xiaolin LI,fdzhang@zju.edu.cn,kunkuang@zju.edu.cn
Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 8, Pages 1181-1193 doi: 10.1631/FITEE.2200268
Keywords: Federated learning Unsupervised learning Representation learning Contrastive learning
Representation learning via a semi-supervised stacked distance autoencoder for image classification Research Articles
Liang Hou, Xiao-yi Luo, Zi-yang Wang, Jun Liang,jliang@zju.edu.cn
Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 7, Pages 963-1118 doi: 10.1631/FITEE.1900116
Keywords: 自动编码器;图像分类;半监督学习;神经网络
Correspondence: Uncertainty-aware complementary label queries for active learning Perspective
Shengyuan LIU, Ke CHEN, Tianlei HU, Yunqing MAO,liushengyuan@zju.edu.cn,chenk@cs.zju.edu.cn,htl@zju.edu.cn,myq@citycloud.com.cn
Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 10, Pages 1497-1503 doi: 10.1631/FITEE.2200589
Keywords: 主动学习;图片分类;弱监督学习
MDLB: a metadata dynamic load balancing mechanism based on reinforcement learning Research Articles
Zhao-qi Wu, Jin Wei, Fan Zhang, Wei Guo, Guang-wei Xie,17034203@qq.com
Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 7, Pages 963-1118 doi: 10.1631/FITEE.1900121
Keywords: 面向对象的存储系统;元数据;动态负载均衡;强化学习;Q_learning
Oguzhan Dogru, Kirubakaran Velswamy, Biao Huang
Engineering 2021, Volume 7, Issue 9, Pages 1248-1261 doi: 10.1016/j.eng.2021.04.027
This paper synchronizes control theory with computer vision by formalizing object tracking as a sequential decision-making process. A reinforcement learning (RL) agent successfully tracks an interface between two liquids, which is often a critical variable to track in many chemical, petrochemical, metallurgical, and oil industries. This method utilizes less than 100 images for creating an environment, from which the agent generates its own data without the need for expert knowledge. Unlike supervised learning (SL) methods that rely on a huge number of parameters, this approach requires far fewer parameters, which naturally reduces its maintenance cost. Besides its frugal nature, the agent is robust to environmental uncertainties such as occlusion, intensity changes, and excessive noise. From a closed-loop control context, an interface location-based deviation is chosen as the optimization goal during training. The methodology showcases RL for real-time object-tracking applications in the oil sands industry. Along with a presentation of the interface tracking problem, this paper provides a detailed review of one of the most effective RL methodologies: actor–critic policy.
Keywords: Interface tracking Object tracking Occlusion Reinforcement learning Uniform manifold approximation and projection
Non-IID Recommender Systems: A Review and Framework of Recommendation Paradigm Shifting Artical
Longbing Cao
Engineering 2016, Volume 2, Issue 2, Pages 212-224 doi: 10.1016/J.ENG.2016.02.013
While recommendation plays an increasingly critical role in our living, study, work, and entertainment, the recommendations we receive are often for irrelevant, duplicate, or uninteresting products and services. A critical reason for such bad recommendations lies in the intrinsic assumption that recommended users and items are independent and identically distributed (IID) in existing theories and systems. Another phenomenon is that, while tremendous efforts have been made to model specific aspects of users or items, the overall user and item characteristics and their non-IIDness have been overlooked. In this paper, the non-IID nature and characteristics of recommendation are discussed, followed by the non-IID theoretical framework in order to build a deep and comprehensive understanding of the intrinsic nature of recommendation problems, from the perspective of both couplings and heterogeneity. This non-IID recommendation research triggers the paradigm shift from IID to non-IID recommendation research and can hopefully deliver informed, relevant, personalized, and actionable recommendations. It creates exciting new directions and fundamental solutions to address various complexities including cold-start, sparse data-based, cross-domain, group-based, and shilling attack-related issues.
Keywords: Independent and identically distributed (IID) Non-IID Heterogeneity Coupling relationship Coupling learning Relational learning IIDness learning Non-IIDness learning Recommender system Recommendation Non-IID recommendation
Decentralized multi-agent reinforcement learning with networked agents: recent advances Review Article
Kaiqing Zhang, Zhuoran Yang, Tamer Başar,kzhang66@illinois.edu,zy6@princeton.edu,basar1@illinois.edu
Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 6, Pages 802-814 doi: 10.1631/FITEE.1900661
Keywords: 强化学习;多智能体系统;网络系统;一致性优化;分布式优化;博弈论
Embedding expert demonstrations into clustering buffer for effective deep reinforcement learning Research Article
Shihmin WANG, Binqi ZHAO, Zhengfeng ZHANG, Junping ZHANG, Jian PU
Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 11, Pages 1541-1556 doi: 10.1631/FITEE.2300084
Keywords: Reinforcement learning Sample efficiency Sampling process Clustering methods Autonomous driving
A home energy management approach using decoupling value and policy in reinforcement learning
熊珞琳,唐漾,刘臣胜,毛帅,孟科,董朝阳,钱锋
Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 9, Pages 1261-1272 doi: 10.1631/FITEE.2200667
Keywords: Home energy system Electric vehicle Reinforcement learning Generalization
Title Author Date Type Operation
A self-supervised method for treatment recommendation in sepsis
Sihan Zhu, Jian Pu,jianpu@fudan.edu.cn
Journal Article
Pre-training with asynchronous supervised learning for reinforcement learning based autonomous driving
Yunpeng Wang, Kunxian Zheng, Daxin Tian, Xuting Duan, Jianshan Zhou,ypwang@buaa.edu.cn,zhengkunxian@buaa.edu.cn,dtian@buaa.edu.cn,duanxuting@buaa.edu.cn
Journal Article
Self-supervised graph learning with target-adaptive masking for session-based recommendation
Yitong WANG, Fei CAI, Zhiqiang PAN, Chengyu SONG,wangyitong20@nudt.edu.cn,caifei08@nudt.edu.cn,panzhiqiang@nudt.edu.cn,songchengyu@nudt.edu.cn
Journal Article
Learning to select pseudo labels: a semi-supervised method for named entity recognition
Zhen-zhen Li, Da-wei Feng, Dong-sheng Li, Xi-cheng Lu,lizhenzhen14@nudt.edu.cn,davyfeng.c@gmail.com,dsli@nudt.edu.cn,xclu@nudt.edu.cn
Journal Article
Interactive medical image segmentation with self-adaptive confidence calibration
沈楚云,李文浩,徐琪森,胡斌,金博,蔡海滨,朱凤平,李郁欣,王祥丰
Journal Article
NGAT: attention in breadth and depth exploration for semi-supervised graph representation learning
Jianke HU, Yin ZHANG,yinzh@zju.edu.cn
Journal Article
Federated unsupervised representation learning
Fengda ZHANG, Kun KUANG, Long CHEN, Zhaoyang YOU, Tao SHEN, Jun XIAO, Yin ZHANG, Chao WU, Fei WU, Yueting ZHUANG, Xiaolin LI,fdzhang@zju.edu.cn,kunkuang@zju.edu.cn
Journal Article
Representation learning via a semi-supervised stacked distance autoencoder for image classification
Liang Hou, Xiao-yi Luo, Zi-yang Wang, Jun Liang,jliang@zju.edu.cn
Journal Article
Correspondence: Uncertainty-aware complementary label queries for active learning
Shengyuan LIU, Ke CHEN, Tianlei HU, Yunqing MAO,liushengyuan@zju.edu.cn,chenk@cs.zju.edu.cn,htl@zju.edu.cn,myq@citycloud.com.cn
Journal Article
MDLB: a metadata dynamic load balancing mechanism based on reinforcement learning
Zhao-qi Wu, Jin Wei, Fan Zhang, Wei Guo, Guang-wei Xie,17034203@qq.com
Journal Article
Actor–Critic Reinforcement Learning and Application in Developing Computer-Vision-Based Interface Tracking
Oguzhan Dogru, Kirubakaran Velswamy, Biao Huang
Journal Article
Non-IID Recommender Systems: A Review and Framework of Recommendation Paradigm Shifting
Longbing Cao
Journal Article
Decentralized multi-agent reinforcement learning with networked agents: recent advances
Kaiqing Zhang, Zhuoran Yang, Tamer Başar,kzhang66@illinois.edu,zy6@princeton.edu,basar1@illinois.edu
Journal Article
Embedding expert demonstrations into clustering buffer for effective deep reinforcement learning
Shihmin WANG, Binqi ZHAO, Zhengfeng ZHANG, Junping ZHANG, Jian PU
Journal Article