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A survey of script learning Review
Yi Han, Linbo Qiao, Jianming Zheng, Hefeng Wu, Dongsheng Li, Xiangke Liao,hanyi12@nudt.edu.cn,qiao.linbo@nudt.edu.cn,zhengjianming12@nudt.edu.cn,wuhefeng@mail.sysu.edu.cn,dsli@nudt.edu.cn,xkliao@nudt.edu.cn
Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 3, Pages 287-436 doi: 10.1631/FITEE.2000347
Keywords: 脚本学习;自然语言处理;常识知识建模;事件推理
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
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
New directions for artificial intelligence: human, machine, biological, and quantum intelligence Comment
Li WEIGANG,Liriam Michi ENAMOTO,Denise Leyi LI,Geraldo Pereira ROCHA FILHO
Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 6, Pages 984-990 doi: 10.1631/FITEE.2100227
This comment reviews the “once learning” mechanism (OLM) that was proposed byWeigang (1998), the subsequent success of “one-shot learning” in object categories (Li FF et al., 2003), and “you only look once” (YOLO) in objective detection (Redmon et al., 2016). Upon analyzing the current state of research in artificial intelligence (AI), we propose to divide AI into the following basic theory categories: artificial human intelligence (AHI), artificial machine intelligence (AMI), artificial biological intelligence (ABI), and artificial quantum intelligence (AQI). These can also be considered as the main directions of research and development (R&D) within AI, and distinguished by the following classification standards and methods: (1) human-, machine-, biological-, and quantum-oriented AI R&D; (2) information input processed by dimensionality increase or reduction; (3) the use of one/a few or a large number of samples for knowledge learning.
Keywords: 人工智能;机器学习;一次性学习;一瞥学习;量子计算
Two-level hierarchical feature learning for image classification Article
Guang-hui SONG,Xiao-gang JIN,Gen-lang CHEN,Yan NIE
Frontiers of Information Technology & Electronic Engineering 2016, Volume 17, Issue 9, Pages 897-906 doi: 10.1631/FITEE.1500346
Keywords: Transfer learning Feature learning Deep convolutional neural network Hierarchical classification Spectral clustering
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
A review of computer graphics approaches to urban modeling from a machine learning perspective Review Article
Tian Feng, Feiyi Fan, Tomasz Bednarz,t.feng@latrobe.edu.au
Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 7, Pages 915-925 doi: 10.1631/FITEE.2000141
Keywords: 城市建模;计算机图形学;机器学习;深度学习
Learning Curve and Its Application in Production Operation al Research
Chen Zhixiang
Strategic Study of CAE 2007, Volume 9, Issue 7, Pages 82-88
Learning curve is one character function that improves output by the experience accumulation of producer. Learning curve can be used to establish scientific cost plan, improve shop flow scheduling, labor quota and plan, quality, and so on. This paper reviews the research literatures, introduces different forms of learning curve, analyzes their characters, and discusses the future directions of application of learning curve.
Keywords: learning curve operational management behaviors research
Mingzhi Zhao, Huiliang Wei, Yiming Mao, Changdong Zhang, Tingting Liu, Wenhe Liao
Engineering 2023, Volume 23, Issue 4, Pages 181-195 doi: 10.1016/j.eng.2022.09.015
Molten pool characteristics have a significant effect on printing quality in laser powder bed fusion (PBF), and quantitative predictions of printing parameters and molten pool dimensions are critical to the intelligent control of the complex processes in PBF. Thus far, bidirectional predictions of printing parameters and molten pool dimensions have been challenging due to the highly nonlinear correlations involved. To
address this issue, we integrate an experiment on molten pool characteristics, a mechanistic model, and deep learning to achieve both forward and inverse predictions of key parameters and molten pool characteristics during laser PBF. The experiment provides fundamental data, the mechanistic model significantly augments the dataset, and the multilayer perceptron (MLP) deep learning model predicts the molten pool dimensions and process parameters based on the dataset built from the experiment and the mechanistic model. The results show that bidirectional predictions of the molten pool dimensions and process parameters can be realized, with the highest prediction accuracies approaching 99.9% and mean prediction accuracies of over 90.0%. Moreover, the prediction accuracy of the MLP model is closely related to the characteristics of the dataset—that is, the learnability of the dataset has a crucial impact on the prediction accuracy. The highest prediction accuracy is 97.3% with enhancement of the dataset via the mechanistic model, while the highest prediction accuracy is 68.3% when using only the experimental dataset. The prediction accuracy of the MLP model largely depends on the quality of the dataset as well. The research results demonstrate that bidirectional predictions of complex correlations using MLP are feasible for laser PBF, and offer a novel and useful framework for the determination of process conditions and outcomes for intelligent additive manufacturing.
Keywords: Additive manufacturing Molten pool Model Deep learning Learnability
Communicative Learning: A Unified Learning Formalism Review
Luyao Yuan, Song-Chun Zhu
Engineering 2023, Volume 25, Issue 6, Pages 77-100 doi: 10.1016/j.eng.2022.10.017
In this article, we propose a communicative learning (CL) formalism that unifies existing machine learning paradigms, such as passive learning, active learning, algorithmic teaching, and so forth, and facilitates the development of new learning methods. Arising from human cooperative communication, this formalism poses learning as a communicative process and combines pedagogy with the burgeoning field of machine learning. The pedagogical insight facilitates the adoption of alternative information sources in machine learning besides randomly sampled data, such as intentional messages given by a helpful teacher. More specifically, in CL, a teacher and a student exchange information with each other collaboratively to transmit and acquire certain knowledge. Each agent has a mind, which includes the agent's knowledge, utility, and mental dynamics. To establish effective communication, each agent also needs an estimation of its partner's mind. We define expressive mental representations and learning formulation sufficient for such recursive modeling, which endows CL with human-comparable learning efficiency. We demonstrate the application of CL to several prototypical collaboration tasks and illustrate that this formalism allows learning protocols to go beyond Shannon's communication limit. Finally, we present our contribution to the foundations of learning by putting forth hierarchies in learning and defining the halting problem of learning.
Keywords: Artificial intelligencehine Cooperative communication Machine learning Pedagogy Theory of mind
A Robust Transfer Dictionary Learning Algorithm for Industrial Process Monitoring Article
Chunhua Yang, Huiping Liang, Keke Huang, Yonggang Li, Weihua Gui
Engineering 2021, Volume 7, Issue 9, Pages 1262-1273 doi: 10.1016/j.eng.2020.08.028
Data-driven process-monitoring methods have been the mainstream for complex industrial systems due to their universality and the reduced need for reaction mechanisms and first-principles knowledge. However, most data-driven process-monitoring methods assume that historical training data and online testing data follow the same distribution. In fact, due to the harsh environment of industrial systems, the
collected data from real industrial processes are always affected by many factors, such as the changeable operating environment, variation in the raw materials, and production indexes. These factors often cause the distributions of online monitoring data and historical training data to differ, which induces a model mismatch in the process-monitoring task. Thus, it is difficult to achieve accurate process monitoring when a model learned from training data is applied to actual online monitoring. In order to resolve the problem of the distribution divergence between historical training data and online testing data that is induced by changeable operation environments, a robust transfer dictionary learning (RTDL) algorithm is proposed in this paper for industrial process monitoring. The RTDL is a synergy of representative learning and domain adaptive transfer learning. The proposed method regards historical training data and online testing data as the source domain and the target domain, respectively, in the transfer learning problem. Maximum mean discrepancy regularization and linear discriminant analysis-like regularization are then incorporated into the dictionary learning framework, which can reduce the distribution divergence between the source domain and target domain. In this way, a robust dictionary can be learned even if the characteristics of the source domain and target domain are evidently different under the interference of a realistic and changeable operation environment. Such a dictionary can effectively improve the performance of process monitoring and mode classification. Extensive experiments including a numerical simulation and two industrial systems are conducted to verify the efficiency and superiority of the proposed method.
Keywords: Process monitoring Multimode process Dictionary learning Transfer learning
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
Max-margin basedBayesian classifier Article
Tao-cheng HU,Jin-hui YU
Frontiers of Information Technology & Electronic Engineering 2016, Volume 17, Issue 10, Pages 973-981 doi: 10.1631/FITEE.1601078
Keywords: Multi-class learning Max-margin learning Online algorithm
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
Machine Learning Produces Superhuman Chip Designs
Robert Pollie,
Engineering 2022, Volume 10, Issue 3, Pages 7-9 doi: 10.1016/j.eng.2022.01.006
Title Author Date Type Operation
A survey of script learning
Yi Han, Linbo Qiao, Jianming Zheng, Hefeng Wu, Dongsheng Li, Xiangke Liao,hanyi12@nudt.edu.cn,qiao.linbo@nudt.edu.cn,zhengjianming12@nudt.edu.cn,wuhefeng@mail.sysu.edu.cn,dsli@nudt.edu.cn,xkliao@nudt.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
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
New directions for artificial intelligence: human, machine, biological, and quantum intelligence
Li WEIGANG,Liriam Michi ENAMOTO,Denise Leyi LI,Geraldo Pereira ROCHA FILHO
Journal Article
Two-level hierarchical feature learning for image classification
Guang-hui SONG,Xiao-gang JIN,Gen-lang CHEN,Yan NIE
Journal Article
Non-IID Recommender Systems: A Review and Framework of Recommendation Paradigm Shifting
Longbing Cao
Journal Article
A review of computer graphics approaches to urban modeling from a machine learning perspective
Tian Feng, Feiyi Fan, Tomasz Bednarz,t.feng@latrobe.edu.au
Journal Article
Learning Curve and Its Application in Production Operation al Research
Chen Zhixiang
Journal Article
Predictions of Additive Manufacturing Process Parameters and Molten Pool Dimensions with a Physics-Informed Deep Learning Model
Mingzhi Zhao, Huiliang Wei, Yiming Mao, Changdong Zhang, Tingting Liu, Wenhe Liao
Journal Article
A Robust Transfer Dictionary Learning Algorithm for Industrial Process Monitoring
Chunhua Yang, Huiping Liang, Keke Huang, Yonggang Li, Weihua Gui
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
NGAT: attention in breadth and depth exploration for semi-supervised graph representation learning
Jianke HU, Yin ZHANG,yinzh@zju.edu.cn
Journal Article