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A Soft Sensor with Light and Efficient Multi-scale Feature Method for Multiple Sampling Rates in Industrial Processing 期刊论文
Machine Intelligence Research, 2024, 卷号: 21, 期号: 2, 页码: 400-410
作者:  Dezheng Wang;  Yinglong Wang;  Fan Yang;  Liyang Xu;  Yinong Zhang;  Yiran Chen;  Ning Liao
Adobe PDF(3208Kb)  |  收藏  |  浏览/下载：12/1  |  提交时间：2024/04/23
Multi-scale, feature extractor, deep neural network (DNN), multirate sampled industrial processes, prediction  
Comprehensive Relation Modelling for Image Paragraph Generation 期刊论文
Machine Intelligence Research, 2024, 卷号: 21, 期号: 2, 页码: 369-382
作者:  Xianglu Zhu;  Zhang Zhang;  Wei Wang;  Zilei Wang
Adobe PDF(1963Kb)  |  收藏  |  浏览/下载：4/2  |  提交时间：2024/04/23
Image paragraph generation, visual relationship, scene graph, graph convolutional network (GCN), long short-term memory  
Enhancing Multi-agent Coordination via Dual-channel Consensus 期刊论文
Machine Intelligence Research, 2024, 卷号: 21, 期号: 2, 页码: 349-368
作者:  Qingyang Zhang;  Kaishen Wang;  Jingqing Ruan;  Yiming Yang;  Dengpeng Xing;  Bo Xu
Adobe PDF(4997Kb)  |  收藏  |  浏览/下载：5/2  |  提交时间：2024/04/23
Multi-agent reinforcement learning, contrastive representation learning, consensus, multi-agent cooperation, cognitive consistency  
Adaptively Enhancing Facial Expression Crucial Regions via a Local Non-local Joint Network 期刊论文
Machine Intelligence Research, 2024, 卷号: 21, 期号: 2, 页码: 331-348
作者:  Guanghui Shi;  Shasha Mao;  Shuiping Gou;  Dandan Yan;  Licheng Jiao;  Lin Xiong
Adobe PDF(3926Kb)  |  收藏  |  浏览/下载：6/2  |  提交时间：2024/04/23
Facial expression recognition, deep neural network, multiple network ensemble, attention network, facial crucial regions  
Text Difficulty Study: Do Machines Behave the Same as Humans Regarding Text Difficulty? 期刊论文
Machine Intelligence Research, 2024, 卷号: 21, 期号: 2, 页码: 283-293
作者:  Bowen Chen;  Xiao Ding;  Yi Zhao;  Bo Fu;  Tingmao Lin;  Bing Qin;  Ting Liu
Adobe PDF(1796Kb)  |  收藏  |  浏览/下载：5/0  |  提交时间：2024/04/23
Cognition inspired natural language processing, psycholinguistics, explainability, text difficulty, curriculum learning  
Corporate Credit Ratings Based on Hierarchical Heterogeneous Graph Neural Networks 期刊论文
Machine Intelligence Research, 2024, 卷号: 21, 期号: 2, 页码: 257-271
作者:  Bo-Jing Feng;  Xi Cheng;  Hao-Nan Xu;  Wen-Fang Xue
Adobe PDF(2621Kb)  |  收藏  |  浏览/下载：4/2  |  提交时间：2024/04/23
Corporate credit rating, hierarchical relation, heterogeneous graph neural networks, adversarial learning  
A Comprehensive Overview of CFN From a Commonsense Perspective 期刊论文
Machine Intelligence Research, 2024, 卷号: 21, 期号: 2, 页码: 239-256
作者:  Ru Li;  Yunxiao Zhao;  Zhiqiang Wang;  Xuefeng Su;  Shaoru Guo;  Yong Guan;  Xiaoqi Han;  Hongyan Zhao
Adobe PDF(2392Kb)  |  收藏  |  浏览/下载：3/0  |  提交时间：2024/04/23
Chinese FrameNet (CFN), commonsense, scenario commonsense, frame, knowledge  
The Life Cycle of Knowledge in Big Language Models: A Survey 期刊论文
Machine Intelligence Research, 2024, 卷号: 21, 期号: 2, 页码: 217-238
作者:  Boxi Cao;  Hongyu Lin;  Xianpei Han;  Le Sun
Adobe PDF(1430Kb)  |  收藏  |  浏览/下载：7/2  |  提交时间：2024/04/23
Pre-trained language model, knowledge acquisition, knowledge representation, knowledge probing, knowledge editing, knowledge application  
Boosting Multi-modal Ocular Recognition via Spatial Feature Reconstruction and Unsupervised Image Quality Estimation 期刊论文
Machine Intelligence Research, 2024, 卷号: 21, 期号: 1, 页码: 197-214
作者:  Zihui Yan;  Yunlong Wang;  Kunbo Zhang;  Zhenan Sun;  Lingxiao He
Adobe PDF(3457Kb)  |  收藏  |  浏览/下载：5/1  |  提交时间：2024/04/23
Iris recognition, periocular recognition, spatial feature reconstruction, fully convolutional network, flexible matching, unsupervised iris quality assessment, adaptive weight fusion  
A Knowledge-enhanced Two-stage Generative Framework for Medical Dialogue Information Extraction 期刊论文
Machine Intelligence Research, 2024, 卷号: 21, 期号: 1, 页码: 153-168
作者:  Zefa Hu;  Ziyi Ni;  Jing Shi;  Shuang Xu;  Bo Xu
Adobe PDF(1525Kb)  |  收藏  |  浏览/下载：4/2  |  提交时间：2024/04/23
Medical dialogue understanding, information extraction, text generation, knowledge-enhanced prompt, low-resource setting, data augmentation