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Mask Distillation Network for Conjunctival Hyperemia Severity Classification 期刊论文
Machine Intelligence Research, 2023, 卷号: 20, 期号: 6, 页码: 909-922
作者:  Mingchao Li;  Kun Huang;  Xiao Ma;  Yuexuan Wang;  Wen Fan;  Qiang Chen
Adobe PDF(2827Kb)  |  收藏  |  浏览/下载：14/4  |  提交时间：2024/04/23
Mask distillation (MD), conjunctiva hyperemia, attention mechanism, severity classification, deep learning  
Multitask Learning with Multiscale Residual Attention for Brain Tumor Segmentation and Classification 期刊论文
Machine Intelligence Research, 2023, 卷号: 20, 期号: 6, 页码: 897-908
作者:  Gaoxiang Li;  Xiao Hui;  Wenjing Li;  Yanlin Luo
Adobe PDF(2873Kb)  |  收藏  |  浏览/下载：12/2  |  提交时间：2024/04/23
Brain tumor segmentation and classification, multitask learning, multiscale residual attention module (MRAM), dynamic weight training, prior knowledge  
Multimodal Biometric Fusion Algorithm Based on Ranking Partition Collision Theory 期刊论文
Machine Intelligence Research, 2023, 卷号: 20, 期号: 6, 页码: 884-896
作者:  Zhuorong Li;  Yunqi Tang
Adobe PDF(2010Kb)  |  收藏  |  浏览/下载：24/10  |  提交时间：2024/04/23
Image processing, convolutional neural network, multimodal, biometrics, fusion  
FedFV: A Personalized Federated Learning Framework for Finger Vein Authentication 期刊论文
Machine Intelligence Research, 2023, 卷号: 20, 期号: 5, 页码: 683-696
作者:  Feng-Zhao Lian;  Jun-Duan Huang;  Ji-Xin Liu;   Guang Chen;  Jun-Hong Zhao;  Wen-Xiong Kang
Adobe PDF(1789Kb)  |  收藏  |  浏览/下载：7/1  |  提交时间：2024/04/23
Finger vein, personalized federated learning, privacy protection, biometric, authentication  
Machine Learning Methods in Solving the Boolean Satisfiability Problem 期刊论文
Machine Intelligence Research, 2023, 卷号: 20, 期号: 5, 页码: 640-655
作者:  Wenxuan Guo;  Hui-Ling Zhen;  Xijun Li;  Wanqian Luo;  Mingxuan Yuan;  Yaohui Jin;  Junchi Yan
Adobe PDF(1518Kb)  |  收藏  |  浏览/下载：19/7  |  提交时间：2024/04/23
Machine learning (ML), Boolean satisfiability (SAT), deep learning, graph neural networks (GNNs), combinatorial optimization  
A Review and Outlook on Predictive Cruise Control of Vehicles and Typical Applications Under Cloud Control System 期刊论文
Machine Intelligence Research, 2023, 卷号: 20, 期号: 5, 页码: 614-639
作者:  Bolin Gao;  Keke Wan;  Qien Chen;  Zhou Wang;  Rui Li;  Yu Jiang;  Run Mei;  Yinghui Luo;  Keqiang Li
Adobe PDF(12630Kb)  |  收藏  |  浏览/下载：18/5  |  提交时间：2024/04/23
Predictive cruise control (PCC), cloud control system (CCS), cooperative control, efficient operation, intelligent connected vehicle  
A Review of Predictive and Contrastive Self-supervised Learning for Medical Images 期刊论文
Machine Intelligence Research, 2023, 卷号: 20, 期号: 4, 页码: 483-513
作者:  Wei-Chien Wang;  Euijoon Ahn;  Dagan Feng;  Jinman Kim
Adobe PDF(2691Kb)  |  收藏  |  浏览/下载：17/5  |  提交时间：2024/04/23
Self-supervised learning (SSL), contrastive learning, deep learning, medical image analysis, computer vision  
A Survey on Recent Advances and Challenges in Reinforcement Learning Methods for Task-oriented Dialogue Policy Learning 期刊论文
Machine Intelligence Research, 2023, 卷号: 20, 期号: 3, 页码: 318-334
作者:  Wai-Chung Kwan;  Hong-Ru Wang;  Hui-Min Wang;  Kam-Fai Wong
Adobe PDF(2211Kb)  |  收藏  |  浏览/下载：1/1  |  提交时间：2024/04/23
Dialogue policy learning (DPL), task-oriented dialogue system (TOD), reinforcement learning (RL), dialogue system, Markov decision process  
AI in Human-computer Gaming: Techniques, Challenges and Opportunities 期刊论文
Machine Intelligence Research, 2023, 卷号: 20, 期号: 3, 页码: 299-317
作者:  Qi-Yue Yin;  Jun Yang;  Kai-Qi Huang;  Mei-Jing Zhao;  Wan-Cheng Ni;  Bin Liang;  Yan Huang;  Shu Wu;  Liang Wang
Adobe PDF(2608Kb)  |  收藏  |  浏览/下载：20/5  |  提交时间：2024/04/23
Human-computer gaming, AI, intelligent decision making, deep reinforcement learning, self-play  
Mitigating Spurious Correlations for Self-supervised Recommendation 期刊论文
Machine Intelligence Research, 2023, 卷号: 20, 期号: 2, 页码: 263-275
作者:  Xin-Yu Lin;  Yi-Yan Xu;  Wen-Jie Wang;  Yang Zhang;  Fu-Li Feng
Adobe PDF(2244Kb)  |  收藏  |  浏览/下载：15/6  |  提交时间：2024/04/23
Self-supervised recommendation  spurious correlations  spurious features  invariant feature learning  contrastive learning