《Machine Intelligence Research》

作品数：196被引量：212H指数：8
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《Machine Intelligence Research》
主办单位：中国科学院自动化研究所
最新期次：2025年2期更多>>
发文主题：LEARNINGATTENTIONMACHINE_LEARNINGGRAPHSURVEY更多>>
发文领域：自动化与计算机技术医药卫生电子电信理学更多>>
发文基金：国家自然科学基金北京市自然科学基金The Royal Society福建省自然科学基金更多>>
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Accelerated Elliptical PDE Solver for Computational Fluid Dynamics Based on Configurable U-Net Architecture: Analogy to V-Cycle Multigrid
《Machine Intelligence Research》2025年第2期324-336,共13页Kiran Bhaganagar David Chambers 
A configurable U-Net architecture is trained to solve the multi-scale elliptical partial differential equations.The motivation is to improve the computational cost of the numerical solution of Navier-Stokes equations...
关键词：Configurable U-Net architecture neural network methods for elliptical equations multi-scale partial differential equations Poisson and Helmholtz equation solvers computational fluid dynamics solutions. 
TAL: Two-stream Adaptive Learning for Generalizable Person Re-identification
《Machine Intelligence Research》2025年第2期337-351,共15页Yichao Yan Junjie Li Shengcai Liao Jie Qin 
Domain generalizable person re-identification(reid)is a challenging task in computer vision,which aims to apply a trained reid model to unseen domains.Prior works either combine the data in all the training domains to...
关键词：Person re-identification domain generalization image retrieval representation learning computer vision. 
Analysis of Conjoint Response in Post-stroke Patients Using the Attention-coupled Weighting Method
《Machine Intelligence Research》2025年第2期352-367,共16页Jingyao Chen Chen Wang Ningcun Xu Zeng-Guang Hou Liang Peng Pu Zhang 
supported in part by the National Key Research and Development Program of China(No.2022YFC3601200);in part by the National Natural Science Foundation of China(Nos.U1913601,U21A20479 and 62203441);in part by the Beijing Sci&Tech Program,China(No.Z211100007921021);in part by the Beijing Natural Science Foundation,China(No.Z170003).
Rehabilitation assessment plays a vital role in the recovery process of post-stroke patients.Currently,many studies have focused on the implementation of automated assessment scales.However,the impact of stroke on the...
关键词：Intelligent assessment conjoint response attentional coupling network spatio-temporal weight extraction. 
Auto-3D-house Design from Structured User Requirements
《Machine Intelligence Research》2025年第2期368-385,共18页Minkui Tan Qi Chen Zixiong Huang Qi Wu Yuanqing Li Jiaqiu Zhou 
supported by the National Natural Science Foundation of China(NSFC)(No.62072190);TCL Science and Technology Innovation Fund,China.
We study the task of automated house design,which aims to automatically generate 3D houses from user requirements.However,in the automatic system,it is non-trivial due to the intrinsic complexity of house designing:1)...
关键词：Automated house design user requirements understanding outline processing layout generation graph feature generation. 
Active Object Detection Based on PPO Learning Algorithm with Decision Knowledge Guidance
《Machine Intelligence Research》2025年第2期386-396,共11页Fujing Yao Guohui Tian Yuhao Wang Ning Yang 
supported in part by the National Natural Science Foundation of China(Nos.62273203 and U1813215);in part by the Special Fund for the Taishan Scholars Program of Shandong Province,China(No.ts2015110005).
After detecting a target object,a service robot must approach the target object to perform the associated service task.In active object detection(AOD)tasks,effective feature information representation and comprehensiv...
关键词：Service robot active object detection reinforcement learning path experience comprehensive decision model 
A Survey of Recent Advances in Commonsense Knowledge Acquisition: Methods and Resources
《Machine Intelligence Research》2025年第2期201-218,共18页Chenhao Wang Jiachun Li Yubo Chen Kang Liu Jun Zhao 
supported by the National Key Research and Development Program of China(No.2020AAA 0106400);the National Natural Science Foundation of China(Nos.61976211 and 62176257);supported by the Strategic Priority Research Program of Chinese Academy of Sciences,China(No.XDA27020100);the Youth Innovation Promotion Association CAS,China,and Yunnan Provincial Major Science and Technology Special Plan Projects,China(No.202202AD 080004).
Imparting human-like commonsense to machines is a long-term goal in the artificial intelligence community.To achieve this goal,constructing large-scale commonsense knowledge resources is an important step.In recent ye...
关键词：Commonsense knowledge knowledge acquisition knowledge representation and processing knowledge resource knowledge engineering. 
Attention Detection Using EEG Signals and Machine Learning: A Review
《Machine Intelligence Research》2025年第2期219-238,共20页Qianru Sun Yueying Zhou Peiliang Gong Daoqiang Zhang 
supported by the National Natural Science Foundation of China(Nos.62136004,62276130 and 62406131);the National Key R&D Program of China(No.2023YFF1204803);Key Research and Development Plan of Jiangsu Province,China(No.BE2022842).
Attention detection using electroencephalogram(EEG)signals has become a popular topic.However,there seems to be a notable gap in the literature regarding comprehensive and systematic reviews of machine learning method...
关键词：Attention detection electroencephalogram(EEG) machine learning deep learning brain-computer interface. 
GraphFM:Graph Factorization Machines for Feature Interaction Modelling
《Machine Intelligence Research》2025年第2期239-253,共15页Shu Wu Zekun Li Yunyue Su Zeyu Cui Xiaoyu Zhang Liang Wang 
supported by the National Science Foundation of China(No.62141608).
Factorization machine(FM)is a prevalent approach to modelling pairwise(second-order)feature interactions when dealing with high-dimensional sparse data.However,on the one hand,FMs fail to capture higher-order feature ...
关键词：Feature interaction factorization machines graph neural network recommender system deep learning 
A Systematic Comparison of Horizontal Federated Learning Algorithm Based on Random Forests in a Medical Setting
《Machine Intelligence Research》2025年第2期254-266,共13页Andrew Cheng Jingqing Zhang Atri Sharma Vibhor Gupta Yike Guo 
The medical industry generates vast amounts of data suitable for machine learning during patient-clinician interaction in hospitals.However,as a result of data protection regulations like the general data protection r...
关键词：Federated learning horizontal federated learning random forests machine learning medical diagnosis. 
Latent Landmark Graph for Efficient Explorationexploitation Balance in Hierarchical Reinforcement Learning
《Machine Intelligence Research》2025年第2期267-288,共22页Qingyang Zhang Hongming Zhang Dengpeng Xing Bo Xu 
supported by National Key R&D Program of China(No.2022ZD0116405);the Strategic Priority Research Program of the Chinese Academy of Sciences,China(No.XDA27030300).
Goal-conditioned hierarchical reinforcement learning(GCHRL)decomposes the desired goal into subgoals and conducts exploration and exploitation in the subgoal space.Its effectiveness heavily relies on subgoal represent...
关键词：Hierarchical reinforcement learning representation learning latent landmark graph contrastive learning exploration and exploitation. 
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