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Learning Top-K Subtask Planning Tree Based on Discriminative Representation Pretraining for Decision-making 期刊论文
Machine Intelligence Research, 2024, 卷号: 21, 期号: 4, 页码: 782-800
作者:  Jingqing Ruan;   Kaishen Wang;   Qingyang Zhang;   Dengpeng Xing;   Bo Xu
Adobe PDF(4577Kb)  |  收藏  |  浏览/下载：7/3  |  提交时间：2024/07/18
Reinforcement learning  representation learning  subtask planning  task decomposition  pretraining.  
Toward Human-centered XAI in Practice: A survey 期刊论文
Machine Intelligence Research, 2024, 卷号: 21, 期号: 4, 页码: 740-770
作者:  Xiangwei Kong;  Shujie Liu;  Luhao Zhu
Adobe PDF(1550Kb)  |  收藏  |  浏览/下载：4/0  |  提交时间：2024/07/18
Artificial intelligence (AI) application  explainable AI (XAI)  human-centered design  visual computing  medical diagnosis  
TextFormer: A Query-based End-to-end Text Spotter with Mixed Supervision 期刊论文
Machine Intelligence Research, 2024, 卷号: 21, 期号: 4, 页码: 704-717
作者:  Yukun Zhai;   Xiaoqiang Zhang;   Xiameng Qin;   Sanyuan Zhao;  Xingping Dong;   Jianbing Shen
Adobe PDF(2312Kb)  |  收藏  |  浏览/下载：9/5  |  提交时间：2024/07/18
End-to-end text spotting  arbitrarily-shaped texts  transformer  mixed supervision  multitask modeling  
Generalized Embedding Machines for Recommender Systems 期刊论文
Machine Intelligence Research, 2024, 卷号: 21, 期号: 3, 页码: 571-584
作者:  Enneng Yang;  Xin Xin;  Li Shen;  Yudong Luo;  Guibing Guo
Adobe PDF(1617Kb)  |  收藏  |  浏览/下载：64/24  |  提交时间：2024/05/23
Feature interactions, high-order interaction, factorization machine (FM), recommender system, graph neural network (GNN)  
Ripple Knowledge Graph Convolutional Networks for Recommendation Systems 期刊论文
Machine Intelligence Research, 2024, 卷号: 21, 期号: 3, 页码: 481-494
作者:  Chen Li;  Yang Cao;  Ye Zhu;  Debo Cheng;  Chengyuan Li;  Yasuhiko Morimoto
Adobe PDF(3688Kb)  |  收藏  |  浏览/下载：54/23  |  提交时间：2024/05/23
Deep learning, recommendation systems, knowledge graph, graph convolutional networks (GCNs), graph neural networks (GNNs)  
Distributed Deep Reinforcement Learning: A Survey and a Multi-player Multi-agent Learning Toolbox 期刊论文
Machine Intelligence Research, 2024, 卷号: 21, 期号: 3, 页码: 411-430
作者:  Qiyue Yin;  Tongtong Yu;  Shengqi Shen;  Jun Yang;  Meijing Zhao;  Wancheng Ni;  Kaiqi Huang;  Bin Liang;  Liang Wang
Adobe PDF(2923Kb)  |  收藏  |  浏览/下载：48/18  |  提交时间：2024/05/23
Deep reinforcement learning, distributed machine learning, self-play, population-play, toolbox  
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：72/26  |  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：53/17  |  提交时间：2024/04/23
Facial expression recognition, deep neural network, multiple network ensemble, attention network, facial crucial regions  
GraphFlow+: Exploiting Conversation Flow in Conversational Machine Comprehension with Graph Neural Networks 期刊论文
Machine Intelligence Research, 2024, 卷号: 21, 期号: 2, 页码: 272-282
作者:  Jing Hu;  Lingfei Wu;  Yu Chen;  Po Hu;  Mohammed J. Zaki
Adobe PDF(1612Kb)  |  收藏  |  浏览/下载：51/13  |  提交时间：2024/04/23
Conversational machine comprehension (MC), reading comprehension, question answering, graph neural networks (GNNs), natural language processing (NLP)  
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：59/20  |  提交时间：2024/04/23
Medical dialogue understanding, information extraction, text generation, knowledge-enhanced prompt, low-resource setting, data augmentation