CASIA OpenIR

浏览/检索结果: 共10条,第1-10条 帮助

限定条件    
已选(0)清除 条数/页:   排序方式:
Overhead-free Noise-tolerant Federated Learning: A New Baseline 期刊论文
Machine Intelligence Research, 2024, 卷号: 21, 期号: 3, 页码: 526-537
作者:  Shiyi Lin;  Deming Zhai;  Feilong Zhang;  Junjun Jiang;  Xianming Liu;  Xiangyang Ji
Adobe PDF(1816Kb)  |  收藏  |  浏览/下载:23/7  |  提交时间:2024/05/23
Federated learning, noise-label learning, privacy-preserving machine learning, edge intelligence, distributed machine learning  
State of the Art on Deep Learning-enhanced Rendering Methods 期刊论文
Machine Intelligence Research, 2023, 卷号: 20, 期号: 6, 页码: 799-821
作者:  Qi Wang;  Zhihua Zhong;  Yuchi Huo;  Hujun Bao;  Rui Wang
Adobe PDF(6540Kb)  |  收藏  |  浏览/下载:42/15  |  提交时间:2024/04/23
Neural rendering, computer graphics, scene representation, rendering, post-processing  
YOLO-CORE: Contour Regression for Efficient Instance Segmentation 期刊论文
Machine Intelligence Research, 2023, 卷号: 20, 期号: 5, 页码: 716-728
作者:  Haoliang Liu;  Wei Xiong;  Yu Zhang
Adobe PDF(11569Kb)  |  收藏  |  浏览/下载:19/2  |  提交时间:2024/04/23
Computer vision, instance segmentation, object shape prediction, contour regression, polar distance  
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:41/12  |  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:41/9  |  提交时间:2024/04/23
Predictive cruise control (PCC), cloud control system (CCS), cooperative control, efficient operation, intelligent connected vehicle  
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:31/6  |  提交时间:2024/04/23
Human-computer gaming, AI, intelligent decision making, deep reinforcement learning, self-play  
DynamicRetriever: A Pre-trained Model-based IR System Without an Explicit Index 期刊论文
Machine Intelligence Research, 2023, 卷号: 20, 期号: 2, 页码: 276-288
作者:  Yu-Jia Zhou;  Jing Yao;  Zhi-Cheng Dou;  Ledell Wu;  Ji-Rong Wen
Adobe PDF(2790Kb)  |  收藏  |  浏览/下载:22/8  |  提交时间:2024/04/23
Information retrieval (IR)  document retrieval  model-based IR  pre-trained language model  differentiable search index  
Efficient Visual Recognition: A Survey on Recent Advances and Brain-inspired Methodologies 期刊论文
Machine Intelligence Research, 2022, 卷号: 19, 期号: 5, 页码: 366-411
作者:  Yang Wu;  Ding-Heng Wang;  Xiao-Tong Lu;  Fan Yang;  Man Yao;  Wei-Sheng Dong;  Jian-Bo Shi;  Guo-Qi Li
Adobe PDF(6780Kb)  |  收藏  |  浏览/下载:27/4  |  提交时间:2024/04/23
Visual recognition  deep neural networks (DNNS)  brain-inspired methodologies  network compression  dynamic inference  survey  
A Weighted Average Consensus Approach for Decentralized Federated Learning 期刊论文
Machine Intelligence Research, 2022, 卷号: 19, 期号: 4, 页码: 319-330
作者:  Alessandro Giuseppi;  Sabato Manfredi;  Antonio Pietrabissa
Adobe PDF(1462Kb)  |  收藏  |  浏览/下载:31/12  |  提交时间:2024/04/23
Federated learning (FedL)  deep learning  federated averaging (FedAvg)  machine learning (ML)  artificial intelligence  discrete-time consensus  distributed systems  
Weakly Correlated Knowledge Integration for Few-shot Image Classification 期刊论文
Machine Intelligence Research, 2022, 卷号: 19, 期号: 1, 页码: 24-37
作者:  Chun Yang;  Chang Liu;  Xu-Cheng Yin
Adobe PDF(1133Kb)  |  收藏  |  浏览/下载:36/12  |  提交时间:2024/04/23
Computer vision  pattern recognition  knowledge refinement and reuse  neural networks  machine vision