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检索条件"机构=Center for Machine Intelligence and Data Science"
216 条 记 录,以下是31-40 订阅
排序:
Your Transformer May Not be as Powerful as You Expect  36
Your Transformer May Not be as Powerful as You Expect
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36th Conference on Neural Information Processing Systems, NeurIPS 2022
作者: Luo, Shengjie Li, Shanda Zheng, Shuxin Liu, Tie-Yan Wang, Liwei He, Di National Key Laboratory of General Artificial Intelligence School of Intelligence Science and Technology Peking University China Machine Learning Department School of Computer Science Carnegie Mellon University United States Microsoft Research United States Center for Data Science Peking University China Zhejiang Lab China
Relative Positional Encoding (RPE), which encodes the relative distance between any pair of tokens, is one of the most successful modifications to the original Transformer. As far as we know, theoretical understanding... 详细信息
来源: 评论
GraphMorph: Tubular Structure Extraction by Morphing Predicted Graphs
arXiv
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arXiv 2025年
作者: Zhang, Zhao Zhao, Ziwei Wang, Dong Wang, Liwei Center for Data Science Peking University China Yizhun Medical AI Co. Ltd China State Key Laboratory of General Artificial Intelligence School of Intelligence Science and Technology Peking University China Center for Machine Learning Research Peking University China China
Accurately restoring topology is both challenging and crucial in tubular structure extraction tasks, such as blood vessel segmentation and road network extraction. Diverging from traditional approaches based on pixel-... 详细信息
来源: 评论
Spatially-Consistent Implicit Volumetric Function for Uni- and Bi-Planar X-Ray-Based Computed Tomography Reconstruction
Spatially-Consistent Implicit Volumetric Function for Uni- a...
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IEEE International Symposium on Biomedical Imaging
作者: Yikun Jiang Xiaoru Yuan Yuru Pei Center for Data Science Peking University Beijing China Key Laboratory of Machine Perception School of Intelligence Science and Technology Peking University Beijing China
We introduce a spatially-consistent implicit f unction representation (sci-f) for high-resolution volumetric computed tomography (CT) image recovery from uni- and bi-planar X-ray images. We devise a deep end-to-end le...
来源: 评论
Towards Highly Efficient Anomaly Detection for Predictive Maintenance
Towards Highly Efficient Anomaly Detection for Predictive Ma...
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International Conference on machine Learning and Applications (ICMLA)
作者: Simon Klüttermann Vanlal Peka Philipp Doebler Emmanuel Müller TU Dortmund University Dortmund Germany Lamarr Institute for Machine Learning and Artificial Intelligence Dortmund Germany Research Center Trustworthy Data Science and Security Dortmund Germany
This paper introduces SEAN, a novel anomaly detection algorithm designed for real-time applications in predictive maintenance. SEAN leverages an ensemble-based approach to deliver competitive performance while drastic... 详细信息
来源: 评论
NC-ALG: Graph-Based Active Learning under Noisy Crowd  40
NC-ALG: Graph-Based Active Learning under Noisy Crowd
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40th IEEE International Conference on data Engineering, ICDE 2024
作者: Zhang, Wentao Wang, Yexin You, Zhenbang Li, Yang Cao, Gang Yang, Zhi Cui, Bin Center for Machine Learning Research Peking University China Key Lab of High Confidence Software Technologies Peking University China Institute of Advanced Algorithms Research Shanghai China Institute of Computational Social Science Peking University Qingdao China National Engineering Labratory for Big Data Analytics and Applications China TEG Tencent Inc. Department of Data Platform China Beijing Academy of Artificial Intelligence China
Graph Neural Networks (GNNs) have achieved great success in various data mining tasks but they heavily rely on a large number of annotated nodes, requiring considerable human efforts. Despite the effectiveness of exis... 详细信息
来源: 评论
A REDUCTION-BASED FRAMEWORK FOR CONSERVATIVE BANDITS AND REINFORCEMENT LEARNING  10
A REDUCTION-BASED FRAMEWORK FOR CONSERVATIVE BANDITS AND REI...
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10th International Conference on Learning Representations, ICLR 2022
作者: Yang, Yunchang Wu, Tianhao Zhong, Han Garcelon, Evrard Pirotta, Matteo Lazaric, Alessandro Wang, Liwei Du, Simon S. Center for Data Science Peking University China University of California Berkeley United States Facebook AI Research Key Laboratory of Machine Perception MOE School of Artificial Intelligence Peking University China International Center for Machine Learning Research Peking University China University of Washington United States
We study bandits and reinforcement learning (RL) subject to a conservative constraint where the agent is asked to perform at least as well as a given baseline policy. This setting is particular relevant in real-world ... 详细信息
来源: 评论
Reinforcement Learning as an Improvement Heuristic for Real-World Production Scheduling
arXiv
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arXiv 2024年
作者: Müller, Arthur Vollenkemper, Lukas Department of Machine Intelligence Fraunhofer IOSB-INA Lemgo32657 Germany Center for Applied Data Science Bielefeld University of Applied Sciences and Arts Gütersloh33330 Germany
The integration of Reinforcement Learning (RL) with heuristic methods is an emerging trend for solving optimization problems, which leverages RL’s ability to learn from the data generated during the search process. O... 详细信息
来源: 评论
Securing Consumer IoT Swarms Using Graph Transformers and SRAM for Firmware Attestation
Securing Consumer IoT Swarms Using Graph Transformers and SR...
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Consumer Communications and Networking Conference, CCNC IEEE
作者: Varun Kohli Bhavya Kohli Muhammad Naveed Aman Biplab Sikdar Department of Electrical and Computer Engineering National University of Singapore Singapore Center for Machine Intelligence and Data Science Indian Institute of Technology Bombay India School of Computing University of Nebraska-Lincoln USA
Consumer Internet of Things (IoT) networks have gained widespread popularity due to their convenience, automation, and security provisions in personal and home environments. Ubiquitous resource-constrained devices, ho... 详细信息
来源: 评论
Smaller Batches, Bigger Gains? Investigating the Impact of Batch Sizes on Reinforcement Learning Based Real-World Production Scheduling
arXiv
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arXiv 2024年
作者: Müller, Arthur Grumbach, Felix Sabatelli, Matthia Department of Machine Intelligence Fraunhofer IOSB-INA Lemgo32657 Germany Center for Applied Data Science Hochschule Bielefeld Gütersloh33330 Germany Department of Artificial Intelligence University of Groningen Groningen9712 CP Netherlands
Production scheduling is an essential task in manufacturing, with Reinforcement Learning (RL) emerging as a key solution. In a previous work, RL was utilized to solve an extended permutation flow shop scheduling probl... 详细信息
来源: 评论
Optimal bounds for p sensitivity sampling via 2 augmentation
arXiv
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arXiv 2024年
作者: Munteanu, Alexander Omlor, Simon Dortmund Data Science Center Faculties of Statistics and Computer Science TU Dortmund University Dortmund Germany Faculty of Statistics and Lamarr Institute for Machine Learning and Artificial Intelligence TU Dortmund University Dortmund Germany
data subsampling is one of the most natural methods to approximate a massively large data set by a small representative proxy. In particular, sensitivity sampling received a lot of attention, which samples points prop... 详细信息
来源: 评论