![]() E01B- PERMANENT WAY PERMANENT-WAY TOOLS MACHINES FOR MAKING RAILWAYS OF ALL KINDS.E01- CONSTRUCTION OF ROADS, RAILWAYS, OR BRIDGES.Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.) Filing date Publication date Application filed by Huzhou Deao Machinery Equipment Co ltd filed Critical Huzhou Deao Machinery Equipment Co ltd Priority to CN202010717574.6A priority Critical patent/CN111827017B/en Publication of CN111827017A publication Critical patent/CN111827017A/en Application granted granted Critical Publication of CN111827017B publication Critical patent/CN111827017B/en Status Active legal-status Critical Current Anticipated expiration legal-status Critical Links Original Assignee Huzhou Deao Machinery Equipment Co ltd Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.) ( en Inventor 张荣卫 张荣兵 Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.) Granted Application number CN202010717574.6A Other languages Chinese ( zh) Google Patents Movable switching track device for track turnout device of annular shuttleĭownload PDF Info Publication number CN111827017A CN111827017A CN202010717574.6A CN202010717574A CN111827017A CN 111827017 A CN111827017 A CN 111827017A CN 202010717574 A CN202010717574 A CN 202010717574A CN 111827017 A CN111827017 A CN 111827017A Authority CN China Prior art keywords rail track double sliding unit Prior art date Legal status (The legal status is an assumption and is not a legal conclusion. Google Patents CN111827017A - Movable switching track device for track turnout device of annular shuttle Surpassing existing negative sampling strategies.CN111827017A - Movable switching track device for track turnout device of annular shuttle ExtensiveĮxperiments on three Amazon benchmarks demonstrate GNNO's effectiveness inĬonsistently enhancing the performance of various state-of-the-art models and Hardness of negative samples, progressing from easy to difficult. Furthermore, GNNO employs curriculum learning to control the It mines hard negative samples based on the degree of overlap with the target GNNO first constructsĪ global weighted item transition graph using training sequences. Information hidden in user behaviors for negative mining. Sampling approach based on Neighborhood Overlap (GNNO) to exploit structural Motivated by this observation, we propose a Graph-based Negative That item pairs in distinct groups may possess different negative With varying degrees of neighborhood overlap change significantly, suggesting Progresses, the distributions of node-pair similarities in different groups In this work, we observe that as training Samples to enhance training and performance. Selection, numerous strategies have been proposed to mine informative negative Instead of merely employing random negative sample Download a PDF of the paper titled Neighborhood-based Hard Negative Mining for Sequential Recommendation, by Lu Fan and 3 other authors Download PDF Abstract: Negative sampling plays a crucial role in training successful sequential
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