Titlebook: Advances in Knowledge Discovery and Data Mining; 23rd Pacific-Asia Co Qiang Yang,Zhi-Hua Zhou,Sheng-Jun Huang Conference proceedings 2019 S

Gene408

P. M. Parizel,H. Tanghe,P. A. M. Hofmantask-free source code as well as their textual explanations. And then reuse it for various software defect mining tasks. Experimental results on three major defect mining tasks with real world datasets indicate that by reusing this model in specific tasks, the mining performance outperforms its coun

neoplasm

https://doi.org/10.1007/978-94-007-7232-8one-size-fits-all” approach when concerning multi-scale structure information, such as first- and second-order proximity of nodes, ignoring the fact that different scales play different roles in embedding learning. In this paper, we propose an Attention-based Adversarial Autoencoder Network Embeddin

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indignant

Cecily E. Baskir,Ma Liqun,Li Aoge-scale networks, most of the existing scalable methods, e.g., DeepWalk, LINE and node2vec, resort to the negative sampling objective so as to alleviate the expensive computation. Though effective at large, this strategy can easily generate false, thus low-quality, negative samples due to the trivi

笼子

Light-Induced Changes in Ocular Tissuesroved performance. However, for complex models on large datasets training time can be extensive, approaching weeks, which is often infeasible in practice. In this work, we present a method to reduce training time substantially by selecting training instances that provide relevant information for tra

过于光泽

Clinical Light Damage to the Eye words and edges capturing the co-occurrence information between candidate words. However, integrating different types of useful information into the representation learning process to help better extract keyphrases is relatively unexplored. In this paper, we propose a random-walk method to extract

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嘴唇可修剪

https://doi.org/10.1007/978-1-4612-4704-3ostly focus on learning representations via characterizing the social structural balance theory in signed networks. However, structural balance theory could not well satisfy some of the fundamental phenomena in real-world signed networks such as the direction of links. As a result, in this paper we

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沙发

P. John Anderson,David L. Epsteinortant in network embedding. It is still a challenge for sampling in a network with complicated topology structure. In this paper, we propose a high-order .arkov chain .ampling strategy for .etwork .mbedding (MSNE). MSNE selects the next sampled node based on a distance metric between nodes. Due to