Titlebook: Advances in Knowledge Discovery and Data Mining; 27th Pacific-Asia Co Hisashi Kashima,Tsuyoshi Ide,Wen-Chih Peng Conference proceedings 202

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Event-Based Reset Control of MASulting from multiple FAQ fields and performs well even with minimal labeled data. We empirically support this claim through experiments on proprietary as well as open-source public datasets in both unsupervised and supervised settings. Our model achieves around 27% and 23% better top-1 accuracy for

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Event-Based Reset Control of MAS we investigate out-of-distribution tasks where the test dataset differs from the training dataset. The results show that isotropic representation can certainly achieve a generally improved performance (The code is available at .).

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Guanglei Zhao,Hailong Cui,Shuang Liunstruct the negative samples with various difficulties (i.e. hard, medium, and easy) based on the conceptual hierarchical structure. Experimental results on the FewRel 2.0 benchmark show that SKProto outperforms state-of-the-art models. We also demonstrate that SKProto has better robustness than oth

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Zhenwei Liu,Donya Nojavanzadeh,Ali Saberi problem (c). These three parts constitute the MIDFA network. Experiments show that our method achieves 83.76% mAP on the ImageNet VID dataset based on ResNet-101, and 84.6% mAP on ResNeXt-101. In addition, we also conduct experiments on a custom-designed multi-class VID dataset, and adding Instance

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The Distributed Observer Approach,. Our best-performing ViT yields 0.961 and 0.911 F1-score and MCC, respectively, observing a 7% gain in MCC against stand-alone training. The proposed method presents a new perspective of leveraging knowledge distillation over transfer learning to encourage the use of customized transformers for eff

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0302-9743 ng data mining, data warehousing, machine learning, artificial intelligence, databases, statistics, knowledge engineering, big data technologies, and foundations..978-3-031-33379-8978-3-031-33380-4Series ISSN 0302-9743 Series E-ISSN 1611-3349