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

雜技演員

書目名稱Advances in Knowledge Discovery and Data Mining影響因子(影響力)




書目名稱Advances in Knowledge Discovery and Data Mining影響因子(影響力)學(xué)科排名




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書目名稱Advances in Knowledge Discovery and Data Mining網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Advances in Knowledge Discovery and Data Mining被引頻次




書目名稱Advances in Knowledge Discovery and Data Mining被引頻次學(xué)科排名




書目名稱Advances in Knowledge Discovery and Data Mining年度引用




書目名稱Advances in Knowledge Discovery and Data Mining年度引用學(xué)科排名




書目名稱Advances in Knowledge Discovery and Data Mining讀者反饋




書目名稱Advances in Knowledge Discovery and Data Mining讀者反饋學(xué)科排名

ESPY

Web-Scale Semantic Product Search with?Large Language Modelsh offline experiments on an e-commerce product dataset, we show that a distilled small BERT-based model (75M params) trained using our approach improves the search relevance metric by up to 23% over a baseline DSSM-based model with similar inference latency. The small model only suffers a 3% drop in

allude

Multi-task Learning Based Keywords Weighted Siamese Model for?Semantic Retrievalfrom queries and documents automatically. Furthermore, we propose a novel multi-task framework that jointly trains both the deep Siamese model and the keywords identification model to help improve each other’s performance. We also conduct comprehensive experiments on both online A/B tests and two fa

sphincter

Basal-Ganglia

MFBE: Leveraging Multi-field Information of?FAQs for?Efficient Dense Retrievalulting 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

痛恨

Isotropic Representation Can Improve Dense Retrieval 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 .).

JAMB

Knowledge-Enhanced Prototypical Network with?Structural Semantics for?Few-Shot Relation Classificatinstruct 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

散步

MIDFA: Memory-Based Instance Division and?Feature Aggregation Network for?Video Object Detection 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

Obstreperous

Vision Transformers for?Small Histological Datasets Learned Through Knowledge Distillation. 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

Chameleon