杂技演员
发表于 2025-3-21 16:57:42
书目名称Advances in Knowledge Discovery and Data Mining影响因子(影响力)<br> http://figure.impactfactor.cn/if/?ISSN=BK0148648<br><br> <br><br>书目名称Advances in Knowledge Discovery and Data Mining影响因子(影响力)学科排名<br> http://figure.impactfactor.cn/ifr/?ISSN=BK0148648<br><br> <br><br>书目名称Advances in Knowledge Discovery and Data Mining网络公开度<br> http://figure.impactfactor.cn/at/?ISSN=BK0148648<br><br> <br><br>书目名称Advances in Knowledge Discovery and Data Mining网络公开度学科排名<br> http://figure.impactfactor.cn/atr/?ISSN=BK0148648<br><br> <br><br>书目名称Advances in Knowledge Discovery and Data Mining被引频次<br> http://figure.impactfactor.cn/tc/?ISSN=BK0148648<br><br> <br><br>书目名称Advances in Knowledge Discovery and Data Mining被引频次学科排名<br> http://figure.impactfactor.cn/tcr/?ISSN=BK0148648<br><br> <br><br>书目名称Advances in Knowledge Discovery and Data Mining年度引用<br> http://figure.impactfactor.cn/ii/?ISSN=BK0148648<br><br> <br><br>书目名称Advances in Knowledge Discovery and Data Mining年度引用学科排名<br> http://figure.impactfactor.cn/iir/?ISSN=BK0148648<br><br> <br><br>书目名称Advances in Knowledge Discovery and Data Mining读者反馈<br> http://figure.impactfactor.cn/5y/?ISSN=BK0148648<br><br> <br><br>书目名称Advances in Knowledge Discovery and Data Mining读者反馈学科排名<br> http://figure.impactfactor.cn/5yr/?ISSN=BK0148648<br><br> <br><br>
ESPY
发表于 2025-3-21 21:50:50
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
发表于 2025-3-22 02:11:40
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
发表于 2025-3-22 08:38:41
http://reply.papertrans.cn/15/1487/148648/148648_4.png
Basal-Ganglia
发表于 2025-3-22 12:15:57
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
发表于 2025-3-22 14:02:37
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
发表于 2025-3-22 20:50:54
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
发表于 2025-3-22 22:58:59
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
发表于 2025-3-23 02:30:06
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
发表于 2025-3-23 07:37:43
http://reply.papertrans.cn/15/1487/148648/148648_10.png