闪光你我
发表于 2025-3-25 04:47:10
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Defense
发表于 2025-3-25 07:53:26
Joint Regularization Knowledge Distillationtween networks are reduced when training with a central example. Teacher and student networks will become more similar as a result of joint training. Extensive experimental results on benchmark datasets such as CIFAR-10, CIFAR-100, and Tiny-ImagNet show that JRKD outperforms many advanced distillati
Dissonance
发表于 2025-3-25 13:00:42
Dual-Branch Contrastive Learning for Network Representation Learningis proposed, in which the two generated views are compared with the original graph separately, and the joint optimization method is used to continuously update the two views, allowing the model to learn more discriminative feature representations. The proposed method was evaluated on three datasets,
gnarled
发表于 2025-3-25 17:05:59
Multi-granularity Contrastive Siamese Networks for Abstractive Text Summarizationcy between the representations of the augmented text pairs through a Siamese network. We conduct empirical experiments on the CNN/Daily Mail and XSum datasets. Compared to many existing benchmarks, the results validate the effectiveness of our model.
Seminar
发表于 2025-3-25 23:35:56
Joint Entity and Relation Extraction for Legal Documents Based on Table Fillingsional table that can express the relation between word pairs for each relation separately and designing three table-filling strategies to decode the triples under the corresponding relations. The experimental results on the information extraction dataset in “CAIL2021” show that the proposed method
Arboreal
发表于 2025-3-26 00:16:28
Dy-KD: Dynamic Knowledge Distillation for Reduced Easy Examples the experimental results show that: (1) Use the curriculum strategy to discard easy examples to prevent the model’s fitting ability from being consumed by fitting easy examples. (2) Giving hard and easy examples varied weight so that the model emphasizes learning hard examples, which can boost stud
仪式
发表于 2025-3-26 04:54:08
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expire
发表于 2025-3-26 10:19:30
Haifeng Qing,Ning Jiang,Jialiang Tang,Xinlei Huang,Wengqing Wuense benefit to all readers who are interested in starting research in this area. In addition, it offers experienced researchers a valuable overview of the latest work in this area..978-981-10-8714-1978-981-10-8715-8Series ISSN 2191-5768 Series E-ISSN 2191-5776
interior
发表于 2025-3-26 14:05:31
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APO
发表于 2025-3-26 19:39:31
Jinta Weng,Donghao Li,Yifan Deng,Jie Zhang,Yue Hu,Heyan Huang