tendinitis
发表于 2025-3-30 12:14:55
http://reply.papertrans.cn/16/1564/156379/156379_51.png
枯燥
发表于 2025-3-30 14:03:20
https://doi.org/10.1007/978-94-6091-299-3possibility of using various types of online augmentations was explored. The most promising methods were highlighted. Experimental studies showed that the quality of the classification was improved for various tasks and various neural network architectures.
ascetic
发表于 2025-3-30 19:36:13
http://reply.papertrans.cn/16/1564/156379/156379_53.png
intention
发表于 2025-3-30 21:21:54
http://reply.papertrans.cn/16/1564/156379/156379_54.png
土产
发表于 2025-3-31 01:00:04
http://reply.papertrans.cn/16/1564/156379/156379_55.png
Crater
发表于 2025-3-31 08:01:37
http://reply.papertrans.cn/16/1564/156379/156379_56.png
偏离
发表于 2025-3-31 09:11:10
Christian Kassung,Sebastian Schwesingerthat the performance of the CNN models was much worse on this set (an almost 30% drop in word accuracy). We performed a classification of errors made by the best model both on the standard test set and the new one.
sphincter
发表于 2025-3-31 16:14:49
Guided Layer-Wise Learning for Deep Models Using Side Informationscriminative training of deep neural networks, DR is defined as a distance over the features and included in the learning objective. With our experimental tests, we show that DR can help the backpropagation to cope with vanishing gradient problems and to provide faster convergence and smaller generalization errors.
Progesterone
发表于 2025-3-31 18:26:58
Adapting the Graph2Vec Approach to Dependency Trees for NLP Tasksres of dependency trees. This new vector representation can be used in NLP tasks where it is important to model syntax (e.g. authorship attribution, intention labeling, targeted sentiment analysis etc.). Universal Dependencies treebanks were clustered to show the consistency and validity of the proposed tree representation methods.
一回合
发表于 2025-4-1 00:58:51
Morpheme Segmentation for Russian: Evaluation of Convolutional Neural Network Modelsthat the performance of the CNN models was much worse on this set (an almost 30% drop in word accuracy). We performed a classification of errors made by the best model both on the standard test set and the new one.