踉跄
发表于 2025-3-23 09:45:24
Conference proceedings 2024eviewed and selected from 93 submissions. They were organized in topical sections as follows: natural language processing; computer vision; data analysis and machine learning; network analysis; and theoretical machine learning and optimization. The book also contains one invited talk in full paper length. .
nocturnal
发表于 2025-3-23 14:21:05
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Adenoma
发表于 2025-3-23 21:03:37
Business in the Digital Economyr for a mention. Experimental results on an ultra-fine entity typing task demonstrate that combining our predictions with the predictions of an existing neural model leads to a slight improvement over the ultra-fine types for mentions that are not pronouns.
不可救药
发表于 2025-3-23 23:54:47
Conference proceedings 2024ial Networks and Texts, AIST 2023, held in Yerevan, Armenia, during September 28-30, 2023. .The 24 full papers included in this book were carefully reviewed and selected from 93 submissions. They were organized in topical sections as follows: natural language processing; computer vision; data analy
BLANC
发表于 2025-3-24 02:54:03
World War II Impacting Colonized Asiacomprehensive analysis of the performance of the compressed models on different setups and compression levels. We observe a performance increase when using FWTTM compared to other methods on low ranks (high compression rates) for both encoder-only and encoder-decoder models.
expdient
发表于 2025-3-24 10:08:32
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installment
发表于 2025-3-24 11:17:18
Numan M. Durakbasa,M. Güneş Gençyılmaz localize bone pathologies, leveraging its real-time object detection capabilities. Additionally, the Swin, a transformer-based module, is utilized to extract contextual information from the localized regions of interest (ROIs) for accurate classification.
责问
发表于 2025-3-24 15:54:16
Transformers Compression: A Study of Matrix Decomposition Methods Using Fisher Informationcomprehensive analysis of the performance of the compressed models on different setups and compression levels. We observe a performance increase when using FWTTM compared to other methods on low ranks (high compression rates) for both encoder-only and encoder-decoder models.
Graphite
发表于 2025-3-24 21:59:00
Needle in a Haystack: Finding Suitable Idioms Based on Text Descriptionstence-BERT family. We also automatically expanded the initial dataset and fine-tuned a pre-trained Sentence-BERT model on the idiom/context matching task. This approach achieved the highest MRR score of 0.507. The data and the trained model are publicly available.
Melatonin
发表于 2025-3-25 01:25:50
DeepLOC: Deep Learning-Based Bone Pathology Localization and Classification in Wrist X-Ray Images localize bone pathologies, leveraging its real-time object detection capabilities. Additionally, the Swin, a transformer-based module, is utilized to extract contextual information from the localized regions of interest (ROIs) for accurate classification.