標(biāo)題: Titlebook: Analysis of Images, Social Networks and Texts; 11th International C Dmitry I. Ignatov,Michael Khachay,Sergey Zagoruyko Conference proceedin [打印本頁(yè)] 作者: Clientele 時(shí)間: 2025-3-21 19:39
書(shū)目名稱(chēng)Analysis of Images, Social Networks and Texts影響因子(影響力)
書(shū)目名稱(chēng)Analysis of Images, Social Networks and Texts影響因子(影響力)學(xué)科排名
書(shū)目名稱(chēng)Analysis of Images, Social Networks and Texts網(wǎng)絡(luò)公開(kāi)度
書(shū)目名稱(chēng)Analysis of Images, Social Networks and Texts網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書(shū)目名稱(chēng)Analysis of Images, Social Networks and Texts被引頻次
書(shū)目名稱(chēng)Analysis of Images, Social Networks and Texts被引頻次學(xué)科排名
書(shū)目名稱(chēng)Analysis of Images, Social Networks and Texts年度引用
書(shū)目名稱(chēng)Analysis of Images, Social Networks and Texts年度引用學(xué)科排名
書(shū)目名稱(chēng)Analysis of Images, Social Networks and Texts讀者反饋
書(shū)目名稱(chēng)Analysis of Images, Social Networks and Texts讀者反饋學(xué)科排名
作者: 刺耳 時(shí)間: 2025-3-21 21:37
Unsupervised Ultra-Fine Entity Typing with?Distributionally Induced Word Sensesr 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.作者: CRUC 時(shí)間: 2025-3-22 02:19
0302-9743 data analysis and machine learning; network analysis; and theoretical machine learning and optimization. The book also contains one invited talk in full paper length.?.978-3-031-54533-7978-3-031-54534-4Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: Scintillations 時(shí)間: 2025-3-22 06:21
0302-9743 mages, Social 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; 作者: Missile 時(shí)間: 2025-3-22 11:21 作者: gastritis 時(shí)間: 2025-3-22 12:54
Alan Brown,Jerry Fishenden,Mark Thompsonges through semantic shifts in the News and Social media corpora; the latter was collected and released as a part of this work. In addition, we compare the performance of these three approaches and highlight their strengths and weaknesses for this task.作者: 違抗 時(shí)間: 2025-3-22 19:32 作者: magnate 時(shí)間: 2025-3-22 22:16
Static, Dynamic, or?Contextualized: What is the?Best Approach for?Discovering Semantic Shifts in?Rusges through semantic shifts in the News and Social media corpora; the latter was collected and released as a part of this work. In addition, we compare the performance of these three approaches and highlight their strengths and weaknesses for this task.作者: 心神不寧 時(shí)間: 2025-3-23 04:14 作者: 劇毒 時(shí)間: 2025-3-23 09:32
RuCAM: Comparative Argumentative Machine for?the?Russian Languagen with respect to information extracted from the OSCAR corpus. We also introduce several datasets for the RuCAM subtasks: comparative question classification, object and aspect identification, comparative sentences classification. We provide models for each subtask and compare them with the existing baselines.作者: 踉蹌 時(shí)間: 2025-3-23 09:45
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.?.作者: nocturnal 時(shí)間: 2025-3-23 14:21 作者: Adenoma 時(shí)間: 2025-3-23 21:03
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.作者: 不可救藥 時(shí)間: 2025-3-23 23:54
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作者: BLANC 時(shí)間: 2025-3-24 02:54
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.作者: expdient 時(shí)間: 2025-3-24 10:08 作者: installment 時(shí)間: 2025-3-24 11:17
Numan M. Durakbasa,M. Güne? Gen?y?lmaz 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.作者: 責(zé)問(wèn) 時(shí)間: 2025-3-24 15:54
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.作者: Graphite 時(shí)間: 2025-3-24 21:59
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.作者: Melatonin 時(shí)間: 2025-3-25 01:25
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.作者: 排他 時(shí)間: 2025-3-25 05:46
Threatening Expression and Target Identification in Under-Resource Languages Using NLP Techniquesrning technique with grid search fine-tuning. The proposed framework is compared with state-of-the art baseline and ten comparable models and it outperformed all for both tasks (threatening expression and target identification). Furthermore, the proposed framework obtained benchmark performance and 作者: COW 時(shí)間: 2025-3-25 10:02 作者: 出處 時(shí)間: 2025-3-25 15:11 作者: Assault 時(shí)間: 2025-3-25 17:01
Less than Necessary or?More than Sufficient: Validating Probing Dataset Sizeite, finding that it can be safely reduced. Our experiments are conducted for two models, BERT and RoBERTa, showing the latter to consistently require more data. Fractions probing can be used to analogously investigate other datasets and models.作者: 使迷惑 時(shí)間: 2025-3-25 20:30 作者: 索賠 時(shí)間: 2025-3-26 02:09
Automatic Detection of?Dialectal Features of?Pskov Dialects in?the?Speech of?Native Speakersapproaches to detecting dialect features, and identified the advantages and disadvantages of each. Ultimately, we developed a unified algorithm that incorporates the best of the approaches considered, it takes a .wav file as input and returns .TextGrid and .eaf files with annotations and detected di作者: peptic-ulcer 時(shí)間: 2025-3-26 06:25
MiVOLO: Multi-input Transformer for?Age and?Gender EstimationImages Dataset. The ground truth annotations for this benchmark have been meticulously generated by human annotators, resulting in high accuracy answers due to the smart aggregation of votes. Furthermore, we compare our model’s age recognition performance with human-level accuracy and demonstrate th作者: deviate 時(shí)間: 2025-3-26 12:22
Handwritten Text Recognition and?Browsing in?Archive of?Prisoners’ Letters from?Smolensk Convict Priors. The search engine reached a precision of 97.14% and a recall of 91.35%. Visualization of the results provided highlighting of the found words on the original images. The study conducted demonstrates the possibility of creating a navigation system and its fitting to a specific handwriting with a作者: 傳授知識(shí) 時(shí)間: 2025-3-26 14:35 作者: 無(wú)彈性 時(shí)間: 2025-3-26 20:16 作者: Meager 時(shí)間: 2025-3-26 23:02 作者: cardiovascular 時(shí)間: 2025-3-27 04:48
https://doi.org/10.1007/978-981-19-8245-3toxification, a relatively new yet practical TST subtask. We work with detoxification in two languages: Russian and English. For both languages, ParaCAIF significantly reduces the toxicity of the generated paraphrases as compared to plain paraphrasers. To the best of our knowledge, it is the first w作者: eardrum 時(shí)間: 2025-3-27 06:00
https://doi.org/10.1057/9781137443649ite, finding that it can be safely reduced. Our experiments are conducted for two models, BERT and RoBERTa, showing the latter to consistently require more data. Fractions probing can be used to analogously investigate other datasets and models.作者: FLAGR 時(shí)間: 2025-3-27 11:58 作者: transient-pain 時(shí)間: 2025-3-27 16:45
Alan Brown,Jerry Fishenden,Mark Thompsonapproaches to detecting dialect features, and identified the advantages and disadvantages of each. Ultimately, we developed a unified algorithm that incorporates the best of the approaches considered, it takes a .wav file as input and returns .TextGrid and .eaf files with annotations and detected di作者: 步兵 時(shí)間: 2025-3-27 20:45 作者: milligram 時(shí)間: 2025-3-28 01:32 作者: archetype 時(shí)間: 2025-3-28 02:29
Threatening Expression and Target Identification in Under-Resource Languages Using NLP Techniquestes unrest and harms to the society. Literature describes several forms of the hate speech and it is quite challenging to differentiate between these forms and to design an automated detection system, especially for under-resource languages. In this study, we propose a robust framework for threateni作者: 全面 時(shí)間: 2025-3-28 09:09 作者: vitrectomy 時(shí)間: 2025-3-28 12:32 作者: HARD 時(shí)間: 2025-3-28 16:39 作者: Eclampsia 時(shí)間: 2025-3-28 21:37 作者: Eructation 時(shí)間: 2025-3-29 00:41
RuCAM: Comparative Argumentative Machine for?the?Russian Languageexplain the choice and support it with arguments. ChatGPT-like models are able nowadays to generate a coherent answer in a natural language, however, they are not fully reliable as they are not publicly accessible and tend to hallucinate. Another solution is a Comparative Argument Machine (CAM), whi作者: Blanch 時(shí)間: 2025-3-29 06:46
Paraphrasers and?Classifiers: Controllable Text Generation for?Text Style Transferetrained language models (LMs). However, the size of contemporary LMs often makes fine-tuning for downstream tasks infeasible. For this reason, methods of controllable text generation (CTG) which do not aim at fine-tuning the original LM have received attention for solving TST tasks. In this work, w作者: Cosmopolitan 時(shí)間: 2025-3-29 10:07 作者: ethereal 時(shí)間: 2025-3-29 13:17
Unsupervised Ultra-Fine Entity Typing with?Distributionally Induced Word Sensesity mention. Hence, automatic type generation is receiving increased interest, typically to be used as distant supervision data. In this study, we investigate an unsupervised way based on distributionally induced word senses. The types or labels are obtained by selecting the appropriate sense cluste作者: Glaci冰 時(shí)間: 2025-3-29 17:52 作者: 制造 時(shí)間: 2025-3-29 23:22 作者: 交響樂(lè) 時(shí)間: 2025-3-30 03:49
Automatic Detection of?Dialectal Features of?Pskov Dialects in?the?Speech of?Native Speakersristic of the Pskov dialects found on the territory of the Opochetsky district of the Pskov region and the Zapadnodvinsky district of the Tver region. The task is divided into two parts: speech recognition and features detection. First of all, we developed a model, the functionality of which include作者: 毗鄰 時(shí)間: 2025-3-30 07:34 作者: 裂縫 時(shí)間: 2025-3-30 09:42
DeepLOC: Deep Learning-Based Bone Pathology Localization and?Classification in?Wrist X-Ray Imagessis. This paper presents a novel approach for bone pathology localization and classification in wrist X-ray images using a combination of YOLO (You Only Look Once) and the Shifted Window Transformer (Swin) with a newly proposed block. The proposed methodology addresses two critical challenges in wri作者: 反抗者 時(shí)間: 2025-3-30 12:47
MiVOLO: Multi-input Transformer for?Age and?Gender Estimationality, there are cases where the face is partially or completely occluded. We present MiVOLO (Multi Input VOLO), a straightforward approach for age and gender estimation using the latest vision transformer. Our method integrates both tasks into a unified dual input/output model, leveraging not only 作者: CLAMP 時(shí)間: 2025-3-30 17:06
Handwritten Text Recognition and?Browsing in?Archive of?Prisoners’ Letters from?Smolensk Convict Prin of the early 20th century) recorded in a single handwriting, is considered. To fit a model for handwritten text recognition, procedures were created for automatic preparation of image collections, including breaking into lines, pen trace segmentation, and deslanting of lines and pages. Experiments作者: 沖突 時(shí)間: 2025-3-30 21:07
Takashi Inoguchi,Lien Thi Quynh Letes unrest and harms to the society. Literature describes several forms of the hate speech and it is quite challenging to differentiate between these forms and to design an automated detection system, especially for under-resource languages. In this study, we propose a robust framework for threateni
