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Статті в журналах з теми "CNN embedding networks"
David, Merlin Susan, and Shini Renjith. "Comparison of word embeddings in text classification based on RNN and CNN." IOP Conference Series: Materials Science and Engineering 1187, no. 1 (September 1, 2021): 012029. http://dx.doi.org/10.1088/1757-899x/1187/1/012029.
Повний текст джерелаRhanoui, Maryem, Mounia Mikram, Siham Yousfi, and Soukaina Barzali. "A CNN-BiLSTM Model for Document-Level Sentiment Analysis." Machine Learning and Knowledge Extraction 1, no. 3 (July 25, 2019): 832–47. http://dx.doi.org/10.3390/make1030048.
Повний текст джерелаSelvarajah, Jarashanth, and Ruwan Nawarathna. "Identifying Tweets with Personal Medication Intake Mentions using Attentive Character and Localized Context Representations." JUCS - Journal of Universal Computer Science 28, no. 12 (December 28, 2022): 1312–29. http://dx.doi.org/10.3897/jucs.84130.
Повний текст джерелаTang, Weixuan, Bin Li, Shunquan Tan, Mauro Barni, and Jiwu Huang. "CNN-Based Adversarial Embedding for Image Steganography." IEEE Transactions on Information Forensics and Security 14, no. 8 (August 2019): 2074–87. http://dx.doi.org/10.1109/tifs.2019.2891237.
Повний текст джерелаZheng, Zhedong, Liang Zheng, and Yi Yang. "A Discriminatively Learned CNN Embedding for Person Reidentification." ACM Transactions on Multimedia Computing, Communications, and Applications 14, no. 1 (January 16, 2018): 1–20. http://dx.doi.org/10.1145/3159171.
Повний текст джерелаWang, Rong, Cong Tian, and Lin Yan. "Malware Detection Using CNN via Word Embedding in Cloud Computing Infrastructure." Scientific Programming 2021 (September 11, 2021): 1–7. http://dx.doi.org/10.1155/2021/8381550.
Повний текст джерелаLiu, Han, Jun Li, Lin He, and Yu Wang. "Superpixel-Guided Layer-Wise Embedding CNN for Remote Sensing Image Classification." Remote Sensing 11, no. 2 (January 17, 2019): 174. http://dx.doi.org/10.3390/rs11020174.
Повний текст джерелаKim, Jaeyoung, Hanhoon Park, and Jong-Il Park. "CNN-based image steganalysis using additional data embedding." Multimedia Tools and Applications 79, no. 1-2 (October 31, 2019): 1355–72. http://dx.doi.org/10.1007/s11042-019-08251-3.
Повний текст джерелаLi, Yue, Hongqi Wang, Liqun Yu, Sarah Yvonne Cooper, and Jing-Yan Wang. "Query-Specific Deep Embedding of Content-Rich Network." Computational Intelligence and Neuroscience 2020 (August 25, 2020): 1–11. http://dx.doi.org/10.1155/2020/5943798.
Повний текст джерелаLi, Na, Deyun Zhou, Jiao Shi, Mingyang Zhang, Tao Wu, and Maoguo Gong. "Deep Fully Convolutional Embedding Networks for Hyperspectral Images Dimensionality Reduction." Remote Sensing 13, no. 4 (February 15, 2021): 706. http://dx.doi.org/10.3390/rs13040706.
Повний текст джерелаДисертації з теми "CNN embedding networks"
Wang, Run Fen. "Semantic Text Matching Using Convolutional Neural Networks." Thesis, Uppsala universitet, Institutionen för lingvistik och filologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-362134.
Повний текст джерелаHameed, Khurram. "Computer vision based classification of fruits and vegetables for self-checkout at supermarkets." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2022. https://ro.ecu.edu.au/theses/2519.
Повний текст джерелаŠůstek, Martin. "Word2vec modely s přidanou kontextovou informací." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2017. http://www.nusl.cz/ntk/nusl-363837.
Повний текст джерелаGong, Rong. "Automatic assessment of singing voice pronunciation: a case study with Jingju music." Doctoral thesis, Universitat Pompeu Fabra, 2018. http://hdl.handle.net/10803/664421.
Повний текст джерелаEl aprendizaje en línea ha cambiado notablemente la educación musical en la pasada década. Una cada vez mayor cantidad de estudiantes de interpretación musical participan en cursos de aprendizaje musical en línea por su fácil accesibilidad y no estar limitada por restricciones de tiempo y espacio. Puede considerarse el canto como la forma más básica de interpretación. La evaluación automática de la voz cantada, como tarea importante en la disciplina de Recuperación de Información Musical (MIR por sus siglas en inglés) tiene como objetivo la extracción de información musicalmente significativa y la medición de la calidad de la voz cantada del estudiante. La corrección y calidad del canto son específicas a cada cultura y su evaluación requiere metodologías con especificidad cultural. La música del jingju (también conocido como ópera de Beijing) es una de las tradiciones musicales más representativas de China y se ha difundido a muchos lugares del mundo donde existen comunidades chinas.Nuestro objetivo es abordar problemas aún no explorados sobre la evaluación automática de la voz cantada en la música del jingju, hacer que las propuestas eurogenéticas actuales sobre evaluación sean más específicas culturalmente, y al mismo tiempo, desarrollar nuevas propuestas sobre evaluación que puedan ser generalizables para otras tradiciones musicales.
Книги з теми "CNN embedding networks"
Schäfer, Anne, and Rüdiger Schmitt-Beck. A Vicious Circle of Demobilization? Context Effects on Turnout at the 2009 and 2013 German Federal Elections. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198792130.003.0006.
Повний текст джерелаUnger, Herwig, and Wolfgang A. Halang, eds. Autonomous Systems 2016. VDI Verlag, 2016. http://dx.doi.org/10.51202/9783186848109.
Повний текст джерелаKubek, Maria M., and Zhong Li, eds. Autonomous Systems 2018. VDI Verlag, 2018. http://dx.doi.org/10.51202/9783186862105.
Повний текст джерелаЧастини книг з теми "CNN embedding networks"
Pflueger, Maximilian, David J. Tena Cucala, and Egor V. Kostylev. "GNNQ: A Neuro-Symbolic Approach to Query Answering over Incomplete Knowledge Graphs." In The Semantic Web – ISWC 2022, 481–97. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-19433-7_28.
Повний текст джерелаBarbaglia, Luca, Sergio Consoli, and Sebastiano Manzan. "Exploring the Predictive Power of News and Neural Machine Learning Models for Economic Forecasting." In Mining Data for Financial Applications, 135–49. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66981-2_11.
Повний текст джерелаZhou, Bolei. "Interpreting Generative Adversarial Networks for Interactive Image Generation." In xxAI - Beyond Explainable AI, 167–75. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04083-2_9.
Повний текст джерелаKoltai, Júlia, Zoltán Kmetty, and Károly Bozsonyi. "From Durkheim to Machine Learning: Finding the Relevant Sociological Content in Depression and Suicide-Related Social Media Discourses." In Pathways Between Social Science and Computational Social Science, 237–58. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-54936-7_11.
Повний текст джерелаXu, Yuemei, Zuwei Fan, and Han Cao. "A Multi-task Text Classification Model Based on Label Embedding Learning." In Communications in Computer and Information Science, 211–25. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9229-1_13.
Повний текст джерелаZhao, Zhangjie, Lin Zhang, Xing Zhang, Ying Wang, and Yi Qin. "CMPD: Context-Based Malicious Parameter Detection for APIs." In Communications in Computer and Information Science, 99–112. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-8285-9_7.
Повний текст джерелаJain, Gauri, Manisha Sharma, and Basant Agarwal. "Spam Detection on Social Media Using Semantic Convolutional Neural Network." In Deep Learning and Neural Networks, 704–19. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-0414-7.ch039.
Повний текст джерелаRayala, Upendar Rao, and Karthick Seshadri. "Word Embedding Techniques for Sentiment Analyzers." In Advances in Data Mining and Database Management, 233–52. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-8061-5.ch013.
Повний текст джерелаDenecke, Kerstin. "Does Enrichment of Clinical Texts by Ontology Concepts Increases Classification Accuracy?" In MEDINFO 2021: One World, One Health – Global Partnership for Digital Innovation. IOS Press, 2022. http://dx.doi.org/10.3233/shti220148.
Повний текст джерелаYe, Wei-Cheng, and Jia-Ching Wang. "Multilabel Classification Based on Graph Neural Networks." In Artificial Intelligence. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.99681.
Повний текст джерелаТези доповідей конференцій з теми "CNN embedding networks"
Sheikh, Nasrullah, Zekarias T. Kefato, and Alberto Montresor. "Semi-Supervised Heterogeneous Information Network Embedding for Node Classification Using 1D-CNN." In 2018 Fifth International Conference on Social Networks Analysis, Management and Security (SNAMS). IEEE, 2018. http://dx.doi.org/10.1109/snams.2018.8554840.
Повний текст джерелаJiang, Junjun, Yi Yu, Jinhui Hu, Suhua Tang, and Jiayi Ma. "Deep CNN Denoiser and Multi-layer Neighbor Component Embedding for Face Hallucination." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/107.
Повний текст джерелаZhang, Yizhou, Guojie Song, Lun Du, Shuwen Yang, and Yilun Jin. "DANE: Domain Adaptive Network Embedding." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/606.
Повний текст джерелаZhao, Gangming, Jingdong Wang, and Zhaoxiang Zhang. "Random Shifting for CNN: a Solution to Reduce Information Loss in Down-Sampling Layers." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/486.
Повний текст джерелаZhang, Jie, Yuxiao Dong, Yan Wang, Jie Tang, and Ming Ding. "ProNE: Fast and Scalable Network Representation Learning." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/594.
Повний текст джерелаDong, Yuxiao, Ziniu Hu, Kuansan Wang, Yizhou Sun, and Jie Tang. "Heterogeneous Network Representation Learning." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/677.
Повний текст джерелаSun, Yiwei, Suhang Wang, Tsung-Yu Hsieh, Xianfeng Tang, and Vasant Honavar. "MEGAN: A Generative Adversarial Network for Multi-View Network Embedding." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/489.
Повний текст джерелаGuo, Junliang, Linli Xu, and Jingchang Liu. "SPINE: Structural Identity Preserved Inductive Network Embedding." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/333.
Повний текст джерелаChan, Patrick P. K., Xian Hu, Lili Zhao, Daniel S. Yeung, Dapeng Liu, and Lei Xiao. "Convolutional Neural Networks based Click-Through Rate Prediction with Multiple Feature Sequences." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/277.
Повний текст джерелаdo Carmo, P., I. J. Reis Filho, and R. Marcacini. "Commodities trend link prediction on heterogeneous information networks." In Symposium on Knowledge Discovery, Mining and Learning. Sociedade Brasileira de Computação - SBC, 2021. http://dx.doi.org/10.5753/kdmile.2021.17464.
Повний текст джерелаЗвіти організацій з теми "CNN embedding networks"
Bano, Masooda, and Zeena Oberoi. Embedding Innovation in State Systems: Lessons from Pratham in India. Research on Improving Systems of Education (RISE), December 2020. http://dx.doi.org/10.35489/bsg-rise-wp_2020/058.
Повний текст джерелаKelly, Luke. Lessons Learned on Cultural Heritage Protection in Conflict and Protracted Crisis. Institute of Development Studies (IDS), April 2021. http://dx.doi.org/10.19088/k4d.2021.068.
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