Artigos de revistas sobre o tema "Neural Network Embeddings"
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Che, Feihu, Dawei Zhang, Jianhua Tao, Mingyue Niu e Bocheng Zhao. "ParamE: Regarding Neural Network Parameters as Relation Embeddings for Knowledge Graph Completion". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 03 (3 de abril de 2020): 2774–81. http://dx.doi.org/10.1609/aaai.v34i03.5665.
Texto completo da fonteHuang, Junjie, Huawei Shen, Liang Hou e Xueqi Cheng. "SDGNN: Learning Node Representation for Signed Directed Networks". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 1 (18 de maio de 2021): 196–203. http://dx.doi.org/10.1609/aaai.v35i1.16093.
Texto completo da fonteSrinidhi, K., T. L.S Tejaswi, CH Rama Rupesh Kumar e I. Sai Siva Charan. "An Advanced Sentiment Embeddings with Applications to Sentiment Based Result Analysis". International Journal of Engineering & Technology 7, n.º 2.32 (31 de maio de 2018): 393. http://dx.doi.org/10.14419/ijet.v7i2.32.15721.
Texto completo da fonteArmandpour, Mohammadreza, Patrick Ding, Jianhua Huang e Xia Hu. "Robust Negative Sampling for Network Embedding". Proceedings of the AAAI Conference on Artificial Intelligence 33 (17 de julho de 2019): 3191–98. http://dx.doi.org/10.1609/aaai.v33i01.33013191.
Texto completo da fonteKamath, S., K. G. Karibasappa, Anvitha Reddy, Arati M. Kallur, B. B. Priyanka e B. P. Bhagya. "Improving the Relation Classification Using Convolutional Neural Network". IOP Conference Series: Materials Science and Engineering 1187, n.º 1 (1 de setembro de 2021): 012004. http://dx.doi.org/10.1088/1757-899x/1187/1/012004.
Texto completo da fonteGu, Haishuo, Jinguang Sui e Peng Chen. "Graph Representation Learning for Street-Level Crime Prediction". ISPRS International Journal of Geo-Information 13, n.º 7 (1 de julho de 2024): 229. http://dx.doi.org/10.3390/ijgi13070229.
Texto completo da fonteZhang, Lei, Feng Qian, Jie Chen e Shu Zhao. "An Unsupervised Rapid Network Alignment Framework via Network Coarsening". Mathematics 11, n.º 3 (21 de janeiro de 2023): 573. http://dx.doi.org/10.3390/math11030573.
Texto completo da fonteTruică, Ciprian-Octavian, Elena-Simona Apostol, Maria-Luiza Șerban e Adrian Paschke. "Topic-Based Document-Level Sentiment Analysis Using Contextual Cues". Mathematics 9, n.º 21 (27 de outubro de 2021): 2722. http://dx.doi.org/10.3390/math9212722.
Texto completo da fonteJang, Youngjin, e Harksoo Kim. "Reliable Classification of FAQs with Spelling Errors Using an Encoder-Decoder Neural Network in Korean". Applied Sciences 9, n.º 22 (7 de novembro de 2019): 4758. http://dx.doi.org/10.3390/app9224758.
Texto completo da fonteGuo, Lei, Haoran Jiang, Xiyu Liu e Changming Xing. "Network Embedding-Aware Point-of-Interest Recommendation in Location-Based Social Networks". Complexity 2019 (4 de novembro de 2019): 1–18. http://dx.doi.org/10.1155/2019/3574194.
Texto completo da fonteNguyen, Van Quan, Tien Nguyen Anh e Hyung-Jeong Yang. "Real-time event detection using recurrent neural network in social sensors". International Journal of Distributed Sensor Networks 15, n.º 6 (junho de 2019): 155014771985649. http://dx.doi.org/10.1177/1550147719856492.
Texto completo da fonteJadon, Anil Kumar, e Suresh Kumar. "Enhancing emotion detection with synergistic combination of word embeddings and convolutional neural networks". Indonesian Journal of Electrical Engineering and Computer Science 35, n.º 3 (1 de setembro de 2024): 1933. http://dx.doi.org/10.11591/ijeecs.v35.i3.pp1933-1941.
Texto completo da fonteAltuntas, Volkan. "NodeVector: A Novel Network Node Vectorization with Graph Analysis and Deep Learning". Applied Sciences 14, n.º 2 (16 de janeiro de 2024): 775. http://dx.doi.org/10.3390/app14020775.
Texto completo da fonteJbene, Mourad, Smail Tigani, Saadane Rachid e Abdellah Chehri. "Deep Neural Network and Boosting Based Hybrid Quality Ranking for e-Commerce Product Search". Big Data and Cognitive Computing 5, n.º 3 (13 de agosto de 2021): 35. http://dx.doi.org/10.3390/bdcc5030035.
Texto completo da fontePopov, Alexander. "Neural Network Models for Word Sense Disambiguation: An Overview". Cybernetics and Information Technologies 18, n.º 1 (1 de março de 2018): 139–51. http://dx.doi.org/10.2478/cait-2018-0012.
Texto completo da fonteHu, Ganglin, e Jun Pang. "Relation-Aware Weighted Embedding for Heterogeneous Graphs". Information Technology and Control 52, n.º 1 (28 de março de 2023): 199–214. http://dx.doi.org/10.5755/j01.itc.52.1.32390.
Texto completo da fonteBui-Thi, Danh, Emmanuel Rivière, Pieter Meysman e Kris Laukens. "Predicting compound-protein interaction using hierarchical graph convolutional networks". PLOS ONE 17, n.º 7 (21 de julho de 2022): e0258628. http://dx.doi.org/10.1371/journal.pone.0258628.
Texto completo da fonteWang, Bin, Yu Chen, Jinfang Sheng e Zhengkun He. "Attributed Graph Embedding Based on Attention with Cluster". Mathematics 10, n.º 23 (1 de dezembro de 2022): 4563. http://dx.doi.org/10.3390/math10234563.
Texto completo da fonteEyharabide, Victoria, Imad Eddine Ibrahim Bekkouch e Nicolae Dragoș Constantin. "Knowledge Graph Embedding-Based Domain Adaptation for Musical Instrument Recognition". Computers 10, n.º 8 (3 de agosto de 2021): 94. http://dx.doi.org/10.3390/computers10080094.
Texto completo da fonteBoldakov, V. "Emotional Speech Synthesis with Emotion Embeddings". Herald of the Siberian State University of Telecommunications and Informatics, n.º 4 (18 de dezembro de 2021): 23–31. http://dx.doi.org/10.55648/1998-6920-2021-15-4-23-31.
Texto completo da fonteOta, Kosuke, Keiichiro Shirai, Hidetoshi Miyao e Minoru Maruyama. "Multimodal Analogy-Based Image Retrieval by Improving Semantic Embeddings". Journal of Advanced Computational Intelligence and Intelligent Informatics 26, n.º 6 (20 de novembro de 2022): 995–1003. http://dx.doi.org/10.20965/jaciii.2022.p0995.
Texto completo da fonteTakase, Sho, Jun Suzuki e Masaaki Nagata. "Character n-Gram Embeddings to Improve RNN Language Models". Proceedings of the AAAI Conference on Artificial Intelligence 33 (17 de julho de 2019): 5074–82. http://dx.doi.org/10.1609/aaai.v33i01.33015074.
Texto completo da fonteNguyen, Andre T., Fred Lu, Gary Lopez Munoz, Edward Raff, Charles Nicholas e James Holt. "Out of Distribution Data Detection Using Dropout Bayesian Neural Networks". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 7 (28 de junho de 2022): 7877–85. http://dx.doi.org/10.1609/aaai.v36i7.20757.
Texto completo da fonteP. Bhopale, Bhopale, e Ashish Tiwari. "LEVERAGING NEURAL NETWORK PHRASE EMBEDDING MODEL FOR QUERY REFORMULATION IN AD-HOC BIOMEDICAL INFORMATION RETRIEVAL". Malaysian Journal of Computer Science 34, n.º 2 (30 de abril de 2021): 151–70. http://dx.doi.org/10.22452/mjcs.vol34no2.2.
Texto completo da fonteGao, Yan, Yandong Wang, Patrick Wang e Lei Gu. "Medical Named Entity Extraction from Chinese Resident Admit Notes Using Character and Word Attention-Enhanced Neural Network". International Journal of Environmental Research and Public Health 17, n.º 5 (2 de março de 2020): 1614. http://dx.doi.org/10.3390/ijerph17051614.
Texto completo da fonteNg, Michael K., Hanrui Wu e Andy Yip. "Stability and Generalization of Hypergraph Collaborative Networks". Machine Intelligence Research 21, n.º 1 (15 de janeiro de 2024): 184–96. http://dx.doi.org/10.1007/s11633-022-1397-1.
Texto completo da fonteWu, Xueyi, Yuanyuan Xu, Wenjie Zhang e Ying Zhang. "Billion-Scale Bipartite Graph Embedding: A Global-Local Induced Approach". Proceedings of the VLDB Endowment 17, n.º 2 (outubro de 2023): 175–83. http://dx.doi.org/10.14778/3626292.3626300.
Texto completo da fonteHagad, Juan Lorenzo, Tsukasa Kimura, Ken-ichi Fukui e Masayuki Numao. "Learning Subject-Generalized Topographical EEG Embeddings Using Deep Variational Autoencoders and Domain-Adversarial Regularization". Sensors 21, n.º 5 (4 de março de 2021): 1792. http://dx.doi.org/10.3390/s21051792.
Texto completo da fonteKim, Harang, e Hyun Min Song. "Lightweight IDS Framework Using Word Embeddings for In-Vehicle Network Security". Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications 15, n.º 2 (29 de junho de 2022): 1–13. http://dx.doi.org/10.58346/jowua.2024.i2.001.
Texto completo da fonteLi, Wenli, e Gang Wu. "One-shot Based Knowledge Graph Embedded Neural Architecture Search Algorithm". Frontiers in Computing and Intelligent Systems 3, n.º 3 (4 de maio de 2023): 1–5. http://dx.doi.org/10.54097/fcis.v3i3.7982.
Texto completo da fonteZhang, Kainan, Zhipeng Cai e Daehee Seo. "Privacy-Preserving Federated Graph Neural Network Learning on Non-IID Graph Data". Wireless Communications and Mobile Computing 2023 (3 de fevereiro de 2023): 1–13. http://dx.doi.org/10.1155/2023/8545101.
Texto completo da fontePeng, Hao, Qing Ke, Ceren Budak, Daniel M. Romero e Yong-Yeol Ahn. "Neural embeddings of scholarly periodicals reveal complex disciplinary organizations". Science Advances 7, n.º 17 (abril de 2021): eabb9004. http://dx.doi.org/10.1126/sciadv.abb9004.
Texto completo da fonteÖzkaya Eren, Ayşegül, e Mustafa Sert. "Audio Captioning with Composition of Acoustic and Semantic Information". International Journal of Semantic Computing 15, n.º 02 (junho de 2021): 143–60. http://dx.doi.org/10.1142/s1793351x21400018.
Texto completo da fonteYe, Yutong, Xiang Lian e Mingsong Chen. "Efficient Exact Subgraph Matching via GNN-Based Path Dominance Embedding". Proceedings of the VLDB Endowment 17, n.º 7 (março de 2024): 1628–41. http://dx.doi.org/10.14778/3654621.3654630.
Texto completo da fonteCroce, Danilo, Daniele Rossini e Roberto Basili. "Neural embeddings: accurate and readable inferences based on semantic kernels". Natural Language Engineering 25, n.º 4 (julho de 2019): 519–41. http://dx.doi.org/10.1017/s1351324919000238.
Texto completo da fonteZhou, Silin, Jing Li, Hao Wang, Shuo Shang e Peng Han. "GRLSTM: Trajectory Similarity Computation with Graph-Based Residual LSTM". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 4 (26 de junho de 2023): 4972–80. http://dx.doi.org/10.1609/aaai.v37i4.25624.
Texto completo da fonteTzougas, George, e Konstantin Kutzkov. "Enhancing Logistic Regression Using Neural Networks for Classification in Actuarial Learning". Algorithms 16, n.º 2 (9 de fevereiro de 2023): 99. http://dx.doi.org/10.3390/a16020099.
Texto completo da fonteChang, Zhihao, Linzhu Yu, Yanchao Xu e Wentao Hu. "Neural Embeddings for kNN Search in Biological Sequence". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 1 (24 de março de 2024): 38–45. http://dx.doi.org/10.1609/aaai.v38i1.27753.
Texto completo da fonteXu, You-Wei, Hong-Jun Zhang, Kai Cheng, Xiang-Lin Liao, Zi-Xuan Zhang e Yun-Bo Li. "Knowledge graph embedding with entity attributes using hypergraph neural networks". Intelligent Data Analysis 26, n.º 4 (11 de julho de 2022): 959–75. http://dx.doi.org/10.3233/ida-216007.
Texto completo da fonteZhong, Fengzhe, Yan Liu, Lian Liu, Guangsheng Zhang e Shunran Duan. "DEDGCN: Dual Evolving Dynamic Graph Convolutional Network". Security and Communication Networks 2022 (10 de maio de 2022): 1–11. http://dx.doi.org/10.1155/2022/6945397.
Texto completo da fonteZhang, Yuanpeng, Jingye Guan, Haobo Wang, Kaiming Li, Ying Luo e Qun Zhang. "Generalized Zero-Shot Space Target Recognition Based on Global-Local Visual Feature Embedding Network". Remote Sensing 15, n.º 21 (28 de outubro de 2023): 5156. http://dx.doi.org/10.3390/rs15215156.
Texto completo da fonteE., Koshel. "Нейронно-мережевий підхід до неперервного вкладення одновимірних потоків даних для аналізу часових рядів в реальному часі". System technologies 2, n.º 151 (17 de abril de 2024): 92–101. http://dx.doi.org/10.34185/1562-9945-2-151-2024-08.
Texto completo da fonteLevy, Omer, Yoav Goldberg e Ido Dagan. "Improving Distributional Similarity with Lessons Learned from Word Embeddings". Transactions of the Association for Computational Linguistics 3 (dezembro de 2015): 211–25. http://dx.doi.org/10.1162/tacl_a_00134.
Texto completo da fonteWang, Yu, Ke Wang, Fengjuan Gao e Linzhang Wang. "Learning semantic program embeddings with graph interval neural network". Proceedings of the ACM on Programming Languages 4, OOPSLA (13 de novembro de 2020): 1–27. http://dx.doi.org/10.1145/3428205.
Texto completo da fonteEliyahu Sason, Yackov Lubarsky, Alexei Gaissinski, Eli Kravchik e Pavel Kisilev. "Oracle-based data generation for highly efficient digital twin network training". ITU Journal on Future and Evolving Technologies 4, n.º 3 (8 de setembro de 2023): 472–84. http://dx.doi.org/10.52953/aweu6345.
Texto completo da fonteHu, Shengze, Weixin Zeng, Pengfei Zhang e Jiuyang Tang. "Neural Graph Similarity Computation with Contrastive Learning". Applied Sciences 12, n.º 15 (29 de julho de 2022): 7668. http://dx.doi.org/10.3390/app12157668.
Texto completo da fonteSun, Xia, Ke Dong, Long Ma, Richard Sutcliffe, Feijuan He, Sushing Chen e Jun Feng. "Drug-Drug Interaction Extraction via Recurrent Hybrid Convolutional Neural Networks with an Improved Focal Loss". Entropy 21, n.º 1 (8 de janeiro de 2019): 37. http://dx.doi.org/10.3390/e21010037.
Texto completo da fonteSi, Yuqi, Jingqi Wang, Hua Xu e Kirk Roberts. "Enhancing clinical concept extraction with contextual embeddings". Journal of the American Medical Informatics Association 26, n.º 11 (2 de julho de 2019): 1297–304. http://dx.doi.org/10.1093/jamia/ocz096.
Texto completo da fonteZhuang, Chengxu, Siming Yan, Aran Nayebi, Martin Schrimpf, Michael C. Frank, James J. DiCarlo e Daniel L. K. Yamins. "Unsupervised neural network models of the ventral visual stream". Proceedings of the National Academy of Sciences 118, n.º 3 (11 de janeiro de 2021): e2014196118. http://dx.doi.org/10.1073/pnas.2014196118.
Texto completo da fonteWang, Chaoyi. "Collaborative filtering method based on graph neural network". Applied and Computational Engineering 6, n.º 1 (14 de junho de 2023): 1288–94. http://dx.doi.org/10.54254/2755-2721/6/20230710.
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