Letteratura scientifica selezionata sul tema "Neural Network Embeddings"
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Articoli di riviste sul tema "Neural Network Embeddings"
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. 03 (3 aprile 2020): 2774–81. http://dx.doi.org/10.1609/aaai.v34i03.5665.
Testo completoHuang, 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. 1 (18 maggio 2021): 196–203. http://dx.doi.org/10.1609/aaai.v35i1.16093.
Testo completoSrinidhi, 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. 2.32 (31 maggio 2018): 393. http://dx.doi.org/10.14419/ijet.v7i2.32.15721.
Testo completoArmandpour, Mohammadreza, Patrick Ding, Jianhua Huang e Xia Hu. "Robust Negative Sampling for Network Embedding". Proceedings of the AAAI Conference on Artificial Intelligence 33 (17 luglio 2019): 3191–98. http://dx.doi.org/10.1609/aaai.v33i01.33013191.
Testo completoKamath, 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. 1 (1 settembre 2021): 012004. http://dx.doi.org/10.1088/1757-899x/1187/1/012004.
Testo completoGu, Haishuo, Jinguang Sui e Peng Chen. "Graph Representation Learning for Street-Level Crime Prediction". ISPRS International Journal of Geo-Information 13, n. 7 (1 luglio 2024): 229. http://dx.doi.org/10.3390/ijgi13070229.
Testo completoZhang, Lei, Feng Qian, Jie Chen e Shu Zhao. "An Unsupervised Rapid Network Alignment Framework via Network Coarsening". Mathematics 11, n. 3 (21 gennaio 2023): 573. http://dx.doi.org/10.3390/math11030573.
Testo completoTruică, Ciprian-Octavian, Elena-Simona Apostol, Maria-Luiza Șerban e Adrian Paschke. "Topic-Based Document-Level Sentiment Analysis Using Contextual Cues". Mathematics 9, n. 21 (27 ottobre 2021): 2722. http://dx.doi.org/10.3390/math9212722.
Testo completoJang, Youngjin, e Harksoo Kim. "Reliable Classification of FAQs with Spelling Errors Using an Encoder-Decoder Neural Network in Korean". Applied Sciences 9, n. 22 (7 novembre 2019): 4758. http://dx.doi.org/10.3390/app9224758.
Testo completoGuo, Lei, Haoran Jiang, Xiyu Liu e Changming Xing. "Network Embedding-Aware Point-of-Interest Recommendation in Location-Based Social Networks". Complexity 2019 (4 novembre 2019): 1–18. http://dx.doi.org/10.1155/2019/3574194.
Testo completoTesi sul tema "Neural Network Embeddings"
Embretsén, Niklas. "Representing Voices Using Convolutional Neural Network Embeddings". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-261415.
Testo completoI dagens samhälle ökar populariteten för röstbaserade tjänster. Att kunna förse användare med röster de tycker om, för att fånga och behålla deras uppmärksamhet, är därför viktigt för att förbättra användarupplevelsen. Att hitta ett effektiv sätt att representera röster, så att likheter mellan dessa kan jämföras, är därför av stor nytta. Inom fältet språkteknologi i maskininlärning har stora framstegs gjorts genom att skapa representationer av ord från de inre lagren av neurala nätverk, så kallade neurala nätverksinbäddningar. Dessa representationer har visat sig innehålla semantiken av orden. Denna uppsats avser att undersöka huruvida liknande representationer kan hittas för ljuddata i form av berättarröster från ljudböcker, där likhet mellan röster fångas upp. För att undersöka detta utvecklades och utvärderades två faltningsnätverk som använde sig av spektrogramrepresentationer av röstdata. Den ena modellen är konstruerad som en vanlig klassificeringsmodell, tränad för att skilja mellan uppläsare i datasetet. Den andra modellen använder parvisa förhållanden mellan datapunkterna och en Kullback–Leibler divergensbaserad optimeringsfunktion, med syfte att minimera och maximera skillnaden mellan lika och olika par av datapunkter. Från dessa modeller används representationer från de olika lagren av nätverket för att representera varje datapunkt under utvärderingen. Både en objektiv och subjektiv utvärderingsmetod används. Under den objektiva utvärderingen undersöks först om de funna representationerna är distinkta för olika uppläsare, sedan undersöks även om dessa fångar upp information om uppläsarens kön. Den vanliga klassificeringsmodellen utvärderas också genom ett användartest, eftersom den modellen nådde en storleksordning bättre resultat under den objektiva utvärderingen. Syftet med användartestet var att undersöka om de funna representationerna innehåller information om den upplevda likheten mellan rösterna. Slutsatsen är att det föreslagna tillvägagångssättet har potential till att användas för att representera röster så att information om likhet fångas upp, men att det krävs mer omfattande testning, undersökning och utvärdering. För framtida studier föreslås mer sofistikerad förbehandling av data samt att samla in och använda sig av data kring förhållandet mellan röster under träningen av modellerna.
Bopaiah, Jeevith. "A recurrent neural network architecture for biomedical event trigger classification". UKnowledge, 2018. https://uknowledge.uky.edu/cs_etds/73.
Testo completoPALUMBO, ENRICO. "Knowledge Graph Embeddings for Recommender Systems". Doctoral thesis, Politecnico di Torino, 2020. http://hdl.handle.net/11583/2850588.
Testo completoPettersson, Fredrik. "Optimizing Deep Neural Networks for Classification of Short Texts". Thesis, Luleå tekniska universitet, Datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-76811.
Testo completoRevanur, Vandan, e Ayodeji Ayibiowu. "Automatic Generation of Descriptive Features for Predicting Vehicle Faults". Thesis, Högskolan i Halmstad, CAISR Centrum för tillämpade intelligenta system (IS-lab), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-42885.
Testo completoMurugan, Srikala. "Determining Event Outcomes from Social Media". Thesis, University of North Texas, 2020. https://digital.library.unt.edu/ark:/67531/metadc1703427/.
Testo completoDe, Vine Lance. "Analogical frames by constraint satisfaction". Thesis, Queensland University of Technology, 2020. https://eprints.qut.edu.au/198036/1/Lance_De%20Vine_Thesis.pdf.
Testo completoHorn, Franziska Verfasser], Klaus-Robert [Akademischer Betreuer] [Gutachter] [Müller, Alan [Gutachter] Akbik e Ziawasch [Gutachter] Abedjan. "Similarity encoder: A neural network architecture for learning similarity preserving embeddings / Franziska Horn ; Gutachter: Klaus-Robert Müller, Alan Akbik, Ziawasch Abedjan ; Betreuer: Klaus-Robert Müller". Berlin : Technische Universität Berlin, 2020. http://d-nb.info/1210998386/34.
Testo completoHorn, Franziska [Verfasser], Klaus-Robert [Akademischer Betreuer] [Gutachter] Müller, Alan [Gutachter] Akbik e Ziawasch [Gutachter] Abedjan. "Similarity encoder: A neural network architecture for learning similarity preserving embeddings / Franziska Horn ; Gutachter: Klaus-Robert Müller, Alan Akbik, Ziawasch Abedjan ; Betreuer: Klaus-Robert Müller". Berlin : Technische Universität Berlin, 2020. http://d-nb.info/1210998386/34.
Testo completoŠůstek, 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.
Testo completoLibri sul tema "Neural Network Embeddings"
Unger, Herwig, e Wolfgang A. Halang, a cura di. Autonomous Systems 2016. VDI Verlag, 2016. http://dx.doi.org/10.51202/9783186848109.
Testo completoCapitoli di libri sul tema "Neural Network Embeddings"
Zhang, Yuan, Jian Cao, Jue Chen, Wenyu Sun e Yuan Wang. "Razor SNN: Efficient Spiking Neural Network with Temporal Embeddings". In Artificial Neural Networks and Machine Learning – ICANN 2023, 411–22. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44192-9_33.
Testo completoMarkov, Ilia, Helena Gómez-Adorno, Juan-Pablo Posadas-Durán, Grigori Sidorov e Alexander Gelbukh. "Author Profiling with Doc2vec Neural Network-Based Document Embeddings". In Advances in Soft Computing, 117–31. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-62428-0_9.
Testo completoBajaj, Ahsaas, Shubham Krishna, Hemant Tiwari e Vanraj Vala. "Learning Mobile App Embeddings Using Multi-task Neural Network". In Natural Language Processing and Information Systems, 29–40. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-23281-8_3.
Testo completoRöchert, Daniel, German Neubaum e Stefan Stieglitz. "Identifying Political Sentiments on YouTube: A Systematic Comparison Regarding the Accuracy of Recurrent Neural Network and Machine Learning Models". In Disinformation in Open Online Media, 107–21. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61841-4_8.
Testo completoPicone, Rico A. R., Dane Webb, Finbarr Obierefu e Jotham Lentz. "New Methods for Metastimuli: Architecture, Embeddings, and Neural Network Optimization". In Augmented Cognition, 288–304. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-78114-9_21.
Testo completoCalderaro, Salvatore, Giosué Lo Bosco, Filippo Vella e Riccardo Rizzo. "Breast Cancer Histologic Grade Identification by Graph Neural Network Embeddings". In Bioinformatics and Biomedical Engineering, 283–96. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-34960-7_20.
Testo completoBiswas, Arijit, Mukul Bhutani e Subhajit Sanyal. "MRNet-Product2Vec: A Multi-task Recurrent Neural Network for Product Embeddings". In Machine Learning and Knowledge Discovery in Databases, 153–65. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-71273-4_13.
Testo completoSalsal, Sura Khalid, e Wafaa ALhamed. "Document Retrieval in Text Archives Using Neural Network-Based Embeddings Compared to TFIDF". In Intelligent Systems and Networks, 526–37. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2094-2_63.
Testo completoMolokwu, Bonaventure C., Shaon Bhatta Shuvo, Narayan C. Kar e Ziad Kobti. "Node Classification in Complex Social Graphs via Knowledge-Graph Embeddings and Convolutional Neural Network". In Lecture Notes in Computer Science, 183–98. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-50433-5_15.
Testo completoBarbaglia, Luca, Sergio Consoli e 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.
Testo completoAtti di convegni sul tema "Neural Network Embeddings"
Luo, Dixin, Haoran Cheng, Qingbin Li e Hongteng Xu. "Coupled Point Process-based Sequence Modeling for Privacy-preserving Network Alignment". In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/678.
Testo completoDong, Yuxiao, Ziniu Hu, Kuansan Wang, Yizhou Sun e 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.
Testo completoLiu, Bing, Wei Luo, Gang Li, Jing Huang e Bo Yang. "Do We Need an Encoder-Decoder to Model Dynamical Systems on Networks?" In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/242.
Testo completoAspis, Yaniv, Krysia Broda, Jorge Lobo e Alessandra Russo. "Embed2Sym - Scalable Neuro-Symbolic Reasoning via Clustered Embeddings". In 19th International Conference on Principles of Knowledge Representation and Reasoning {KR-2022}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/kr.2022/44.
Testo completoGarcia-Romero, Daniel, David Snyder, Gregory Sell, Daniel Povey e Alan McCree. "Speaker diarization using deep neural network embeddings". In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2017. http://dx.doi.org/10.1109/icassp.2017.7953094.
Testo completoHamaguchi, Takuo, Hidekazu Oiwa, Masashi Shimbo e Yuji Matsumoto. "Knowledge Transfer for Out-of-Knowledge-Base Entities : A Graph Neural Network Approach". 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.2017/250.
Testo completoCheng, Weiyu, Yanyan Shen, Yanmin Zhu e Linpeng Huang. "DELF: A Dual-Embedding based Deep Latent Factor Model for Recommendation". 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.2018/462.
Testo completoRomero, Hector E., Ning Ma e Guy J. Brown. "Snorer Diarisation Based On Deep Neural Network Embeddings". In ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020. http://dx.doi.org/10.1109/icassp40776.2020.9053683.
Testo completoSnyder, David, Daniel Garcia-Romero, Daniel Povey e Sanjeev Khudanpur. "Deep Neural Network Embeddings for Text-Independent Speaker Verification". In Interspeech 2017. ISCA: ISCA, 2017. http://dx.doi.org/10.21437/interspeech.2017-620.
Testo completoSettle, Shane, e Karen Livescu. "Discriminative acoustic word embeddings: Tecurrent neural network-based approaches". In 2016 IEEE Spoken Language Technology Workshop (SLT). IEEE, 2016. http://dx.doi.org/10.1109/slt.2016.7846310.
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