Academic literature on the topic 'Neural Network Embeddings'
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Journal articles on the topic "Neural Network Embeddings":
Che, Feihu, Dawei Zhang, Jianhua Tao, Mingyue Niu, and Bocheng Zhao. "ParamE: Regarding Neural Network Parameters as Relation Embeddings for Knowledge Graph Completion." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 03 (April 3, 2020): 2774–81. http://dx.doi.org/10.1609/aaai.v34i03.5665.
Huang, Junjie, Huawei Shen, Liang Hou, and Xueqi Cheng. "SDGNN: Learning Node Representation for Signed Directed Networks." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 1 (May 18, 2021): 196–203. http://dx.doi.org/10.1609/aaai.v35i1.16093.
Srinidhi, K., T. L.S Tejaswi, CH Rama Rupesh Kumar, and I. Sai Siva Charan. "An Advanced Sentiment Embeddings with Applications to Sentiment Based Result Analysis." International Journal of Engineering & Technology 7, no. 2.32 (May 31, 2018): 393. http://dx.doi.org/10.14419/ijet.v7i2.32.15721.
Armandpour, Mohammadreza, Patrick Ding, Jianhua Huang, and Xia Hu. "Robust Negative Sampling for Network Embedding." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 3191–98. http://dx.doi.org/10.1609/aaai.v33i01.33013191.
Kamath, S., K. G. Karibasappa, Anvitha Reddy, Arati M. Kallur, B. B. Priyanka, and B. P. Bhagya. "Improving the Relation Classification Using Convolutional Neural Network." IOP Conference Series: Materials Science and Engineering 1187, no. 1 (September 1, 2021): 012004. http://dx.doi.org/10.1088/1757-899x/1187/1/012004.
Gu, Haishuo, Jinguang Sui, and Peng Chen. "Graph Representation Learning for Street-Level Crime Prediction." ISPRS International Journal of Geo-Information 13, no. 7 (July 1, 2024): 229. http://dx.doi.org/10.3390/ijgi13070229.
Zhang, Lei, Feng Qian, Jie Chen, and Shu Zhao. "An Unsupervised Rapid Network Alignment Framework via Network Coarsening." Mathematics 11, no. 3 (January 21, 2023): 573. http://dx.doi.org/10.3390/math11030573.
Truică, Ciprian-Octavian, Elena-Simona Apostol, Maria-Luiza Șerban, and Adrian Paschke. "Topic-Based Document-Level Sentiment Analysis Using Contextual Cues." Mathematics 9, no. 21 (October 27, 2021): 2722. http://dx.doi.org/10.3390/math9212722.
Jang, Youngjin, and Harksoo Kim. "Reliable Classification of FAQs with Spelling Errors Using an Encoder-Decoder Neural Network in Korean." Applied Sciences 9, no. 22 (November 7, 2019): 4758. http://dx.doi.org/10.3390/app9224758.
Guo, Lei, Haoran Jiang, Xiyu Liu, and Changming Xing. "Network Embedding-Aware Point-of-Interest Recommendation in Location-Based Social Networks." Complexity 2019 (November 4, 2019): 1–18. http://dx.doi.org/10.1155/2019/3574194.
Dissertations / Theses on the topic "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.
I 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.
PALUMBO, ENRICO. "Knowledge Graph Embeddings for Recommender Systems." Doctoral thesis, Politecnico di Torino, 2020. http://hdl.handle.net/11583/2850588.
Pettersson, 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.
Revanur, Vandan, and 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.
Murugan, Srikala. "Determining Event Outcomes from Social Media." Thesis, University of North Texas, 2020. https://digital.library.unt.edu/ark:/67531/metadc1703427/.
De, 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.
Horn, Franziska Verfasser], Klaus-Robert [Akademischer Betreuer] [Gutachter] [Müller, Alan [Gutachter] Akbik, and 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.
Horn, Franziska [Verfasser], Klaus-Robert [Akademischer Betreuer] [Gutachter] Müller, Alan [Gutachter] Akbik, and 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.
Šůstek, 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.
Books on the topic "Neural Network Embeddings":
Unger, Herwig, and Wolfgang A. Halang, eds. Autonomous Systems 2016. VDI Verlag, 2016. http://dx.doi.org/10.51202/9783186848109.
Book chapters on the topic "Neural Network Embeddings":
Zhang, Yuan, Jian Cao, Jue Chen, Wenyu Sun, and 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.
Markov, Ilia, Helena Gómez-Adorno, Juan-Pablo Posadas-Durán, Grigori Sidorov, and 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.
Bajaj, Ahsaas, Shubham Krishna, Hemant Tiwari, and 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.
Röchert, Daniel, German Neubaum, and 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.
Picone, Rico A. R., Dane Webb, Finbarr Obierefu, and 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.
Calderaro, Salvatore, Giosué Lo Bosco, Filippo Vella, and 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.
Biswas, Arijit, Mukul Bhutani, and 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.
Salsal, Sura Khalid, and 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.
Molokwu, Bonaventure C., Shaon Bhatta Shuvo, Narayan C. Kar, and 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.
Barbaglia, 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.
Conference papers on the topic "Neural Network Embeddings":
Luo, Dixin, Haoran Cheng, Qingbin Li, and 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.
Dong, 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.
Liu, Bing, Wei Luo, Gang Li, Jing Huang, and 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.
Aspis, Yaniv, Krysia Broda, Jorge Lobo, and 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.
Garcia-Romero, Daniel, David Snyder, Gregory Sell, Daniel Povey, and 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.
Hamaguchi, Takuo, Hidekazu Oiwa, Masashi Shimbo, and 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.
Cheng, Weiyu, Yanyan Shen, Yanmin Zhu, and 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.
Romero, Hector E., Ning Ma, and 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.
Snyder, David, Daniel Garcia-Romero, Daniel Povey, and 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.
Settle, Shane, and 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.