Gotowa bibliografia na temat „Graph Attention Networks”
Utwórz poprawne odniesienie w stylach APA, MLA, Chicago, Harvard i wielu innych
Zobacz listy aktualnych artykułów, książek, rozpraw, streszczeń i innych źródeł naukowych na temat „Graph Attention Networks”.
Przycisk „Dodaj do bibliografii” jest dostępny obok każdej pracy w bibliografii. Użyj go – a my automatycznie utworzymy odniesienie bibliograficzne do wybranej pracy w stylu cytowania, którego potrzebujesz: APA, MLA, Harvard, Chicago, Vancouver itp.
Możesz również pobrać pełny tekst publikacji naukowej w formacie „.pdf” i przeczytać adnotację do pracy online, jeśli odpowiednie parametry są dostępne w metadanych.
Artykuły w czasopismach na temat "Graph Attention Networks"
Wu, Nan, i Chaofan Wang. "Ensemble Graph Attention Networks". Transactions on Machine Learning and Artificial Intelligence 10, nr 3 (12.06.2022): 29–41. http://dx.doi.org/10.14738/tmlai.103.12399.
Pełny tekst źródłaMurzin, M. V., I. A. Kulikov i N. A. Zhukova. "Methods for Constructing Graph Neural Networks". LETI Transactions on Electrical Engineering & Computer Science 17, nr 10 (2024): 40–48. https://doi.org/10.32603/2071-8985-2024-17-10-40-48.
Pełny tekst źródłaSheng, Jinfang, Yufeng Zhang, Bin Wang i Yaoxing Chang. "MGATs: Motif-Based Graph Attention Networks". Mathematics 12, nr 2 (16.01.2024): 293. http://dx.doi.org/10.3390/math12020293.
Pełny tekst źródłaChatzianastasis, Michail, Johannes Lutzeyer, George Dasoulas i Michalis Vazirgiannis. "Graph Ordering Attention Networks". Proceedings of the AAAI Conference on Artificial Intelligence 37, nr 6 (26.06.2023): 7006–14. http://dx.doi.org/10.1609/aaai.v37i6.25856.
Pełny tekst źródłaLi, Yu, Yuan Tian, Jiawei Zhang i Yi Chang. "Learning Signed Network Embedding via Graph Attention". Proceedings of the AAAI Conference on Artificial Intelligence 34, nr 04 (3.04.2020): 4772–79. http://dx.doi.org/10.1609/aaai.v34i04.5911.
Pełny tekst źródłaWang, Bin, Yu Chen, Jinfang Sheng i Zhengkun He. "Attributed Graph Embedding Based on Attention with Cluster". Mathematics 10, nr 23 (1.12.2022): 4563. http://dx.doi.org/10.3390/math10234563.
Pełny tekst źródłaLi, Zitong, Xiang Cheng, Lixiao Sun, Ji Zhang i Bing Chen. "A Hierarchical Approach for Advanced Persistent Threat Detection with Attention-Based Graph Neural Networks". Security and Communication Networks 2021 (4.05.2021): 1–14. http://dx.doi.org/10.1155/2021/9961342.
Pełny tekst źródłaChen, Lu, Boer Lv, Chi Wang, Su Zhu, Bowen Tan i Kai Yu. "Schema-Guided Multi-Domain Dialogue State Tracking with Graph Attention Neural Networks". Proceedings of the AAAI Conference on Artificial Intelligence 34, nr 05 (3.04.2020): 7521–28. http://dx.doi.org/10.1609/aaai.v34i05.6250.
Pełny tekst źródłaLiu, Jie, Lingyun Song, Li Gao i Xuequn Shang. "MMAN: Metapath Based Multi-Level Graph Attention Networks for Heterogeneous Network Embedding (Student Abstract)". Proceedings of the AAAI Conference on Artificial Intelligence 36, nr 11 (28.06.2022): 13005–6. http://dx.doi.org/10.1609/aaai.v36i11.21639.
Pełny tekst źródłaWang, Rui, Bicheng Li, Shengwei Hu, Wenqian Du i Min Zhang. "Knowledge Graph Embedding via Graph Attenuated Attention Networks". IEEE Access 8 (2020): 5212–24. http://dx.doi.org/10.1109/access.2019.2963367.
Pełny tekst źródłaRozprawy doktorskie na temat "Graph Attention Networks"
Guo, Dalu. "Attention Networks in Visual Question Answering and Visual Dialog". Thesis, The University of Sydney, 2021. https://hdl.handle.net/2123/25079.
Pełny tekst źródłaDronzeková, Michaela. "Analýza polygonálních modelů pomocí neuronových sítí". Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2020. http://www.nusl.cz/ntk/nusl-417253.
Pełny tekst źródłaLee, John Boaz T. "Deep Learning on Graph-structured Data". Digital WPI, 2019. https://digitalcommons.wpi.edu/etd-dissertations/570.
Pełny tekst źródłaYou, Di. "Attributed Multi-Relational Attention Network for Fact-checking URL Recommendation". Digital WPI, 2019. https://digitalcommons.wpi.edu/etd-theses/1321.
Pełny tekst źródłaMazzieri, Diego. "Machine Learning for combinatorial optimization: the case of Vehicle Routing". Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/24688/.
Pełny tekst źródłaGullstrand, Mattias, i Stefan Maraš. "Using Graph Neural Networks for Track Classification and Time Determination of Primary Vertices in the ATLAS Experiment". Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-288505.
Pełny tekst źródłaFrån och med 2027 kommer \textit{high-luminosity Large Hadron Collider} (HL-LHC) att tas i drift och möjliggöra mätningar med högre precision och utforskningar av nya fysikprocesser mellan elementarpartiklar. Ett centralt problem som uppstår i ATLAS-detektorn vid rekonstruktionen av partikelkollisioner är att separera sällsynta och intressanta interaktioner, så kallade \textit{hard-scatters} (HS) från ointressanta \textit{pileup}-interaktioner (PU) i den kompakta rumsliga dimensionen. Svårighetsgraden för detta problem ökar vid högre luminositeter. Med hjälp av den kommande \textit{High-Granularity Timing-detektorns} (HGTD) mätningar kommer även tidsinformation relaterat till interaktionerna att erhållas. I detta projekt används denna information för att beräkna tiden för enskillda interaktioner vilket därmed kan användas för att separera HS-interaktioner från PU-interaktioner. Den nuvarande metoden använder en trädregressionsmetod, s.k. boosted decision tree (BDT) tillsammans med tidsinformationen från HGTD för att bestämma en tid. Vi föreslår ett nytt tillvägagångssätt baserat på ett s.k. uppvaktande grafnätverk (GAT), där varje protonkollision representeras som en graf över partikelspåren och där GAT-egenskaperna tillämpas på spårnivå. Våra resultat visar att vi kan replikera de BDT-baserade resultaten och till och med förbättra resultaten på bekostnad av att öka osäkerheten i tidsbestämningarna. Vi drar slutsatsen att även om det finns potential för GAT-modeller att överträffa BDT-modeller, bör mer komplexa versioner av de förra tillämpas. Vi ger slutligen några förbättringsförslag som vi hoppas ska kunna inspirera till ytterligare studier och framsteg inom detta område, vilket visar lovande potential.
Breckel, Thomas P. K. [Verfasser], Christiane [Akademischer Betreuer] Thiel i Stefan [Akademischer Betreuer] Debener. "Insights into brain networks from functional MRI and graph analysis during and following attentional demand / Thomas P. K. Breckel. Betreuer: Christiane Thiel ; Stefan Debener". Oldenburg : BIS der Universität Oldenburg, 2013. http://d-nb.info/1050299434/34.
Pełny tekst źródłaBreckel, Thomas [Verfasser], Christiane Akademischer Betreuer] Thiel i Stefan [Akademischer Betreuer] [Debener. "Insights into brain networks from functional MRI and graph analysis during and following attentional demand / Thomas P. K. Breckel. Betreuer: Christiane Thiel ; Stefan Debener". Oldenburg : BIS der Universität Oldenburg, 2013. http://nbn-resolving.de/urn:nbn:de:gbv:715-oops-15262.
Pełny tekst źródłaAmor, Yasmine. "Ιntelligent apprοach fοr trafic cοngestiοn predictiοn". Electronic Thesis or Diss., Normandie, 2024. http://www.theses.fr/2024NORMR129.
Pełny tekst źródłaTraffic congestion presents a critical challenge to urban areas, as the volume of vehicles continues to grow faster than the system’s overall capacity. This growth impacts economic activity, environmental sustainability, and overall quality of life. Although strategies for mitigating traffic congestion have seen improvements over the past few decades, many cities still struggle to manage it effectively. While various models have been developed to tackle this issue, existing approaches often fall short in providing real-time, localized predictions that can adapt to complex and dynamic traffic conditions. Most rely on fixed prediction horizons and lack the intelligent infrastructure needed for flexibility. This thesis addresses these gaps by proposing an intelligent, decentralized, infrastructure-based approach for traffic congestion estimation and prediction.We start by studying Traffic Estimation. We examine the possible congestion measures and data sources required for different contexts that may be studied. We establish a three-dimensional relationship between these axes. A rule-based system is developed to assist researchers and traffic operators in recommending the most appropriate congestion measures based on the specific context under study. We then proceed to Traffic Prediction, introducing our DECentralized COngestion esTimation and pRediction model using Intelligent Variable Message Signs (DECOTRIVMS). This infrastructure-based model employs intelligent Variable Message Signs (VMSs) to collect real-time traffic data and provide short-term congestion predictions with variable prediction horizons.We use Graph Attention Networks (GATs) due to their ability to capture complex relationships and handle graph-structured data. They are well-suited for modeling interactions between different road segments. In addition to GATs, we employ online learning methods, specifically, Stochastic Gradient Descent (SGD) and ADAptive GRAdient Descent (ADAGRAD). While these methods have been successfully used in various other domains, their application in traffic congestion prediction remains under-explored. In our thesis, we aim to bridge that gap by exploring their effectiveness within the context of real-time traffic congestion forecasting.Finally, we validate our model’s effectiveness through two case studies conducted in Muscat, Oman, and Rouen, France. A comprehensive comparative analysis is performed, evaluating various prediction techniques, including GATs, Graph Convolutional Networks (GCNs), SGD and ADAGRAD. The achieved results underscore the potential of DECOTRIVMS, demonstrating its potential for accurate and effective traffic congestion prediction across diverse urban contexts
Blini, Elvio A. "Biases in Visuo-Spatial Attention: from Assessment to Experimental Induction". Doctoral thesis, Università degli studi di Padova, 2016. http://hdl.handle.net/11577/3424480.
Pełny tekst źródłaIn questo lavoro presenterò una serie di ricerche che possono sembrare piuttosto eterogenee per quesiti sperimentali e approcci metodologici, ma sono tuttavia legate da un filo conduttore comune: i costrutti di ragionamento e attenzione spaziale. Affronterò in particolare aspetti legati alla valutazione delle asimmetrie attenzionali, nell'individuo sano come nel paziente con disturbi neurologici, il loro ruolo in vari aspetti della cognizione umana, e i loro substrati neurali, guidato dalla convinzione che l’attenzione spaziale giochi un ruolo importante in svariati processi mentali non necessariamente limitati alla percezione. Quanto segue è stato dunque organizzato in due sezioni distinte. Nella prima mi soffermerò sulla valutazione delle asimmetrie visuospaziali, iniziando dalla descrizione di un nuovo paradigma particolarmente adatto a questo scopo. Nel primo capitolo descriverò gli effetti del doppio compito e del carico attenzionale su un test di monitoraggio spaziale; il risultato principale mostra un netto peggioramento nella prestazione al compito di detezione spaziale in funzione del carico di memoria introdotto. Nel secondo capitolo applicherò lo stesso paradigma ad una popolazione clinica contraddistinta da lesione cerebrale dell’emisfero sinistro. Nonostante una valutazione neuropsicologica standard non evidenziasse alcun deficit lateralizzato dell’attenzione, mostrerò che sfruttare un compito accessorio può portare ad una spiccata maggiore sensibilità dei test diagnostici, con evidenti ricadute benefiche sull'iter clinico e terapeutico dei pazienti. Infine, nel terzo capitolo suggerirò, tramite dati preliminari, che asimmetrie attenzionali possono essere individuate, nell'individuo sano, anche lungo l’asse sagittale; argomenterò, in particolare, che attorno allo spazio peripersonale sembrano essere generalmente concentrate più risorse attentive, e che i benefici conseguenti si estendono a compiti di varia natura (ad esempio compiti di discriminazione). Passerò dunque alla seconda sezione, in cui, seguendo una logica inversa, indurrò degli spostamenti nel focus attentivo in modo da valutarne il ruolo in compiti di varia natura. Nei capitoli quarto e quinto sfrutterò delle stimolazioni sensoriali: la stimolazione visiva optocinetica e la stimolazione galvanico vestibolare, rispettivamente. Nel quarto capitolo mostrerò che l’attenzione spaziale è coinvolta nella cognizione numerica, con cui intrattiene rapporti bidirezionali. Nello specifico mostrerò da un lato che la stimolazione optocinetica può modulare l’occorrenza di errori procedurali nel calcolo mentale, dall'altro che il calcolo stesso ha degli effetti sull'attenzione spaziale e in particolare sul comportamento oculomotorio. Nel quinto capitolo esaminerò gli effetti della stimolazione galvanica vestibolare, una tecnica particolarmente promettente per la riabilitazione dei disturbi attentivi lateralizzati, sulle rappresentazioni mentali dello spazio. Discuterò in modo critico un recente modello della negligenza spaziale unilaterale, suggerendo che stimolazioni e disturbi vestibolari possano sì avere ripercussioni sulle rappresentazioni metriche dello spazio, ma senza comportare necessariamente inattenzione per lo spazio stesso. Infine, nel sesto capitolo descriverò gli effetti di cattura dell’attenzione visuospaziale che stimoli distrattori intrinsecamente motivanti possono esercitare nell'adulto sano. Cercherò, in particolare, di predire l’entità di questa cattura attenzionale partendo da immagini di risonanza magnetica funzionale a riposo: riporterò dati preliminari focalizzati sull'importanza del circuito cingolo-opercolare, effettuando un parallelismo con popolazioni cliniche caratterizzate da comportamenti di dipendenza.
Książki na temat "Graph Attention Networks"
Dorogovtsev, Sergey N., i José F. F. Mendes. The Nature of Complex Networks. Oxford University PressOxford, 2022. http://dx.doi.org/10.1093/oso/9780199695119.001.0001.
Pełny tekst źródłaBianconi, Ginestra. Synchronization, Non-linear Dynamics and Control. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198753919.003.0015.
Pełny tekst źródłaCzęści książek na temat "Graph Attention Networks"
Liu, Zhiyuan, i Jie Zhou. "Graph Attention Networks". W Introduction to Graph Neural Networks, 39–41. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-031-01587-8_7.
Pełny tekst źródłaHuang, Junjie, Huawei Shen, Liang Hou i Xueqi Cheng. "Signed Graph Attention Networks". W Artificial Neural Networks and Machine Learning – ICANN 2019: Workshop and Special Sessions, 566–77. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30493-5_53.
Pełny tekst źródłaAbdel-Basset, Mohamed, Nour Moustafa, Hossam Hawash i Zahir Tari. "Graph Attention Networks: A Journey from Start to End". W Responsible Graph Neural Networks, 131–58. Boca Raton: Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003329701-6.
Pełny tekst źródłaFrey, Christian M. M., Yunpu Ma i Matthias Schubert. "SEA: Graph Shell Attention in Graph Neural Networks". W Machine Learning and Knowledge Discovery in Databases, 326–43. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-26390-3_20.
Pełny tekst źródłaMa, Liheng, Reihaneh Rabbany i Adriana Romero-Soriano. "Graph Attention Networks with Positional Embeddings". W Advances in Knowledge Discovery and Data Mining, 514–27. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-75762-5_41.
Pełny tekst źródłaTao, Ye, Ying Li i Zhonghai Wu. "Revisiting Attention-Based Graph Neural Networks for Graph Classification". W Lecture Notes in Computer Science, 442–58. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-14714-2_31.
Pełny tekst źródłaWang, Yubin, Zhenyu Zhang, Tingwen Liu i Li Guo. "SLGAT: Soft Labels Guided Graph Attention Networks". W Advances in Knowledge Discovery and Data Mining, 512–23. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47426-3_40.
Pełny tekst źródłaJyoti, Divya, Akhil Sharma, Shaswat Gupta, Prashant Manhar, Jyoti Srivastava i Dharamendra Prasad Mahato. "Abstractive Summarization Using Gated Graph Attention Networks". W Lecture Notes on Data Engineering and Communications Technologies, 95–106. Cham: Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-87766-7_9.
Pełny tekst źródłaZhao, Ming, Weijia Jia i Yusheng Huang. "Attention-Based Aggregation Graph Networks for Knowledge Graph Information Transfer". W Advances in Knowledge Discovery and Data Mining, 542–54. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47436-2_41.
Pełny tekst źródłaWang, Jingqi, Cui Zhu i Wenjun Zhu. "Dynamic Embedding Graph Attention Networks for Temporal Knowledge Graph Completion". W Knowledge Science, Engineering and Management, 722–34. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-10983-6_55.
Pełny tekst źródłaStreszczenia konferencji na temat "Graph Attention Networks"
Kato, Jun, Airi Mita, Keita Gobara i Akihiro Inokuchi. "Deep Graph Attention Networks". W 2024 Twelfth International Symposium on Computing and Networking Workshops (CANDARW), 170–74. IEEE, 2024. https://doi.org/10.1109/candarw64572.2024.00034.
Pełny tekst źródłaIshtiaq, Mariam, Jong-Un Won i Sangchan Park. "GATreg - Graph Attention Networks with Regularization". W 2025 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), 1022–26. IEEE, 2025. https://doi.org/10.1109/icaiic64266.2025.10920663.
Pełny tekst źródłaBuschmann, Fernando Vera, Zhihui Du i David Bader. "Enhanced Knowledge Graph Attention Networks for Efficient Graph Learning". W 2024 IEEE High Performance Extreme Computing Conference (HPEC), 1–7. IEEE, 2024. https://doi.org/10.1109/hpec62836.2024.10938526.
Pełny tekst źródłaGao, Yiwei, i Qing Gao. "Graph Structure Adversarial Attack Design Based on Graph Attention Networks". W 2024 43rd Chinese Control Conference (CCC), 9028–33. IEEE, 2024. http://dx.doi.org/10.23919/ccc63176.2024.10661862.
Pełny tekst źródłaLi, Min, Xuejun Li i Jing Liao. "Graph Attention Networks Fusing Semantic Information for Knowledge Graph Completion". W 2024 17th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), 1–6. IEEE, 2024. https://doi.org/10.1109/cisp-bmei64163.2024.10906235.
Pełny tekst źródłaVu, Tuan Hoang, Anh Nguyen i Minh Tuan Nguyen. "Graph Attention Networks Based Cardiac Arrest Diagnosis". W 2024 International Conference on Advanced Technologies for Communications (ATC), 762–67. IEEE, 2024. https://doi.org/10.1109/atc63255.2024.10908172.
Pełny tekst źródłaRustamov, Zahiriddin, Ayham Zaitouny, Rafat Damseh i Nazar Zaki. "GAIS: A Novel Approach to Instance Selection with Graph Attention Networks". W 2024 IEEE International Conference on Knowledge Graph (ICKG), 309–16. IEEE, 2024. https://doi.org/10.1109/ickg63256.2024.00046.
Pełny tekst źródłaWang, Shuowei, Sifan Ding i William Zhu. "Deep Graph Matching with Improved Graph-Context Networks based by Attention". W 2024 International Conference on Artificial Intelligence and Power Systems (AIPS), 156–60. IEEE, 2024. http://dx.doi.org/10.1109/aips64124.2024.00041.
Pełny tekst źródłaBrynte, Lucas, José Pedro Iglesias, Carl Olsson i Fredrik Kahl. "Learning Structure-From-Motion with Graph Attention Networks". W 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 4808–17. IEEE, 2024. http://dx.doi.org/10.1109/cvpr52733.2024.00460.
Pełny tekst źródłaZhang, Shuya, Tao Yang i Kongfa Hu. "Multi-Label Syndrome Classification with Graph Attention Networks". W 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 4802–6. IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10822526.
Pełny tekst źródłaRaporty organizacyjne na temat "Graph Attention Networks"
Fait, Aaron, Grant Cramer i Avichai Perl. Towards improved grape nutrition and defense: The regulation of stilbene metabolism under drought. United States Department of Agriculture, maj 2014. http://dx.doi.org/10.32747/2014.7594398.bard.
Pełny tekst źródła