المؤلفون: Mujahid, Omer

المساهمون: University/Department: Universitat de Girona. Departament d'Enginyeria Elèctrica, Electrònica i Automàtica

Thesis Advisors: Vehí, Josep, Contreras, Ivan, Institut d'Informàtica i Aplicacions

المصدر: TDX (Tesis Doctorals en Xarxa)

مصطلحات موضوعية: Diabetis tipus 1, Type 1 diabetes, Diabetes tipo 1, Pacients virtuals, Virtual patients, Pacientes virtuales, Simuladors de Diabetis tipus 1, Type 1 diabetes simulators, Simuladores de diabetes tipo 1, síntesi causal de dades de glucosa a la sang, Causal blood glucose data synthesis, Síntesis causal de glucosa en la sangre, Síntesi condicional de dades de glucosa a la sang, Conditional blood glucose data synthesis, Síntesis condicional de dades de glucosa en la sangre, Models generatius profunds, Deep generative models, Modelos generativos profundos, Models basats en dades, Data-driven models, Modelos basados en datos

Time: 616.4, 621.3

وصف الملف: application/pdf

URL الوصول: http://hdl.handle.net/10803/689606

المؤلفون: Cabrera Tejera, Alvis

المساهمون: University/Department: Universitat de Girona. Departament d'Enginyeria Elèctrica, Electrònica i Automàtica

Thesis Advisors: Vehí, Josep, Beneyto Tantiña, Aleix

المصدر: TDX (Tesis Doctorals en Xarxa)

مصطلحات موضوعية: Diabetis tipus 1, Diabetes tipo 1, Type 1 diabetes, Pacients virtuals, Pacientes virtuales, Virtual patients, Simuladors, Simuladores, Simulators, Glucosa a la sang, Glucosa en la sangre, Blood glucose, Dades composicionals, Datos composicionales, Compositional data, Models generatius profunds, Modelos generativos profundos, Deep generative models, Models basats en dades, Modelos basados en datos, Data-driven models, Diabetis mellitus

Time: 616.4, 621.3

وصف الملف: application/pdf

URL الوصول: http://hdl.handle.net/10803/689607

المؤلفون: Ramires, António

المساهمون: University/Department: Universitat Pompeu Fabra. Departament de Tecnologies de la Informació i les Comunicacions

Thesis Advisors: Serra, Xavier, Font Corbera, Frederic

المصدر: TDX (Tesis Doctorals en Xarxa)

مصطلحات موضوعية: Electronic music production, Instrument classification, Percussive sound generation, Music information retrieval, Deep learning, Deep generative models, Producción de música electrónica, Clasificación de instrumentos, Generación de sonidos percusivos, Recuperación de la información musical, Aprendizaje profundo, Modelos generativos profundos

وصف الملف: application/pdf

URL الوصول: http://hdl.handle.net/10803/687697

المؤلفون: Deveshwar, Nikhil, Rajagopal, Abhejit, Sahin, Sule, Shimron, Efrat, Larson, Peder EZ

المصدر: Bioengineering. 10(3)

مصطلحات موضوعية: Engineering, Biomedical Engineering, Bioengineering, Biomedical Imaging, 4.1 Discovery and preclinical testing of markers and technologies, Detection, screening and diagnosis, synthetic phase, synthetic multi-coil data, deep generative models, GANs, generative adversarial network, synthetic data, MRI reconstruction, deep learning, unrolled networks, Biomedical engineering

وصف الملف: application/pdf

URL الوصول: https://escholarship.org/uc/item/5p96987g
https://escholarship.org/content/qt5p96987g/qt5p96987g.pdf

المؤلفون: Jingbin Xu, Degang Xu, Kun Wan, Ying Zhang

المصدر: Water Science and Technology, Vol 88, Iss 11, Pp 2762-2778 (2023)

مصطلحات موضوعية: data compensation, deep generative models, imbalanced samples, vae–wgan–gp model, water quality assessment, Environmental technology. Sanitary engineering, TD1-1066

وصف الملف: electronic resource

Relation: http://wst.iwaponline.com/content/88/11/2762; https://doaj.org/toc/0273-1223; https://doaj.org/toc/1996-9732

URL الوصول: https://doaj.org/article/fa1d852eff50414cb1f3f808528cede3

المؤلفون: Ziying Yang, Jie Zhang, Wei Wang, Huan Li

المصدر: Applied Sciences, Vol 14, Iss 19, p 8742 (2024)

مصطلحات موضوعية: backdoor attack, deep generative models, data poisoning, invisible trigger, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999

وصف الملف: electronic resource

Relation: https://www.mdpi.com/2076-3417/14/19/8742; https://doaj.org/toc/2076-3417

URL الوصول: https://doaj.org/article/6540fff00f54480cbbfa3da04ada6bc6

المؤلفون: Bekri, Naoufal El, Drumetz, Lucas, Vermet, Franck

المساهمون: Laboratoire de Mathématiques de Bretagne Atlantique (LMBA), Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS), Département Mathematical and Electrical Engineering (IMT Atlantique - MEE), IMT Atlantique (IMT Atlantique), Institut Mines-Télécom Paris (IMT)-Institut Mines-Télécom Paris (IMT), Equipe Observations Signal & Environnement (Lab-STICC_OSE), Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom Paris (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom Paris (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom Paris (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom Paris (IMT), Océan Dynamique Observations Analyse (ODYSSEY), Université de Rennes (UR)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Université de Brest (UBO)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-IMT Atlantique (IMT Atlantique), ANR-21-CE48-0005,LEMONADE,Apprentissage et modélisation de la dynamique spectrale de séries temporelles d'images satellitaires(2021), ANR-11-LABX-0020,LEBESGUE,Centre de Mathématiques Henri Lebesgue : fondements, interactions, applications et Formation(2011)

المصدر: ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) ; https://imt-atlantique.hal.science/hal-04716987 ; ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Apr 2024, Seoul, France. pp.6395-6399, ⟨10.1109/ICASSP48485.2024.10446131⟩

مصطلحات موضوعية: Stochastic differential equations, deep generative models, normalizing flows, time series, convex neural network, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [MATH.MATH-PR]Mathematics [math]/Probability [math.PR]

جغرافية الموضوع: Seoul, France

الاتاحة: https://imt-atlantique.hal.science/hal-04716987
https://imt-atlantique.hal.science/hal-04716987v1/document
https://imt-atlantique.hal.science/hal-04716987v1/file/Time_Change_Normalizing_Flow_v2.pdf
https://doi.org/10.1109/ICASSP48485.2024.10446131

المؤلفون: van Krieken, Emile

المصدر: van Krieken , E 2024 , ' Optimisation in Neurosymbolic Learning Systems ' , PhD , Vrije Universiteit Amsterdam . https://doi.org/10.5463/thesis.466

مصطلحات موضوعية: Neurosymbolische Kunstmatige Intelligentie, Machinaal Leren, Leren met Achtergrondkennis, Diepe Generatieve Modellen, Fuzzy Logica, Probabilistische Logica, Probabilistisch Programmeren, Optimalisatie, Schatters van Gradiënten, Neurale Netwerken, Neurosymbolic Artificial Intelligence, Machine Learning, Learning with Background Knowledge, Deep Generative Models, Fuzzy Logic, Probabilistic Logic, Probabilistic Programming, Optimization, Gradient Estimation, Neural Networks

وصف الملف: application/pdf

Relation: https://research.vu.nl/en/publications/270d7b7c-be6a-4b07-a8c9-22cca8dd5e96

الاتاحة: https://research.vu.nl/en/publications/270d7b7c-be6a-4b07-a8c9-22cca8dd5e96
https://doi.org/10.5463/thesis.466
https://hdl.handle.net/1871.1/270d7b7c-be6a-4b07-a8c9-22cca8dd5e96
https://research.vu.nl/ws/files/282134811/phd%20thesis%20final%20-%20655f23e0eb769.pdf
https://research.vu.nl/ws/files/282134813/170489%20van%20krieken%20r13%20omsdefdpr%20-%20655f242c28257.pdf
https://research.vu.nl/ws/files/282134815/inhoudsopgave%20-%20655f23e7e96b2.pdf
https://research.vu.nl/ws/files/282134817/main-3-4%20-%206527c18438876.pdf

المؤلفون: Solal, Maëlys, Hassanaly, Ravi, Burgos, Ninon

المساهمون: Algorithms, models and methods for images and signals of the human brain = Algorithmes, modèles et méthodes pour les images et les signaux du cerveau humain ICM Paris (ARAMIS), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière AP-HP, Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière AP-HP, Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019), ANR-10-IAHU-0006,IHU-A-ICM,Institut de Neurosciences Translationnelles de Paris(2010)

المصدر: SPIE Medical Imaging ; https://hal.science/hal-04291561 ; SPIE Medical Imaging, Feb 2024, San Diego (California), United States

مصطلحات موضوعية: Unsupervised anomaly detection, Deep generative models, Alzheimer's disease, Abnormality maps, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [SCCO.NEUR]Cognitive science/Neuroscience, [SDV.IB.MN]Life Sciences [q-bio]/Bioengineering/Nuclear medicine

جغرافية الموضوع: San Diego (California), United States

Relation: info:eu-repo/semantics/altIdentifier/arxiv/2311.12081; hal-04291561; https://hal.science/hal-04291561; https://hal.science/hal-04291561v2/document; https://hal.science/hal-04291561v2/file/SOLAL_SPIE-2024_CN-STD.pdf; ARXIV: 2311.12081

الاتاحة: https://hal.science/hal-04291561
https://hal.science/hal-04291561v2/document
https://hal.science/hal-04291561v2/file/SOLAL_SPIE-2024_CN-STD.pdf

المؤلفون: Iñigo Prada-Luengo, Viktoria Schuster, Yuhu Liang, Thilde Terkelsen, Valentina Sora, Anders Krogh

المصدر: Genome Biology, Vol 24, Iss 1, Pp 1-17 (2023)

مصطلحات موضوعية: Deep generative models, Deep learning, Differential expression analysis, DEG, DEseq2, Transcriptomics, Biology (General), QH301-705.5, Genetics, QH426-470

وصف الملف: electronic resource

Relation: https://doaj.org/toc/1474-760X

URL الوصول: https://doaj.org/article/a01c7581a83340df990a066ff0394ff0

المؤلفون: Bongsung Bae, Haelee Bae, Hojung Nam

المصدر: Journal of Cheminformatics, Vol 15, Iss 1, Pp 1-18 (2023)

مصطلحات موضوعية: De novo drug design, Deep generative models, Reinforcement learning, Bioactivity optimization, Information technology, T58.5-58.64, Chemistry, QD1-999

وصف الملف: electronic resource

Relation: https://doaj.org/toc/1758-2946

URL الوصول: https://doaj.org/article/c1989da341904f67a40940d73b85e5bc

المؤلفون: Bradshaw, John

المساهمون: Hernández-Lobato, José Miguel, Ghahramani, Zoubin, Schölkopf, Bernhard

مصطلحات موضوعية: 542, ML, machine learning, artificial intelligence, Deep generative models, de novo design, retrosynthesis, reaction prediction, chemoinformatics, cheminformatics, ml4molecules

URL الوصول: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.834515

المؤلفون: Ma, Chao

المساهمون: Hernandez Lobato, Jose Miguel

مصطلحات موضوعية: Bayesian inference, Decision making, Deep generative models, Deep learning, Machine learning, Missing data, Uncertainty

URL الوصول: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.866927

المؤلفون: Sheng-Yang Huang, Yuankai Wang, Enriqueta Llabres-Valls, Mochen Jiang, Fei Chen

المصدر: Land, Vol 13, Iss 9, p 1397 (2024)

مصطلحات موضوعية: urban design, deep generative models, cross-domain translation, landscape connectivity, progressive reasoning, urban ecology, Agriculture

وصف الملف: electronic resource

Relation: https://www.mdpi.com/2073-445X/13/9/1397; https://doaj.org/toc/2073-445X

URL الوصول: https://doaj.org/article/a2dae9b8b66f44eb80a685ab35747481

المؤلفون: Amit Gangwal, Azim Ansari, Iqrar Ahmad, Abul Kalam Azad, Vinoth Kumarasamy, Vetriselvan Subramaniyan, Ling Shing Wong

المصدر: Frontiers in Pharmacology, Vol 15 (2024)

مصطلحات موضوعية: ChatGPT, de novo drug design, deep generative models, AlphaFold, variational autoencoders, generative adversarial network, Therapeutics. Pharmacology, RM1-950

وصف الملف: electronic resource

Relation: https://www.frontiersin.org/articles/10.3389/fphar.2024.1331062/full; https://doaj.org/toc/1663-9812

URL الوصول: https://doaj.org/article/8074ab061f8049b08d0b9de47707813d

المؤلفون: Jonathan Warrell, Leonidas Salichos, Michael Gancz, Mark B. Gerstein

مصطلحات موضوعية: Bioinformatics and computational biology not elsewhere classified, Evolutionary biology not elsewhere classified, cultural evolution, computational music analysis, genomics, deep generative models

Relation: https://figshare.com/articles/journal_contribution/Supplementary_materials_from_Latent_evolutionary_signatures_a_general_framework_for_analysing_music_and_cultural_evolution/25382648

الاتاحة: https://doi.org/10.6084/m9.figshare.25382648.v1

المؤلفون: XIA, Haochong, SUN, Shuo, WANG, Xinrun, AN, Bo

المصدر: Research Collection School Of Computing and Information Systems

مصطلحات موضوعية: Deep Generative Models & Autoencoders, Time-Series/Data Streams, Artificial Intelligence and Robotics, Finance and Financial Management, Numerical Analysis and Scientific Computing

وصف الملف: application/pdf

Relation: https://ink.library.smu.edu.sg/sis_research/9129; https://ink.library.smu.edu.sg/context/sis_research/article/10132/viewcontent/29531_MarketGan_pvoa.pdf

الاتاحة: https://ink.library.smu.edu.sg/sis_research/9129
https://doi.org/10.1609/aaai.v38i14.29531
https://ink.library.smu.edu.sg/context/sis_research/article/10132/viewcontent/29531_MarketGan_pvoa.pdf

المؤلفون: Kissas, Georgios, Mishra, Siddhartha, Chatzi, Eleni, id_orcid:0 000-0002-6870-240X, De Lorenzis, Laura, id_orcid:0 000-0003-2748-3287

المصدر: Computer Methods in Applied Mechanics and Engineering, 428

مصطلحات موضوعية: Automated model discovery, Data-driven constitutive models, Formal grammars, Symbolic regression, Deep generative models

وصف الملف: application/application/pdf

Relation: info:eu-repo/semantics/altIdentifier/wos/001258103500001; info:eu-repo/grantAgreement/SNF/Projekte MINT/204316; http://hdl.handle.net/20.500.11850/677357

الاتاحة: https://hdl.handle.net/20.500.11850/677357
https://doi.org/10.3929/ethz-b-000677357

المؤلفون: Hisaichi Shibata, Shouhei Hanaoka, Takahiro Nakao, Tomohiro Kikuchi, Yuta Nakamura, Yukihiro Nomura, Takeharu Yoshikawa, Osamu Abe

المصدر: Applied Sciences, Vol 14, Iss 8, p 3489 (2024)

مصطلحات موضوعية: deep generative models, denoising, differential privacy, diffusion models, head magnetic resonance images, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999

Relation: https://www.mdpi.com/2076-3417/14/8/3489; https://doaj.org/toc/2076-3417; https://doaj.org/article/ba18df91de9f40e7bb1a09e6b53a0e5f

الاتاحة: https://doi.org/10.3390/app14083489
https://doaj.org/article/ba18df91de9f40e7bb1a09e6b53a0e5f

المؤلفون: Deshpande, Rucha, Kelkar, Varun A, Gotsis, Dimitrios, Kc, Prabhat, Zeng, Rongping, Myers, Kyle J, Brooks, Frank J, Anastasio, Mark A

المصدر: Med Phys ; ISSN:2473-4209 ; Volume:52 ; Issue:1

مصطلحات موضوعية: DGM evaluation, deep generative models, grand challenge

Relation: https://doi.org/10.1002/mp.17473; https://pubmed.ncbi.nlm.nih.gov/39447007

الاتاحة: https://doi.org/10.1002/mp.17473
https://pubmed.ncbi.nlm.nih.gov/39447007