Letteratura scientifica selezionata sul tema "Optimisation du code latent"
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Articoli di riviste sul tema "Optimisation du code latent"
Dubisz, Stanisław. "Najnowsze dzieje języka polskiego (1918–2018)". Poradnik Językowy, n. 10/2022(799) (5 settembre 2022): 11–26. http://dx.doi.org/10.33896/porj.2022.10.1.
Testo completoHetyei, Csaba, e Ferenc Szlivka. "COUNTER-ROTATING DUAL ROTOR WIND TURBINE LAYOUT OPTIMISATION". Acta Polytechnica 61, n. 2 (30 aprile 2021): 342–49. http://dx.doi.org/10.14311/ap.2021.61.0342.
Testo completoYe, Fei, e Adrian G. Bors. "Task-Free Continual Generation and Representation Learning via Dynamic Expansionable Memory Cluster". Proceedings of the AAAI Conference on Artificial Intelligence 38, n. 15 (24 marzo 2024): 16451–59. http://dx.doi.org/10.1609/aaai.v38i15.29582.
Testo completoDahlin, J. E. "Cook up better code [Software code optimisation]". Electronics Systems and Software 5, n. 6 (1 dicembre 2007): 24–27. http://dx.doi.org/10.1049/ess:20070606.
Testo completoJindal, Richa, e Sanjay Singla. "Ant colony optimisation for latent fingerprint matching". International Journal of Advanced Intelligence Paradigms 19, n. 2 (2021): 161. http://dx.doi.org/10.1504/ijaip.2021.115247.
Testo completoLee, Isack, e Seok Bong Yoo. "Latent-PER: ICA-Latent Code Editing Framework for Portrait Emotion Recognition". Mathematics 10, n. 22 (14 novembre 2022): 4260. http://dx.doi.org/10.3390/math10224260.
Testo completoLienard, M., e P. Degauque. "Correlation radar: optimisation of code generator architecture". Electronics Letters 39, n. 19 (2003): 1405. http://dx.doi.org/10.1049/el:20030844.
Testo completoLewis, S. J., D. G. Ireland e W. Vanderbauwhede. "Code optimisation in a nested-sampling algorithm". Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 785 (giugno 2015): 105–9. http://dx.doi.org/10.1016/j.nima.2015.03.006.
Testo completoDhamdhere, D. M. "A fast algorithm for code movement optimisation". ACM SIGPLAN Notices 23, n. 10 (ottobre 1988): 172–80. http://dx.doi.org/10.1145/51607.51621.
Testo completoMerllié, Dominique, e Bruno Boussard. "«Comment vous étes-vous connus ?» - 2 - Code patent, code latent". Actes de la recherche en sciences sociales 70, n. 1 (1987): 87–92. http://dx.doi.org/10.3406/arss.1987.2398.
Testo completoTesi sul tema "Optimisation du code latent"
Li, Huiyu. "Exfiltration et anonymisation d'images médicales à l'aide de modèles génératifs". Electronic Thesis or Diss., Université Côte d'Azur, 2024. http://www.theses.fr/2024COAZ4041.
Testo completoThis thesis aims to address some specific safety and privacy issues when dealing with sensitive medical images within data lakes. This is done by first exploring potential data leakage when exporting machine learning models and then by developing an anonymization approach that protects data privacy.Chapter 2 presents a novel data exfiltration attack, termed Data Exfiltration by Compression (DEC), which leverages image compression techniques to exploit vulnerabilities in the model exporting process. This attack is performed when exporting a trained network from a remote data lake, and is applicable independently of the considered image processing task. By exploring both lossless and lossy compression methods, this chapter demonstrates how DEC can effectively be used to steal medical images and reconstruct them with high fidelity, using two public CT and MR datasets. This chapter also explores mitigation measures that a data owner can implement to prevent the attack. It first investigates the application of differential privacy measures, such as Gaussian noise addition, to mitigate this attack, and explores how attackers can create attacks resilient to differential privacy. Finally, an alternative model export strategy is proposed which involves model fine-tuning and code verification.Chapter 3 introduces the Generative Medical Image Anonymization framework, a novel approach to balance the trade-off between preserving patient privacy while maintaining the utility of the generated images to solve downstream tasks. The framework separates the anonymization process into two key stages: first, it extracts identity and utility-related features from medical images using specially trained encoders; then, it optimizes the latent code to achieve the desired trade-off between anonymity and utility. We employ identity and utility encoders to verify patient identities and detect pathologies, and use a generative adversarial network-based auto-encoder to create realistic synthetic images from the latent space. During optimization, we incorporate these encoders into novel loss functions to produce images that remove identity-related features while maintaining their utility to solve a classification problem. The effectiveness of this approach is demonstrated through extensive experiments on the MIMIC-CXR chest X-ray dataset, where the generated images successfully support lung pathology detection.Chapter 4 builds upon the work from Chapter 4 by utilizing generative adversarial networks (GANs) to create a more robust and scalable anonymization solution. The framework is structured into two distinct stages: first, we develop a streamlined encoder and a novel training scheme to map images into a latent space. In the second stage, we minimize the dual-loss functions proposed in Chapter 3 to optimize the latent representation of each image. This method ensures that the generated images effectively remove some identifiable features while retaining crucial diagnostic information. Extensive qualitative and quantitative experiments on the MIMIC-CXR dataset demonstrate that our approach produces high-quality anonymized images that maintain essential diagnostic details, making them well-suited for training machine learning models in lung pathology classification.The conclusion chapter summarizes the scientific contributions of this work, and addresses remaining issues and challenges for producing secured and privacy preserving sensitive medical data
Lomüller, Victor. "Générateur de code multi-temps et optimisation de code multi-objectifs". Thesis, Grenoble, 2014. http://www.theses.fr/2014GRENM050/document.
Testo completoCompilation is an essential step to create efficient applications.This step allows the use of high-level and target independent languages while maintaining good performances.However, many obstacle prevent compilers to fully optimize applications.For static compilers, the major obstacle is the poor knowledge of the execution context, particularly knowledge on the architecture and data.This knowledge is progressively known during the application life cycle.Compilers progressively integrated dynamic code generation techniques to be able to use this knowledge.However, those techniques usually focuses on improvement of hardware capabilities usage but don't take data into account.In this thesis, we investigate data usage in applications optimization process on Nvidia GPU.We present a method that uses different moments in the application life cycle to create adaptive libraries able to take into account data size.Those libraries can therefore provide more adapted kernels.With the GEMM algorithm, the method is able to provide gains up to 100~\% while avoiding code size explosion.The thesis also investigate runtime code generation gains and costs from the execution speed, memory footprint and energy consumption point of view.We present and study 2 light-weight runtime code generation approaches that can specialize code.We show that those 2 approaches can obtain comparable, and even superior, gains compared to LLVM but at a lower cost
Chaabane, Rim. "Analyse et optimisation de patterns de code". Paris 8, 2011. http://www.theses.fr/2011PA084174.
Testo completoNotre travail consiste en l’analyse et l’optimisation du code source d’applications de type système hérité (ou legacy system). Nous avons particulièrement travaillé sur un logiciel, appelé GP3, qui est développé et maintenu par la société de finances Sungard. Ce logiciel existe depuis plus d’une vingtaine d’années, il est écrit en un langage propriétaire, procédural et de 4ème génération, appelé ADL (ou Application Development Language). Ce logiciel à été initialement développé sous VMS et accédait à des bases de données d’ancienne génération. Pour des raisons commerciales, il fut porté sous UNIX et s’adresse maintenant à des SGBD-R de nouvelles génération ; Oracle et Sybase. Il a également été étendu de manière à offrir une interface web. Ce système hérité doit maintenant faire face à de nouveaux défis, comme la croissance de la taille des bases de données. Durant ces 20 dernières années, nous avons pu observer la fusion de plusieurs entreprises, ce qui implique la fusion de bases de données. Ces dernières peuvent dépasser les 1 Téra de données et plus encore sont à prévoir à l’avenir. Dans ce nouveau contexte, le langage ADL montre des limites à traiter une aussi importante masse de données. Des patterns de code, désignant des structures d’accès en base, sont suspectés d’être responsables de dégradations des performances. Notre travail consiste à détecter toutes les instances de patterns dans le code source, puis d’identifier les instances les plus consommatrices en temps d’exécution et en nombre d’appels. Nous avons développé un premier outil, nommé Adlmap, basé sur l’analyse statique de code, et qui permet de détecter toutes les accès en base dans le code source. Les accès en base identifiés comme patterns de code sont marqués. Le second outil que nous avons développé, nommé Pmonitor, est basé sur une analyse hybride ; combinaison d’analyses statique et dynamique. Cet outil nous permet de mesurer les performances des patterns de code et ainsi, d’identifier les instances les moins performantes
Jupp, Ian David. "The optimisation of discrete pixel code aperture telescopes". Thesis, University of Southampton, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.243190.
Testo completoHalli, Abderrahmane Nassim. "Optimisation de code pour application Java haute-performance". Thesis, Université Grenoble Alpes (ComUE), 2016. http://www.theses.fr/2016GREAM047/document.
Testo completoL'auteur n'a pas fourni de résumé en anglais
Adamczewski, Martine. "Vectorisation, analyse et optimisation d'un code bidimensionnel eulérien". Bordeaux 1, 1986. http://www.theses.fr/1986BOR10603.
Testo completoAdamczewski, Martine. "Vectorisation, analyse et optimisation d'un code bidimensionnel eulérien". Grenoble 2 : ANRT, 1986. http://catalogue.bnf.fr/ark:/12148/cb375953123.
Testo completoRastelli, Riccardo, e Nial Friel. "Optimal Bayesian estimators for latent variable cluster models". Springer Nature, 2018. http://dx.doi.org/10.1007/s11222-017-9786-y.
Testo completoCosma, Georgina. "An approach to source-code plagiarism detection investigation using latent semantic analysis". Thesis, University of Warwick, 2008. http://wrap.warwick.ac.uk/3575/.
Testo completoRadhouane, Ridha. "Optimisation de modems VDSL". Valenciennes, 2000. https://ged.uphf.fr/nuxeo/site/esupversions/63219c61-e8b1-424c-a9aa-4c3502179c78.
Testo completoLibri sul tema "Optimisation du code latent"
Adamczewski, Martine. Vectorisation, analyse et optimisation d'un code bidimensionnel eulérien. Grenoble: A.N.R.T, Université Pierre Mendes France (Grenoble II), 1986.
Cerca il testo completoJaana, Laiho, Wacker Achim e Novosad Tomáš, a cura di. Radio network planning and optimisation for UMTS. Hoboken, N.J: Wiley, 2005.
Cerca il testo completoJ, Nawrocki Maciej, Dohler Mischa e Aghvami A. Hamid, a cura di. Understanding UMTS radio network modelling, planning and automated optimisation: Theory and practice / edited by Maciej J. Nawrocki, Mischa Dohler, A. Hamid Aghvami. Chichester: John Wiley & Sons, 2006.
Cerca il testo completoWille, Volker, e Phil Pickering. WCDMA Performance Optimisation. Wiley-Interscience, 2007.
Cerca il testo completoBonaventura, Luca, René Redler e Reinhard Budich. Earth System Modelling - Volume 2: Algorithms, Code Infrastructure and Optimisation. Springer, 2011.
Cerca il testo completoPetoumenos, Pavlos. International Workshop on Code Optimisation for Multi and Many Cores. Association for Computing Machinery, 2017.
Cerca il testo completoRadio network planning and optimisation for UMTS. 2a ed. Chichester, UK: John Wiley & Sons, 2004.
Cerca il testo completoAWAKEN THE SLUMBERING GODDESS: THE LATENT CODE OF THE HINDU GODDESS ARCHETYPES. BookSurge Publishing, 2007.
Cerca il testo completoAghvami, Hamid, Mischa Dohler e Maciej Nawrocki. Understanding Umts Radio Network Modelling, Planning and Automated Optimisation. Wiley & Sons, Incorporated, John, 2006.
Cerca il testo completoProceedings of the 2015 International Workshop on Code Optimisation for Multi and Many Cores. Association for Computing Machinery, 2015.
Cerca il testo completoCapitoli di libri sul tema "Optimisation du code latent"
Mozdzynski, George. "Code Optimisation". In Earth System Modelling - Volume 2, 67–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23831-4_7.
Testo completoCapon, P. C., e P. J. Jinks. "Code optimisation". In Compiler Engineering Using Pascal, 187–93. London: Macmillan Education UK, 1988. http://dx.doi.org/10.1007/978-1-349-10401-7_14.
Testo completoSharp, Richard. "6. Analysis and Optimisation of Intermediate Code". In Higher-Level Hardware Synthesis, 87–111. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24657-2_6.
Testo completoZhou, Jiayu, e Xiaoqiang Zhu. "EHA3D: Expressive Head Avatar via Disentangled Latent Code". In Communications in Computer and Information Science, 243–57. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-3623-2_18.
Testo completoZukerman, Moshe, Pang L. Hiew e Maxim Gitlits. "FEC Code Rate and Bandwidth Optimisation in WATM Networks". In Multiaccess, Mobility and Teletraffic, 207–20. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5437-0_17.
Testo completoWang, Shuo, Chen Qin, Nicolò Savioli, Chen Chen, Declan P. O’Regan, Stuart Cook, Yike Guo, Daniel Rueckert e Wenjia Bai. "Joint Motion Correction and Super Resolution for Cardiac Segmentation via Latent Optimisation". In Medical Image Computing and Computer Assisted Intervention – MICCAI 2021, 14–24. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87199-4_2.
Testo completoGromniak, Martin, Sven Magg e Stefan Wermter. "Neural Field Conditioning Strategies for 2D Semantic Segmentation". In Artificial Neural Networks and Machine Learning – ICANN 2023, 520–32. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44210-0_42.
Testo completoXu, Ting, Dibo Shi, Yi Ji e Chunping Liu. "Image Generation with the Enhanced Latent Code and Sub-pixel Sampling". In Neural Information Processing, 387–98. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63830-6_33.
Testo completoLee, Isack, Jun-Seok Yun, Hee Hyeon Kim, Youngju Na e Seok Bong Yoo. "LatentGaze: Cross-Domain Gaze Estimation Through Gaze-Aware Analytic Latent Code Manipulation". In Computer Vision – ACCV 2022, 161–78. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-26348-4_10.
Testo completoBeckmann, Olav, Alastair Houghton, Michael Mellor e Paul H. J. Kelly. "Runtime Code Generation in C++ as a Foundation for Domain-Specific Optimisation". In Domain-Specific Program Generation, 291–306. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-25935-0_17.
Testo completoAtti di convegni sul tema "Optimisation du code latent"
Zhang, Chenyu. "Improving Depth Map Geometric Consistency Using an Enhanced Transformer Latent Code Model". In 2024 9th International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS), 586–89. IEEE, 2024. https://doi.org/10.1109/iciibms62405.2024.10792864.
Testo completoAndreou, Thomas, Craig White, Konstantinos Kontis, Shahrokh Shahpar e Nicholas Brown. "Part 1: A Swirl Vane Generation Code for Fuel Spray Nozzles". In ASME Turbo Expo 2020: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/gt2020-15414.
Testo completoLing, Wang, Phil Blunsom, Edward Grefenstette, Karl Moritz Hermann, Tomáš Kočiský, Fumin Wang e Andrew Senior. "Latent Predictor Networks for Code Generation". In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Stroudsburg, PA, USA: Association for Computational Linguistics, 2016. http://dx.doi.org/10.18653/v1/p16-1057.
Testo completoSchüle, Maximilian E., Maximilian Springer, Alfons Kemper e Thomas Neumann. "LLVM code optimisation for automatic differentiation". In SIGMOD/PODS '22: International Conference on Management of Data. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3533028.3533302.
Testo completoRashed, Muhammad Salman, Marco Meijer e Paul D. Teal. "Doppler Tolerant Code Optimisation Scheme for Multi-code Sonar Systems". In OCEANS 2019 - Marseille. IEEE, 2019. http://dx.doi.org/10.1109/oceanse.2019.8867543.
Testo completoSolhjoo, Babak, e Emanuele Rodolà. "Latent Code Disentanglement Using Orthogonal Latent Codes and Inter-Domain Signal Transformation". In 15th International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and Technology Publications, 2023. http://dx.doi.org/10.5220/0011860200003393.
Testo completode Gevigney, Valentin Durand, Pierre-Francois Marteau, Arnaud Delhay e Damien Lolive. "Video Latent Code Interpolation for Anomalous Behavior Detection". In 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2020. http://dx.doi.org/10.1109/smc42975.2020.9282857.
Testo completoZheng, Fengde, e Chunyu Yang. "Latent fingerprint match using Minutia Spherical Coordinate Code". In 2015 International Conference on Biometrics (ICB). IEEE, 2015. http://dx.doi.org/10.1109/icb.2015.7139061.
Testo completoScannell, Aidan, Carl Henrik Ek e Arthur Richards. "Trajectory Optimisation in Learned Multimodal Dynamical Systems via Latent-ODE Collocation". In 2021 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2021. http://dx.doi.org/10.1109/icra48506.2021.9561362.
Testo completoBarattin, Simone, Christos Tzelepis, Ioannis Patras e Nicu Sebe. "Attribute-Preserving Face Dataset Anonymization via Latent Code Optimization". In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2023. http://dx.doi.org/10.1109/cvpr52729.2023.00773.
Testo completoRapporti di organizzazioni sul tema "Optimisation du code latent"
Solomon, A. D., M. D. Morris, J. Martin e M. Olszewski. Development of a simulation code for a latent heat thermal energy storage system in a space station. Office of Scientific and Technical Information (OSTI), aprile 1986. http://dx.doi.org/10.2172/5777340.
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