Literatura científica selecionada sobre o tema "Data distribution shift"
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Artigos de revistas sobre o assunto "Data distribution shift"
Cheng, Luling, Xue Yang, Luliang Tang, Qian Duan, Zihan Kan, Xia Zhang e Xinyue Ye. "Spatiotemporal Analysis of Taxi-Driver Shifts Using Big Trace Data". ISPRS International Journal of Geo-Information 9, n.º 4 (24 de abril de 2020): 281. http://dx.doi.org/10.3390/ijgi9040281.
Texto completo da fonteIslind, Anna Sigridur, Tomas Lindroth, Johan Lundin e Gunnar Steineck. "Shift in translations: Data work with patient-generated health data in clinical practice". Health Informatics Journal 25, n.º 3 (13 de março de 2019): 577–86. http://dx.doi.org/10.1177/1460458219833097.
Texto completo da fonteSharet, Nir, e Ilan Shimshoni. "Analyzing Data Changes using Mean Shift Clustering". International Journal of Pattern Recognition and Artificial Intelligence 30, n.º 07 (25 de maio de 2016): 1650016. http://dx.doi.org/10.1142/s0218001416500166.
Texto completo da fonteTyagi, Dushyant. "Designing an Effective Combined Shewhart-CUSUM Control Scheme with Exponentially Distributed Data". International Journal of Mathematical, Engineering and Management Sciences 4, n.º 5 (1 de outubro de 2019): 1277–86. http://dx.doi.org/10.33889/ijmems.2019.4.5-101.
Texto completo da fonteKuang, Kun, Hengtao Zhang, Runze Wu, Fei Wu, Yueting Zhuang e Aijun Zhang. "Balance-Subsampled Stable Prediction Across Unknown Test Data". ACM Transactions on Knowledge Discovery from Data 16, n.º 3 (30 de junho de 2022): 1–21. http://dx.doi.org/10.1145/3477052.
Texto completo da fonteYe, Nanyang, Lin Zhu, Jia Wang, Zhaoyu Zeng, Jiayao Shao, Chensheng Peng, Bikang Pan, Kaican Li e Jun Zhu. "Certifiable Out-of-Distribution Generalization". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 9 (26 de junho de 2023): 10927–35. http://dx.doi.org/10.1609/aaai.v37i9.26295.
Texto completo da fonteRezaei, Ashkan, Anqi Liu, Omid Memarrast e Brian D. Ziebart. "Robust Fairness Under Covariate Shift". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 11 (18 de maio de 2021): 9419–27. http://dx.doi.org/10.1609/aaai.v35i11.17135.
Texto completo da fonteSoultan, Alaaeldin, Diego Pavón-Jordán, Ute Bradter, Brett K. Sandercock, Wesley M. Hochachka, Alison Johnston, Jon Brommer et al. "The future distribution of wetland birds breeding in Europe validated against observed changes in distribution". Environmental Research Letters 17, n.º 2 (1 de fevereiro de 2022): 024025. http://dx.doi.org/10.1088/1748-9326/ac4ebe.
Texto completo da fonteLone, Showkat Ahmad, Zahid Rasheed, Sadia Anwar, Majid Khan, Syed Masroor Anwar e Sana Shahab. "Enhanced fault detection models with real-life applications". AIMS Mathematics 8, n.º 8 (2023): 19595–636. http://dx.doi.org/10.3934/math.20231000.
Texto completo da fonteWalther, Gian-Reto, Silje Berger e Martin T. Sykes. "An ecological ‘footprint’ of climate change". Proceedings of the Royal Society B: Biological Sciences 272, n.º 1571 (28 de junho de 2005): 1427–32. http://dx.doi.org/10.1098/rspb.2005.3119.
Texto completo da fonteTeses / dissertações sobre o assunto "Data distribution shift"
Dadalto, Câmara Gomes Eduardo. "Improving artificial intelligence reliability through out-of-distribution and misclassification detection". Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASG018.
Texto completo da fonteThis thesis explores the intersection of machine learning (ML) and safety, aiming to address challenges associated with the deployment of intelligent systems in real-world scenarios. Despite significant progress in ML, concerns related to privacy, fairness, and trustworthiness have emerged, prompting the need for enhancing the reliability of AI systems. The central focus of the thesis is to enable ML algorithms to detect deviations from normal behavior, thereby contributing to the overall safety of intelligent systems.The thesis begins by establishing the foundational concepts of out-of-distribution (OOD) detection and misclassification detection in Chapter 1, providing essential background literature and explaining key principles. The introduction emphasizes the importance of addressing issues related to unintended and harmful behavior in ML, particularly when AI systems produce unexpected outcomes due to various factors such as mismatches in data distributions.In Chapter 2, the thesis introduces a novel OOD detection method based on the Fisher-Rao geodesic distance between probability distributions. This approach unifies the formulation of detection scores for both network logits and feature spaces, contributing to improved robustness and reliability in identifying samples outside the training distribution.Chapter 3 presents an unsupervised OOD detection method that analyzes neural trajectories without requiring supervision or hyperparameter tuning. This method aims to identify atypical sample trajectories through various layers, enhancing the adaptability of ML models to diverse scenarios.Chapter 4 focuses on consolidating and enhancing OOD detection by combining multiple detectors effectively. It presents a universal method for ensembling existing detectors, transforming the problem into a multi-variate hypothesis test and leveraging meta-analysis tools. This approach improves data shift detection, making it a valuable tool for real-time model performance monitoring in dynamic and evolving environments.In Chapter 5, the thesis addresses misclassification detection and uncertainty estimation through a data-driven approach, introducing a practical closed-form solution. The method quantifies uncertainty relative to an observer, distinguishing between confident and uncertain predictions even in the face of challenging or unfamiliar data. This contributes to a more nuanced understanding of the model's confidence and helps flag predictions requiring human intervention.The thesis concludes by discussing future perspectives and directions for improving safety in ML and AI, emphasizing the ongoing evolution of AI systems towards greater transparency, robustness, and trustworthiness. The collective work presented in the thesis represents a significant step forward in advancing AI safety, contributing to the development of more reliable and trustworthy machine learning models that can operate effectively in diverse and dynamic real-world scenarios
Lowry, Sonia L. "Analysis of statnamic load test data using a load shed distribution model". [Tampa, Fla.] : University of South Florida, 2005. http://purl.fcla.edu/fcla/etd/SFE0001238.
Texto completo da fonteBickel, Steffen. "Learning under differing training and test distributions". Phd thesis, Universität Potsdam, 2008. http://opus.kobv.de/ubp/volltexte/2009/3333/.
Texto completo da fonteEines der wichtigsten Probleme im Maschinellen Lernen ist das Trainieren von Vorhersagemodellen aus Trainingsdaten und das Ableiten von Vorhersagen für Testdaten. Vorhersagemodelle basieren üblicherweise auf der Annahme, dass Trainingsdaten aus der gleichen Verteilung gezogen werden wie Testdaten. In der Praxis ist diese Annahme oft nicht erfüllt, zum Beispiel, wenn Trainingsdaten durch Fragebögen gesammelt werden. Hier steht meist nur eine verzerrte Zielpopulation zur Verfügung, denn Teile der Population können unterrepräsentiert sein, nicht erreichbar sein, oder ignorieren die Aufforderung zum Ausfüllen des Fragebogens. In vielen Anwendungen stehen nur sehr wenige Trainingsdaten aus der Testverteilung zur Verfügung, weil solche Daten teuer oder aufwändig zu sammeln sind. Daten aus alternativen Quellen, die aus ähnlichen Verteilungen gezogen werden, sind oft viel einfacher und günstiger zu beschaffen. Die vorliegende Arbeit beschäftigt sich mit dem Lernen von Vorhersagemodellen aus Trainingsdaten, deren Verteilung sich von der Testverteilung unterscheidet. Es werden verschiedene Problemstellungen behandelt, die von unterschiedlichen Annahmen über die Beziehung zwischen Trainings- und Testverteilung ausgehen. Darunter fallen auch Multi-Task-Lernen und Lernen unter Covariate Shift und Sample Selection Bias. Es werden mehrere neue Modelle hergeleitet, die direkt den Unterschied zwischen Trainings- und Testverteilung charakterisieren, ohne dass eine einzelne Schätzung der Verteilungen nötig ist. Zentrale Bestandteile der Modelle sind Gewichtungsfaktoren, mit denen die Trainingsverteilung durch Umgewichtung auf die Testverteilung abgebildet wird. Es werden kombinierte Modelle zum Lernen mit verschiedenen Trainings- und Testverteilungen untersucht, für deren Schätzung nur ein einziges Optimierungsproblem gelöst werden muss. Die kombinierten Modelle können mit zwei Optimierungsschritten approximiert werden und dadurch kann fast jedes gängige Vorhersagemodell so erweitert werden, dass verzerrte Trainingsverteilungen korrigiert werden. In Fallstudien zu Email-Spam-Filterung, HIV-Therapieempfehlung, Zielgruppenmarketing und anderen Anwendungen werden die neuen Modelle mit Referenzmethoden verglichen.
Neubert, Karin. "Das nichtparametrische Behrens-Fisher-Problem: ein studentisierter Permutationstest und robuste Konfidenzintervalle für den Shift-Effekt". Doctoral thesis, 2006. http://hdl.handle.net/11858/00-1735-0000-000D-F21D-C.
Texto completo da fonteLivros sobre o assunto "Data distribution shift"
Berg, John C. Leave It in the Ground. ABC-CLIO, LLC, 2019. http://dx.doi.org/10.5040/9798400677960.
Texto completo da fonteRay, Ranjan. The Link between Preferences, Prices, Inequality, and Poverty. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198812555.003.0007.
Texto completo da fonteBallon, Paola, e Jorge Dávalos. Inequality and the changing nature of work in Peru. UNU-WIDER, 2020. http://dx.doi.org/10.35188/unu-wider/2020/925-9.
Texto completo da fonteGaiha, Raghav, Raghbendra Jha, Vani S. Kulkarni e Nidhi Kaicker. Diets, Nutrition, and Poverty. Editado por Ronald J. Herring. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780195397772.013.029.
Texto completo da fonteFleury, James, Bryan Hikari Hartzheim e Stephen Mamber, eds. The Franchise Era. Edinburgh University Press, 2019. http://dx.doi.org/10.3366/edinburgh/9781474419222.001.0001.
Texto completo da fonteCapítulos de livros sobre o assunto "Data distribution shift"
Oza, Poojan, Hien V. Nguyen e Vishal M. Patel. "Multiple Class Novelty Detection Under Data Distribution Shift". In Computer Vision – ECCV 2020, 432–49. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58571-6_26.
Texto completo da fonteAshmore, Rob, e Matthew Hill. "“Boxing Clever”: Practical Techniques for Gaining Insights into Training Data and Monitoring Distribution Shift". In Lecture Notes in Computer Science, 393–405. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99229-7_33.
Texto completo da fonteStade, Dawid, e Martin Manns. "Robotic Assembly Line Balancing with Multimodal Stochastic Processing Times". In Advances in Automotive Production Technology – Towards Software-Defined Manufacturing and Resilient Supply Chains, 78–84. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-27933-1_8.
Texto completo da fonteDasu, Tamraparni, Shankar Krishnan, Dongyu Lin, Suresh Venkatasubramanian e Kevin Yi. "Change (Detection) You Can Believe in: Finding Distributional Shifts in Data Streams". In Advances in Intelligent Data Analysis VIII, 21–34. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03915-7_3.
Texto completo da fonteXiang, Brian, e Abdelrahman Abdelmonsef. "Vector-Based Data Improves Left-Right Eye-Tracking Classifier Performance After a Covariate Distributional Shift". In HCI International 2022 - Late Breaking Papers. Design, User Experience and Interaction, 617–32. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-17615-9_44.
Texto completo da fonteClement, Tobias, Hung Truong Thanh Nguyen, Nils Kemmerzell, Mohamed Abdelaal e Davor Stjelja. "Coping with Data Distribution Shifts: XAI-Based Adaptive Learning with SHAP Clustering for Energy Consumption Prediction". In Lecture Notes in Computer Science, 147–59. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-8391-9_12.
Texto completo da fonteBrajesh, Saurabh. "Big Data Analytics in Retail Supply Chain". In Big Data, 1473–94. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9840-6.ch067.
Texto completo da fonteShimano, Koji, Yui Oyake e Tsuyoshi Kobayashi. "Methods and Practices for Analyzing Vegetation Shift Using Phytosociological Hierarchical Data". In Vegetation Index and Dynamics - Methodologies for Teaching Plant Diversity and Conservation Status [Working Title]. IntechOpen, 2023. http://dx.doi.org/10.5772/intechopen.1003759.
Texto completo da fonteBen Loussaief, Eddardaa, e Domenec Puig. "Towards Cross-Sites Generalization for Prostate MRI Segmentation to Unseen Data". In Frontiers in Artificial Intelligence and Applications. IOS Press, 2022. http://dx.doi.org/10.3233/faia220341.
Texto completo da fontePéron, Guillaume. "Spatial demography". In Demographic Methods across the Tree of Life, 259–72. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198838609.003.0015.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Data distribution shift"
Zhu, Yichen, Jian Yuan, Bo Jiang, Tao Lin, Haiming Jin, Xinbing Wang e Chenghu Zhou. "Prediction with Incomplete Data under Agnostic Mask Distribution Shift". 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.2023/525.
Texto completo da fonteXie, Hui, Xuanxuan Liu e Li Guo. "Semi-supervised One-pass Learning under Distribution Shift". In ICBDT 2023: 2023 6th International Conference on Big Data Technologies. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3627377.3627446.
Texto completo da fonteHu, Xuanming, Wei Fan, Kun Yi, Pengfei Wang, Yuanbo Xu, Yanjie Fu e Pengyang Wang. "Boosting Urban Prediction via Addressing Spatial-Temporal Distribution Shift". In 2023 IEEE International Conference on Data Mining (ICDM). IEEE, 2023. http://dx.doi.org/10.1109/icdm58522.2023.00025.
Texto completo da fonteGao, Yuan, Xiang Wang, Xiangnan He, Zhenguang Liu, Huamin Feng e Yongdong Zhang. "Alleviating Structural Distribution Shift in Graph Anomaly Detection". In WSDM '23: The Sixteenth ACM International Conference on Web Search and Data Mining. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3539597.3570377.
Texto completo da fonteGuo, Lan-Zhe, Zhi Zhou e Yu-Feng Li. "RECORD: Resource Constrained Semi-Supervised Learning under Distribution Shift". In KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3394486.3403214.
Texto completo da fonteHuang, Biwei, Kun Zhang, Jiji Zhang, Ruben Sanchez-Romero, Clark Glymour e Bernhard Scholkopf. "Behind Distribution Shift: Mining Driving Forces of Changes and Causal Arrows". In 2017 IEEE International Conference on Data Mining (ICDM). IEEE, 2017. http://dx.doi.org/10.1109/icdm.2017.114.
Texto completo da fonteZhu, Yichen, e Bo Jiang. "StableMiss+: Prediction with Incomplete Data Under Agnostic Mask Distribution Shift". In ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2024. http://dx.doi.org/10.1109/icassp48485.2024.10446980.
Texto completo da fonteXiao, Teng, Zhengyu Chen e Suhang Wang. "Reconsidering Learning Objectives in Unbiased Recommendation: A Distribution Shift Perspective". In KDD '23: The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3580305.3599487.
Texto completo da fonteBarrows, Josh, Valentin Radu, Matthew Hill e Fabio Ciravegna. "Active Learning with Data Distribution Shift Detection for Updating Localization Systems". In 2021 International Conference on Indoor Positioning and Indoor Navigation (IPIN). IEEE, 2021. http://dx.doi.org/10.1109/ipin51156.2021.9662543.
Texto completo da fonteCaron, Matthew. "Shortcut Learning in Financial Text Mining: Exposing the Overly Optimistic Performance Estimates of Text Classification Models under Distribution Shift". In 2022 IEEE International Conference on Big Data (Big Data). IEEE, 2022. http://dx.doi.org/10.1109/bigdata55660.2022.10020933.
Texto completo da fonteRelatórios de organizações sobre o assunto "Data distribution shift"
Dubeck, Margaret M., Jonathan M. B. Stern e Rehemah Nabacwa. Learning to Read in a Local Language in Uganda: Creating Learner Profiles to Track Progress and Guide Instruction Using Early Grade Reading Assessment Results. RTI Press, junho de 2021. http://dx.doi.org/10.3768/rtipress.2021.op.0068.2106.
Texto completo da fonteMaupin, Julie, e Dr Michael Mamoun. DTPH56-06-T-0004 Plastic Pipe Failure, Risk, and Threat Analysis. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), março de 2006. http://dx.doi.org/10.55274/r0012119.
Texto completo da fonteAterido, Reyes, Mary Hallward-Driemeier e Carmen Pagés. Investment Climate and Employment Growth: The Impact of Access to Finance, Corruption and Regulations across Firms. Inter-American Development Bank, outubro de 2007. http://dx.doi.org/10.18235/0011259.
Texto completo da fonteSalavisa, Isabel, Mark Soares e Sofia Bizarro. A Critical Assessment of Organic Agriculture in Portugal: A reflection on the agro-food system transition. DINÂMIA'CET-Iscte, 2021. http://dx.doi.org/10.15847/dinamiacet-iul.wp.2021.05.
Texto completo da fonteShapovalova, Daria, Tavis Potts, John Bone e Keith Bender. Measuring Just Transition : Indicators and scenarios for a Just Transition in Aberdeen and Aberdeenshire. University of Aberdeen, outubro de 2023. http://dx.doi.org/10.57064/2164/22364.
Texto completo da fonteHealth Innovation & Technology in Latin America & the Caribbean. Inter-American Development Bank, abril de 2024. http://dx.doi.org/10.18235/0012923.
Texto completo da fonte