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Artykuły w czasopismach na temat "Learning with pre-built models"
Fauszt, Tibor, László Bognár i Ágnes Sándor. "Increasing the Prediction Power of Moodle Machine Learning Models with Self-defined Indicators". International Journal of Emerging Technologies in Learning (iJET) 16, nr 24 (21.12.2021): 23–39. http://dx.doi.org/10.3991/ijet.v16i24.23923.
Pełny tekst źródłaErian, Karim H., Pedro H. Regalado i James M. Conrad. "Missing data handling for machine learning models". IAES International Journal of Robotics and Automation (IJRA) 10, nr 2 (1.06.2021): 123. http://dx.doi.org/10.11591/ijra.v10i2.pp123-132.
Pełny tekst źródłaCinar, Eyup. "A Sensor Fusion Method Using Transfer Learning Models for Equipment Condition Monitoring". Sensors 22, nr 18 (8.09.2022): 6791. http://dx.doi.org/10.3390/s22186791.
Pełny tekst źródłaLage, Isaac, Emily Chen, Jeffrey He, Menaka Narayanan, Been Kim, Samuel J. Gershman i Finale Doshi-Velez. "Human Evaluation of Models Built for Interpretability". Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 7 (28.10.2019): 59–67. http://dx.doi.org/10.1609/hcomp.v7i1.5280.
Pełny tekst źródłaYimam, Seid Muhie, Abinew Ali Ayele, Gopalakrishnan Venkatesh, Ibrahim Gashaw i Chris Biemann. "Introducing Various Semantic Models for Amharic: Experimentation and Evaluation with Multiple Tasks and Datasets". Future Internet 13, nr 11 (27.10.2021): 275. http://dx.doi.org/10.3390/fi13110275.
Pełny tekst źródłaWang, Chengyu, Mengli Cheng, Xu Hu i Jun Huang. "EasyASR: A Distributed Machine Learning Platform for End-to-end Automatic Speech Recognition". Proceedings of the AAAI Conference on Artificial Intelligence 35, nr 18 (18.05.2021): 16111–13. http://dx.doi.org/10.1609/aaai.v35i18.18028.
Pełny tekst źródłaSingh, Hrithik, Shambhavi Kaushik, Shruti Talyan i Kartikeya Dwivedi. "Skin Cancer Detection Using Deep Learning techniques". International Journal for Research in Applied Science and Engineering Technology 10, nr 5 (31.05.2022): 4296–305. http://dx.doi.org/10.22214/ijraset.2022.43090.
Pełny tekst źródłaPedditzi, Maria L., i Marcello Nonnis. "Pre-service Teachers' Representations About Children's Learning: A Pilot Study". Open Psychology Journal 13, nr 1 (13.11.2020): 315–20. http://dx.doi.org/10.2174/1874350102013010315.
Pełny tekst źródłaZhang, Li, Haimeng Fan, Chengxia Peng, Guozheng Rao i Qing Cong. "Sentiment Analysis Methods for HPV Vaccines Related Tweets Based on Transfer Learning". Healthcare 8, nr 3 (28.08.2020): 307. http://dx.doi.org/10.3390/healthcare8030307.
Pełny tekst źródłaAchicanoy, Harold, Deisy Chaves i Maria Trujillo. "StyleGANs and Transfer Learning for Generating Synthetic Images in Industrial Applications". Symmetry 13, nr 8 (16.08.2021): 1497. http://dx.doi.org/10.3390/sym13081497.
Pełny tekst źródłaRozprawy doktorskie na temat "Learning with pre-built models"
Thompson, Kate. "Models as mindtools for environmental education: How do students use models to learn about a complex socio-environmental system?" University of Sydney, 2008. http://hdl.handle.net/2123/3608.
Pełny tekst źródłaEnvironmental issues are complex and understanding them involves integration of different areas of knowledge, feedback and time delays, however strategies to cope with complexity are not often used or taught in environmental education. The aim of this thesis is to examine the benefit of three such strategies for environmental education: multiple external representations, learning from models, and collaborative learning. The socio-environmental system modelled was visitor impact in a national park in Australia. Students in Year 9 and 10 from two schools were given a text description (Text group) and either a system dynamics model (SDM group), an agent-based model (ABM group), or both models (SDM & ABM group). This experimental design allowed learning outcomes (environmental and system dynamics knowledge, and understanding of the socio-environmental system) and use of the model(s) (in terms of the proportion of time spent on each screen, activities, and strategies) to be compared in each learning environment (individual and collaborative). Multiple external representations were the most successful strategy in the individual learning environment in terms of increases in environmental knowledge. However, students given only the system dynamics model had greater understanding of the system, and students given only the agent-based model increased environmental knowledge easily identified in the animated representation. Prior knowledge, patterns of use, strategies for changing variables and the representational affordances of the models explained some of these differences. In particular, prior knowledge was an important indicator of how students coordinated use of the models in the SDM & ABM group. Learning with a system dynamics model was the most successful strategy for students in the collaborative learning environment. Differences between the learning environments were detected in all groups with respect to both learning outcomes and use of the models due to prior knowledge, interrogation of the models, and the learning environments themselves. These experiments have provided evidence that strategies for understanding complex systems provide viable methods of communicating complex ideas to school-aged students with varying levels of prior knowledge. In particular, multiple external representations provided students with flexibility in how they learned; models allowed students to experiment with a system otherwise not allowed; and a collaborative learning environment facilitated students’ interpretation of a system dynamics model.
Thompson, Kate. "Models as mindtools for environmental education: How do students use models to learn about a complex socio-environmental system?" Thesis, The University of Sydney, 2007. http://hdl.handle.net/2123/3608.
Pełny tekst źródłaBitter, James. "Integrative Family Therapy and Counseling: Advanced Practices Across Multiple Theoretical Models (Pre-Convention Learning Institute)". Digital Commons @ East Tennessee State University, 2009. https://dc.etsu.edu/etsu-works/6111.
Pełny tekst źródłaJahanshahi, Kaveh. "Quantification of the influences of built-form upon travel of employed adults : new models based on the UK National Travel Survey". Thesis, University of Cambridge, 2017. https://www.repository.cam.ac.uk/handle/1810/267841.
Pełny tekst źródłaDureckova, Hana. "Robust Machine Learning QSPR Models for Recognizing High Performing MOFs for Pre-Combustion Carbon Capture and Using Molecular Simulation to Study Adsorption of Water and Gases in Novel MOFs". Thesis, Université d'Ottawa / University of Ottawa, 2018. http://hdl.handle.net/10393/37288.
Pełny tekst źródłaHolm, Henrik. "Bidirectional Encoder Representations from Transformers (BERT) for Question Answering in the Telecom Domain. : Adapting a BERT-like language model to the telecom domain using the ELECTRA pre-training approach". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-301313.
Pełny tekst źródłaDubbelriktade språkmodeller som BERT har på senare år nått stora framgångar inom språkteknologiområdet. Flertalet vidareutvecklingar av BERT har tagits fram, bland andra ELECTRA, vars nyskapande diskriminativa träningsprocess förkortar träningstiden. Majoriteten av forskningen inom området utförs på data från den allmänna domänen. Med andra ord finns det utrymme för kunskapsbildning inom domäner med områdesspecifikt språk. I detta arbete utforskas metoder för att anpassa en dubbelriktad språkmodell till telekomdomänen. För att säkerställa hög effektivitet i förträningsstadiet används ELECTRA-modellen. Uppnådd prestanda i måldomänen mäts med hjälp av ett frågebesvaringsdataset för telekom-området. Tre metoder för domänanpassning undersöks: (1) fortsatt förträning på text från telekom-området av en modell förtränad på den allmänna domänen; (2) förträning från grunden på telekom-text; samt (3) förträning från grunden på en kombination av text från telekom-området och den allmänna domänen. Experimenten visar att metod 1 är både kostnadseffektiv och fördelaktig ur ett prestanda-perspektiv. Redan efter kort fortsatt förträning kan tydliga förbättringar inom frågebesvaring inom måldomänen urskiljas, samtidigt som generaliserbarhet kvarhålls. Tillvägagångssätt 2 uppvisar högst prestanda inom måldomänen, om än med markant sämre förmåga att generalisera. Metod 3 kombinerar fördelarna från de tidigare två metoderna genom hög prestanda dels inom måldomänen, dels inom den allmänna domänen. Samtidigt tillåter metoden användandet av ett tokenizer-vokabulär väl anpassat för båda domäner. Sammanfattningsvis bestäms en domänanpassningsmetods lämplighet av den respektive situationen och datan som tillhandahålls, samt de tillgängliga beräkningsresurserna. Resultaten påvisar de tydliga vinningar som domänanpassning kan ge upphov till, även då frågebesvaringsuppgiften lärs genom träning på ett dataset hämtat ur den allmänna domänen på grund av otillräckliga mängder frågebesvaringsdata inom måldomänen.
Bjöörn, Anton. "Employing a Transformer Language Model for Information Retrieval and Document Classification : Using OpenAI's generative pre-trained transformer, GPT-2". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-281766.
Pełny tekst źródłaInformationsflödet på Internet fortsätter att öka vilket gör det allt lättare att missa viktiga nyheter som inte intresserar en stor mängd människor. För att bekämpa detta problem behövs allt mer sofistikerade informationssökningsmetoder. Förtränade transformermodeller har sedan ett par år tillbaka tagit över som de mest framstående neurala nätverken för att hantera text. Det här arbetet undersöker hur väl en sådan språkmodell, Open AIs General Pre-trained Transformer 2 (GPT-2), kan generalisera från att generera text till att användas för informationssökning och klassificering av texter. För att utvärdera detta jämförs en transformerbaserad modell med en mer traditionell Term Frequency- Inverse Document Frequency (TF-IDF) vektoriseringsmodell. Målet är att klargöra hur användbara förtränade transformermodeller faktiskt är i skapandet av specialiserade informationssökningssystem. Den minsta versionen av språkmodellen GPT-2 anpassas och tränas om till att ranka och klassificera nyhetsartiklar, skrivna på engelska, och uppnår liknande prestanda som den TF-IDF baserade modellen. Den GPT-2 baserade modellen hade i genomsnitt 0.74 procentenheter högre Normalized Discounted Cumulative Gain (NDCG) men provstorleken var ej stor nog för att ge dessa resultat hög statistisk säkerhet.
Gustafson, Nathaniel Lee. "A Confidence-Prioritization Approach to Data Processing in Noisy Data Sets and Resulting Estimation Models for Predicting Streamflow Diel Signals in the Pacific Northwest". BYU ScholarsArchive, 2012. https://scholarsarchive.byu.edu/etd/3294.
Pełny tekst źródłaBokosmaty, Rena. "Student learning experiences with the online component of a partially flipped teaching model". Thesis, The University of Sydney, 2022. https://hdl.handle.net/2123/29916.
Pełny tekst źródłaNivens, Ryan Andrew, i Renée Rice Moran. "Beyond Problem-Based Learning: How a Residency Model Improves the Education of Pre-Service Teachers". Digital Commons @ East Tennessee State University, 2015. https://dc.etsu.edu/etsu-works/221.
Pełny tekst źródłaKsiążki na temat "Learning with pre-built models"
Learning through the built environment: An ecological approach to child development. New York: Irvington Pub., 1985.
Znajdź pełny tekst źródłaPolyakova, Anna, Tat'yana Sergeeva i Irina Kitaeva. The continuous formation of the stochastic culture of schoolchildren in the context of the digital transformation of general education. ru: INFRA-M Academic Publishing LLC., 2022. http://dx.doi.org/10.12737/1876368.
Pełny tekst źródłaRE, Marena Simmons Jones, i Maurice Geddis MA AMFT/APCC. Cllaimm© Model: Optimal Results Maximizing True Potential for Student Learning Preschool Through Pre-Adolesent. Author Solutions, LLC, 2022.
Znajdź pełny tekst źródłaRE, Marena Simmons Jones, i Maurice Geddis MA AMFT/APCC. Cllaimm© Model: Optimal Results Maximizing True Potential for Student Learning Preschool Through Pre-Adolesent. Author Solutions, LLC, 2022.
Znajdź pełny tekst źródłaRegelski, Thomas A. Curriculum Philosophy and Theory for Music Education Praxis. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780197558690.001.0001.
Pełny tekst źródłaSchuldberg, David, Ruth Richards i Shan Guisinger, red. Chaos and Nonlinear Psychology. Oxford University Press, 2022. http://dx.doi.org/10.1093/oso/9780190465025.001.0001.
Pełny tekst źródłaKoyama, Dennis, red. Development of Innovative Pedagogical Practices for a Modern Learning Experience. CSMFL Publications, 2021. http://dx.doi.org/10.46679/9788194848363.
Pełny tekst źródłaCole, Ester, i Maria Kokai, red. Consultation and Mental Health Interventions in School Settings. Hogrefe Publishing, 2021. http://dx.doi.org/10.1027/00583-000.
Pełny tekst źródłaKissane, David W., Barry D. Bultz, Phyllis N. Butow, Carma L. Bylund, Simon Noble i Susie Wilkinson, red. Oxford Textbook of Communication in Oncology and Palliative Care. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780198736134.001.0001.
Pełny tekst źródłaJanssen, Markus, i Thomas Wiedenhorn, red. School adoption in teacher education. Waxmann Verlag GmbH, 2020. http://dx.doi.org/10.31244/9783830992639.
Pełny tekst źródłaCzęści książek na temat "Learning with pre-built models"
Kamath, Sanjay, Brigitte Grau i Yue Ma. "How to Pre-train Your Model? Comparison of Different Pre-training Models for Biomedical Question Answering". W Machine Learning and Knowledge Discovery in Databases, 646–60. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43887-6_58.
Pełny tekst źródłaSeshu Babu, G., T. K. Sachin Saj, V. Sowmya i K. P. Soman. "Tuberculosis Classification Using Pre-trained Deep Learning Models". W Advances in Automation, Signal Processing, Instrumentation, and Control, 767–74. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8221-9_71.
Pełny tekst źródłaZhang, Luxin, Pascal Germain, Yacine Kessaci i Christophe Biernacki. "Target to Source Coordinate-Wise Adaptation of Pre-trained Models". W Machine Learning and Knowledge Discovery in Databases, 378–94. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67658-2_22.
Pełny tekst źródłaWildgans-Lang, Angelika, Sarah Scheuerer, Andreas Obersteiner, Frank Fischer i Kristina Reiss. "Learning to Diagnose Primary Students’ Mathematical Competence Levels and Misconceptions in Document-Based Simulations". W Learning to Diagnose with Simulations, 17–31. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89147-3_3.
Pełny tekst źródłaCarneiro, Gustavo, Jacinto Nascimento i Andrew P. Bradley. "Unregistered Multiview Mammogram Analysis with Pre-trained Deep Learning Models". W Lecture Notes in Computer Science, 652–60. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24574-4_78.
Pełny tekst źródłaWu, Yuxuan, Ding Wang, Yunkai Zou i Ziyi Huang. "Improving Deep Learning Based Password Guessing Models Using Pre-processing". W Information and Communications Security, 163–83. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-15777-6_10.
Pełny tekst źródłaLi, Bangqi, Boci Peng, Yifeng Shao i Zhichun Wang. "Prerequisite Learning with Pre-trained Language and Graph Embedding Models". W Natural Language Processing and Chinese Computing, 98–108. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88483-3_8.
Pełny tekst źródłavan Kampen, Paul. "A Workshop Approach to Pre-service Physics Teacher Education". W Concepts, Strategies and Models to Enhance Physics Teaching and Learning, 171–80. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18137-6_15.
Pełny tekst źródłaHire, Dnyanda N., i A. V. Patil. "Pre-trained deep learning models for content-based image classification and retrieval". W Application of Communication Computational Intelligence and Learning, 114–26. London: Routledge, 2022. http://dx.doi.org/10.1201/9781003340867-12.
Pełny tekst źródłaYang, Guanqun, Shay Dineen, Zhipeng Lin i Xueqing Liu. "Few-Sample Named Entity Recognition for Security Vulnerability Reports by Fine-Tuning Pre-trained Language Models". W Deployable Machine Learning for Security Defense, 55–78. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87839-9_3.
Pełny tekst źródłaStreszczenia konferencji na temat "Learning with pre-built models"
Wu, Tz-Ying, Gurumurthy Swaminathan, Zhizhong Li, Avinash Ravichandran, Nuno Vasconcelos, Rahul Bhotika i Stefano Soatto. "Class-Incremental Learning with Strong Pre-trained Models". W 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2022. http://dx.doi.org/10.1109/cvpr52688.2022.00938.
Pełny tekst źródłaAtanassov, Atanas, i Dimitar Pilev. "Pre-trained Deep Learning Models for Facial Emotions Recognition". W 2020 International Conference Automatics and Informatics (ICAI). IEEE, 2020. http://dx.doi.org/10.1109/icai50593.2020.9311334.
Pełny tekst źródłaWang, Chengyu, Haojie Pan, Minghui Qiu, Jun Huang, Fei Yang i Yin Zhang. "Meta Distant Transfer Learning for Pre-trained Language Models". W Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA, USA: Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.emnlp-main.768.
Pełny tekst źródłaLee, Kuan-Ying, Yuanyi Zhong i Yu-Xiong Wang. "Do Pre-trained Models Benefit Equally in Continual Learning?" W 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). IEEE, 2023. http://dx.doi.org/10.1109/wacv56688.2023.00642.
Pełny tekst źródłaMarquez, Freddy J. "Drilling Optimization Applying Machine Learning Regression Algorithms". W Offshore Technology Conference. OTC, 2021. http://dx.doi.org/10.4043/30934-ms.
Pełny tekst źródłaZarzuelo Romero, Carmen, Alejandro López-Ruiz, Manuel Díez-Minguito, Antonio Moñino, Pedro Magaña i Miguel Ortega-Sánchez. "INTRODUCING GRADUATE STUDENTS INTO PRE-PROCESSING TECHNIQUES FOR ADVANCED NUMERICAL MODELS: APPLICATION TO HYDRODYNAMIC MODELS". W International Conference on Education and New Learning Technologies. IATED, 2016. http://dx.doi.org/10.21125/edulearn.2016.0285.
Pełny tekst źródłaAlshalali, Tagrid, i Darsana Josyula. "Fine-Tuning of Pre-Trained Deep Learning Models with Extreme Learning Machine". W 2018 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 2018. http://dx.doi.org/10.1109/csci46756.2018.00096.
Pełny tekst źródłaIyer, Vasanth, Alex J. Aved, Todd B. Howlett, Jeffrey T. Carlo, Asif Mehmood, Niki Pissniou i S. Sitharama Iyengar. "Fast multi-modal reuse: co-occurrence pre-trained deep learning models". W Real-Time Image Processing and Deep Learning 2019, redaktorzy Nasser Kehtarnavaz i Matthias F. Carlsohn. SPIE, 2019. http://dx.doi.org/10.1117/12.2519546.
Pełny tekst źródłaLiu, Zihan, Genta Indra Winata, Andrea Madotto i Pascale Fung. "Preserving Cross-Linguality of Pre-trained Models via Continual Learning". W Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021). Stroudsburg, PA, USA: Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.repl4nlp-1.8.
Pełny tekst źródłaOstellino, S., A. Benso i G. Politano. "Brain MRI Images Pre-processing of Heterogeneous Data-sets for Deep Learning Applications". W 13th International Conference on Bioinformatics Models, Methods and Algorithms. SCITEPRESS - Science and Technology Publications, 2022. http://dx.doi.org/10.5220/0010828500003123.
Pełny tekst źródłaRaporty organizacyjne na temat "Learning with pre-built models"
Lasko, Kristofer, i Elena Sava. Semi-automated land cover mapping using an ensemble of support vector machines with moderate resolution imagery integrated into a custom decision support tool. Engineer Research and Development Center (U.S.), listopad 2021. http://dx.doi.org/10.21079/11681/42402.
Pełny tekst źródłaCordero, Eugene, i Kiana Luong. Promoting Interest in Transportation Careers Among Young Women. Mineta Transportation Institute, listopad 2021. http://dx.doi.org/10.31979/mti.2021.2028.
Pełny tekst źródłaPerdigão, Rui A. P. Information physics and quantum space technologies for natural hazard sensing, modelling and prediction. Meteoceanics, wrzesień 2021. http://dx.doi.org/10.46337/210930.
Pełny tekst źródłaFilmer, Deon, James Habyarimana i Shwetlena Sabarwal. Teacher Performance-Based Incentives and Learning Inequality. Research on Improving Systems of Education (RISE), wrzesień 2020. http://dx.doi.org/10.35489/bsg-rise-wp_2020/047.
Pełny tekst źródłaTayeb, Shahab. Taming the Data in the Internet of Vehicles. Mineta Transportation Institute, styczeń 2022. http://dx.doi.org/10.31979/mti.2022.2014.
Pełny tekst źródłaStrutynska, Oksana V., Grygoriy M. Torbin, Mariia A. Umryk i Roman M. Vernydub. Digitalization of the educational process for the training of the pre-service teachers. [б. в.], czerwiec 2021. http://dx.doi.org/10.31812/123456789/4437.
Pełny tekst źródłaKriegel, Francesco. Learning description logic axioms from discrete probability distributions over description graphs (Extended Version). Technische Universität Dresden, 2018. http://dx.doi.org/10.25368/2022.247.
Pełny tekst źródłaVelychko, Vladyslav Ye, Elena H. Fedorenko i Darja A. Kassim. Conceptual Bases of Use of Free Software in the Professional Training of Pre-Service Teacher of Mathematics, Physics and Computer Science. [б. в.], listopad 2018. http://dx.doi.org/10.31812/123456789/2667.
Pełny tekst źródłaKharkivska, Alla A., Liudmyla V. Shtefan, Muntasir Alsadoon i Aleksandr D. Uchitel. Technology of forming future journalists' social information competence in Iraq based on the use of a dynamic pedagogical site. [б. в.], lipiec 2020. http://dx.doi.org/10.31812/123456789/3853.
Pełny tekst źródłaTyshchenko, Yelyzaveta Yu, i Andrii M. Striuk. Актуальність розробки моделі адаптивного навчання. [б. в.], grudzień 2018. http://dx.doi.org/10.31812/123456789/2889.
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