Academic literature on the topic 'Learning with pre-built models'
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Journal articles on the topic "Learning with pre-built models"
Fauszt, Tibor, László Bognár, and Ágnes Sándor. "Increasing the Prediction Power of Moodle Machine Learning Models with Self-defined Indicators." International Journal of Emerging Technologies in Learning (iJET) 16, no. 24 (December 21, 2021): 23–39. http://dx.doi.org/10.3991/ijet.v16i24.23923.
Full textErian, Karim H., Pedro H. Regalado, and James M. Conrad. "Missing data handling for machine learning models." IAES International Journal of Robotics and Automation (IJRA) 10, no. 2 (June 1, 2021): 123. http://dx.doi.org/10.11591/ijra.v10i2.pp123-132.
Full textCinar, Eyup. "A Sensor Fusion Method Using Transfer Learning Models for Equipment Condition Monitoring." Sensors 22, no. 18 (September 8, 2022): 6791. http://dx.doi.org/10.3390/s22186791.
Full textLage, Isaac, Emily Chen, Jeffrey He, Menaka Narayanan, Been Kim, Samuel J. Gershman, and Finale Doshi-Velez. "Human Evaluation of Models Built for Interpretability." Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 7 (October 28, 2019): 59–67. http://dx.doi.org/10.1609/hcomp.v7i1.5280.
Full textYimam, Seid Muhie, Abinew Ali Ayele, Gopalakrishnan Venkatesh, Ibrahim Gashaw, and Chris Biemann. "Introducing Various Semantic Models for Amharic: Experimentation and Evaluation with Multiple Tasks and Datasets." Future Internet 13, no. 11 (October 27, 2021): 275. http://dx.doi.org/10.3390/fi13110275.
Full textWang, Chengyu, Mengli Cheng, Xu Hu, and Jun Huang. "EasyASR: A Distributed Machine Learning Platform for End-to-end Automatic Speech Recognition." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 18 (May 18, 2021): 16111–13. http://dx.doi.org/10.1609/aaai.v35i18.18028.
Full textSingh, Hrithik, Shambhavi Kaushik, Shruti Talyan, and Kartikeya Dwivedi. "Skin Cancer Detection Using Deep Learning techniques." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 4296–305. http://dx.doi.org/10.22214/ijraset.2022.43090.
Full textPedditzi, Maria L., and Marcello Nonnis. "Pre-service Teachers' Representations About Children's Learning: A Pilot Study." Open Psychology Journal 13, no. 1 (November 13, 2020): 315–20. http://dx.doi.org/10.2174/1874350102013010315.
Full textZhang, Li, Haimeng Fan, Chengxia Peng, Guozheng Rao, and Qing Cong. "Sentiment Analysis Methods for HPV Vaccines Related Tweets Based on Transfer Learning." Healthcare 8, no. 3 (August 28, 2020): 307. http://dx.doi.org/10.3390/healthcare8030307.
Full textAchicanoy, Harold, Deisy Chaves, and Maria Trujillo. "StyleGANs and Transfer Learning for Generating Synthetic Images in Industrial Applications." Symmetry 13, no. 8 (August 16, 2021): 1497. http://dx.doi.org/10.3390/sym13081497.
Full textDissertations / Theses on the topic "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.
Full textEnvironmental 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.
Full textBitter, 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.
Full textJahanshahi, 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.
Full textDureckova, 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.
Full textHolm, 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.
Full textDubbelriktade 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.
Full textInformationsflödet 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.
Full textBokosmaty, 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.
Full textNivens, Ryan Andrew, and 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.
Full textBooks on the topic "Learning with pre-built models"
Learning through the built environment: An ecological approach to child development. New York: Irvington Pub., 1985.
Find full textPolyakova, Anna, Tat'yana Sergeeva, and 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.
Full textRE, Marena Simmons Jones, and Maurice Geddis MA AMFT/APCC. Cllaimm© Model: Optimal Results Maximizing True Potential for Student Learning Preschool Through Pre-Adolesent. Author Solutions, LLC, 2022.
Find full textRE, Marena Simmons Jones, and Maurice Geddis MA AMFT/APCC. Cllaimm© Model: Optimal Results Maximizing True Potential for Student Learning Preschool Through Pre-Adolesent. Author Solutions, LLC, 2022.
Find full textRegelski, Thomas A. Curriculum Philosophy and Theory for Music Education Praxis. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780197558690.001.0001.
Full textSchuldberg, David, Ruth Richards, and Shan Guisinger, eds. Chaos and Nonlinear Psychology. Oxford University Press, 2022. http://dx.doi.org/10.1093/oso/9780190465025.001.0001.
Full textKoyama, Dennis, ed. Development of Innovative Pedagogical Practices for a Modern Learning Experience. CSMFL Publications, 2021. http://dx.doi.org/10.46679/9788194848363.
Full textCole, Ester, and Maria Kokai, eds. Consultation and Mental Health Interventions in School Settings. Hogrefe Publishing, 2021. http://dx.doi.org/10.1027/00583-000.
Full textKissane, David W., Barry D. Bultz, Phyllis N. Butow, Carma L. Bylund, Simon Noble, and Susie Wilkinson, eds. Oxford Textbook of Communication in Oncology and Palliative Care. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780198736134.001.0001.
Full textJanssen, Markus, and Thomas Wiedenhorn, eds. School adoption in teacher education. Waxmann Verlag GmbH, 2020. http://dx.doi.org/10.31244/9783830992639.
Full textBook chapters on the topic "Learning with pre-built models"
Kamath, Sanjay, Brigitte Grau, and Yue Ma. "How to Pre-train Your Model? Comparison of Different Pre-training Models for Biomedical Question Answering." In 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.
Full textSeshu Babu, G., T. K. Sachin Saj, V. Sowmya, and K. P. Soman. "Tuberculosis Classification Using Pre-trained Deep Learning Models." In 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.
Full textZhang, Luxin, Pascal Germain, Yacine Kessaci, and Christophe Biernacki. "Target to Source Coordinate-Wise Adaptation of Pre-trained Models." In 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.
Full textWildgans-Lang, Angelika, Sarah Scheuerer, Andreas Obersteiner, Frank Fischer, and Kristina Reiss. "Learning to Diagnose Primary Students’ Mathematical Competence Levels and Misconceptions in Document-Based Simulations." In Learning to Diagnose with Simulations, 17–31. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89147-3_3.
Full textCarneiro, Gustavo, Jacinto Nascimento, and Andrew P. Bradley. "Unregistered Multiview Mammogram Analysis with Pre-trained Deep Learning Models." In Lecture Notes in Computer Science, 652–60. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24574-4_78.
Full textWu, Yuxuan, Ding Wang, Yunkai Zou, and Ziyi Huang. "Improving Deep Learning Based Password Guessing Models Using Pre-processing." In Information and Communications Security, 163–83. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-15777-6_10.
Full textLi, Bangqi, Boci Peng, Yifeng Shao, and Zhichun Wang. "Prerequisite Learning with Pre-trained Language and Graph Embedding Models." In 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.
Full textvan Kampen, Paul. "A Workshop Approach to Pre-service Physics Teacher Education." In 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.
Full textHire, Dnyanda N., and A. V. Patil. "Pre-trained deep learning models for content-based image classification and retrieval." In Application of Communication Computational Intelligence and Learning, 114–26. London: Routledge, 2022. http://dx.doi.org/10.1201/9781003340867-12.
Full textYang, Guanqun, Shay Dineen, Zhipeng Lin, and Xueqing Liu. "Few-Sample Named Entity Recognition for Security Vulnerability Reports by Fine-Tuning Pre-trained Language Models." In 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.
Full textConference papers on the topic "Learning with pre-built models"
Wu, Tz-Ying, Gurumurthy Swaminathan, Zhizhong Li, Avinash Ravichandran, Nuno Vasconcelos, Rahul Bhotika, and Stefano Soatto. "Class-Incremental Learning with Strong Pre-trained Models." In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2022. http://dx.doi.org/10.1109/cvpr52688.2022.00938.
Full textAtanassov, Atanas, and Dimitar Pilev. "Pre-trained Deep Learning Models for Facial Emotions Recognition." In 2020 International Conference Automatics and Informatics (ICAI). IEEE, 2020. http://dx.doi.org/10.1109/icai50593.2020.9311334.
Full textWang, Chengyu, Haojie Pan, Minghui Qiu, Jun Huang, Fei Yang, and Yin Zhang. "Meta Distant Transfer Learning for Pre-trained Language Models." In 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.
Full textLee, Kuan-Ying, Yuanyi Zhong, and Yu-Xiong Wang. "Do Pre-trained Models Benefit Equally in Continual Learning?" In 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). IEEE, 2023. http://dx.doi.org/10.1109/wacv56688.2023.00642.
Full textMarquez, Freddy J. "Drilling Optimization Applying Machine Learning Regression Algorithms." In Offshore Technology Conference. OTC, 2021. http://dx.doi.org/10.4043/30934-ms.
Full textZarzuelo Romero, Carmen, Alejandro López-Ruiz, Manuel Díez-Minguito, Antonio Moñino, Pedro Magaña, and Miguel Ortega-Sánchez. "INTRODUCING GRADUATE STUDENTS INTO PRE-PROCESSING TECHNIQUES FOR ADVANCED NUMERICAL MODELS: APPLICATION TO HYDRODYNAMIC MODELS." In International Conference on Education and New Learning Technologies. IATED, 2016. http://dx.doi.org/10.21125/edulearn.2016.0285.
Full textAlshalali, Tagrid, and Darsana Josyula. "Fine-Tuning of Pre-Trained Deep Learning Models with Extreme Learning Machine." In 2018 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 2018. http://dx.doi.org/10.1109/csci46756.2018.00096.
Full textIyer, Vasanth, Alex J. Aved, Todd B. Howlett, Jeffrey T. Carlo, Asif Mehmood, Niki Pissniou, and S. Sitharama Iyengar. "Fast multi-modal reuse: co-occurrence pre-trained deep learning models." In Real-Time Image Processing and Deep Learning 2019, edited by Nasser Kehtarnavaz and Matthias F. Carlsohn. SPIE, 2019. http://dx.doi.org/10.1117/12.2519546.
Full textLiu, Zihan, Genta Indra Winata, Andrea Madotto, and Pascale Fung. "Preserving Cross-Linguality of Pre-trained Models via Continual Learning." In 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.
Full textOstellino, S., A. Benso, and G. Politano. "Brain MRI Images Pre-processing of Heterogeneous Data-sets for Deep Learning Applications." In 13th International Conference on Bioinformatics Models, Methods and Algorithms. SCITEPRESS - Science and Technology Publications, 2022. http://dx.doi.org/10.5220/0010828500003123.
Full textReports on the topic "Learning with pre-built models"
Lasko, Kristofer, and 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.), November 2021. http://dx.doi.org/10.21079/11681/42402.
Full textCordero, Eugene, and Kiana Luong. Promoting Interest in Transportation Careers Among Young Women. Mineta Transportation Institute, November 2021. http://dx.doi.org/10.31979/mti.2021.2028.
Full textPerdigão, Rui A. P. Information physics and quantum space technologies for natural hazard sensing, modelling and prediction. Meteoceanics, September 2021. http://dx.doi.org/10.46337/210930.
Full textFilmer, Deon, James Habyarimana, and Shwetlena Sabarwal. Teacher Performance-Based Incentives and Learning Inequality. Research on Improving Systems of Education (RISE), September 2020. http://dx.doi.org/10.35489/bsg-rise-wp_2020/047.
Full textTayeb, Shahab. Taming the Data in the Internet of Vehicles. Mineta Transportation Institute, January 2022. http://dx.doi.org/10.31979/mti.2022.2014.
Full textStrutynska, Oksana V., Grygoriy M. Torbin, Mariia A. Umryk, and Roman M. Vernydub. Digitalization of the educational process for the training of the pre-service teachers. [б. в.], June 2021. http://dx.doi.org/10.31812/123456789/4437.
Full textKriegel, 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.
Full textVelychko, Vladyslav Ye, Elena H. Fedorenko, and 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.], November 2018. http://dx.doi.org/10.31812/123456789/2667.
Full textKharkivska, Alla A., Liudmyla V. Shtefan, Muntasir Alsadoon, and Aleksandr D. Uchitel. Technology of forming future journalists' social information competence in Iraq based on the use of a dynamic pedagogical site. [б. в.], July 2020. http://dx.doi.org/10.31812/123456789/3853.
Full textTyshchenko, Yelyzaveta Yu, and Andrii M. Striuk. Актуальність розробки моделі адаптивного навчання. [б. в.], December 2018. http://dx.doi.org/10.31812/123456789/2889.
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