Relevant bibliographies by topics / AutoDL

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'AutoDL'

Author: Grafiati
Published: 9 March 2023
Last updated: 31 July 2025

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Journal articles on the topic "AutoDL"

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Bang, Chang Seok, Hyun Lim, Hae Min Jeong, and Sung Hyeon Hwang. "Use of Endoscopic Images in the Prediction of Submucosal Invasion of Gastric Neoplasms: Automated Deep Learning Model Development and Usability Study." Journal of Medical Internet Research 23, no. 4 (2021): e25167. http://dx.doi.org/10.2196/25167.

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Background In a previous study, we examined the use of deep learning models to classify the invasion depth (mucosa-confined versus submucosa-invaded) of gastric neoplasms using endoscopic images. The external test accuracy reached 77.3%. However, model establishment is labor intense, requiring high performance. Automated deep learning (AutoDL) models, which enable fast searching of optimal neural architectures and hyperparameters without complex coding, have been developed. Objective The objective of this study was to establish AutoDL models to classify the invasion depth of gastric neoplasms.
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Chen, Yi-Wei, Qingquan Song, and Xia Hu. "Techniques for Automated Machine Learning." ACM SIGKDD Explorations Newsletter 22, no. 2 (2021): 35–50. http://dx.doi.org/10.1145/3447556.3447567.

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Automated machine learning (AutoML) aims to find optimal machine learning solutions automatically given a problem description, its task type, and datasets. It could release the burden of data scientists from the multifarious manual tuning process and enable the access of domain experts to the off-the-shelf machine learning solutions without extensive experience. In this paper, we portray AutoML as a bi-level optimization problem, where one problem is nested within another to search the optimum in the search space, and review the current developments of AutoML in terms of three categories, auto
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Chien, Ching-Syuan. "AutoDL-based convolutional neural networks for wildfire detection." Applied and Computational Engineering 18, no. 1 (2023): 134–42. http://dx.doi.org/10.54254/2755-2721/18/20230978.

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Wildfires have emerged as a pressing issue in many regions of the world due to the ongoing impact of global warming on the planet. However, a reliable and high-performance detection system is currently lacking. This study strives to introduce a wildfire detection system that is based on neural networks and image recognition. This study utilized Edge Impulse to train a neural network to identify wildfire occurrences in the given pictures. To optimize the performance and adaptability of the model, an extensive dataset was compiled by collating images from two Kaggle projects, resulting in a fina
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Tuggener, Lukas, Mohammadreza Amirian, Fernando Benites, et al. "Design Patterns for Resource-Constrained Automated Deep-Learning Methods." AI 1, no. 4 (2020): 510–38. http://dx.doi.org/10.3390/ai1040031.

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We present an extensive evaluation of a wide variety of promising design patterns for automated deep-learning (AutoDL) methods, organized according to the problem categories of the 2019 AutoDL challenges, which set the task of optimizing both model accuracy and search efficiency under tight time and computing constraints. We propose structured empirical evaluations as the most promising avenue to obtain design principles for deep-learning systems due to the absence of strong theoretical support. From these evaluations, we distill relevant patterns which give rise to neural network design recom
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Zimmer, Lucas, Marius Lindauer, and Frank Hutter. "Auto-Pytorch: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL." IEEE Transactions on Pattern Analysis and Machine Intelligence 43, no. 9 (2021): 3079–90. http://dx.doi.org/10.1109/tpami.2021.3067763.

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Liu, Zhengying, Adrien Pavao, Zhen Xu, et al. "Winning Solutions and Post-Challenge Analyses of the ChaLearn AutoDL Challenge 2019." IEEE Transactions on Pattern Analysis and Machine Intelligence 43, no. 9 (2021): 3108–25. http://dx.doi.org/10.1109/tpami.2021.3075372.

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Preuveneers, Davy. "AutoFL: Towards AutoML in a Federated Learning Context." Applied Sciences 13, no. 14 (2023): 8019. http://dx.doi.org/10.3390/app13148019.

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Federated learning (FL) is a decentralized machine learning (ML) technique that learns from distributed data by moving the training process from a centralized server towards many clients rather than centralizing the client data, as is common with classical machine learning. The recent literature on federated learning often focuses on domain-specific use cases (e.g., IoT), investigates various privacy concerns (e.g., membership inference), or analyzes the impact of adversarial attacks (e.g., poisoning) and possible countermeasures. In these works, it is common for the server to have already cho
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Wang, Wenbo, and Chengyou Lei. "Training a Minesweeper Agent Using a Convolutional Neural Network." Applied Sciences 15, no. 5 (2025): 2490. https://doi.org/10.3390/app15052490.

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The Minesweeper game is modeled as a sequential decision-making task, for which a neural network architecture, state encoding, and reward function were herein designed. Both a Deep Q-Network (DQN) and supervised learning methods were successfully applied to optimize the training of the game. The experiments were conducted on the AutoDL platform using an NVIDIA RTX 3090 GPU for efficient computation. The results showed that in a 6 × 6 grid with four mines, the DQN model achieved an average win rate of 93.3% (standard deviation: 0.77%), while the supervised learning method achieved 91.2% (standa
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Chen, Xu, and Brett Wujek. "AutoDAL: Distributed Active Learning with Automatic Hyperparameter Selection." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 3537–44. http://dx.doi.org/10.1609/aaai.v34i04.5759.

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Automated machine learning (AutoML) strives to establish an appropriate machine learning model for any dataset automatically with minimal human intervention. Although extensive research has been conducted on AutoML, most of it has focused on supervised learning. Research of automated semi-supervised learning and active learning algorithms is still limited. Implementation becomes more challenging when the algorithm is designed for a distributed computing environment. With this as motivation, we propose a novel automated learning system for distributed active learning (AutoDAL) to address these
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Parker-Holder, Jack, Raghu Rajan, Xingyou Song, et al. "Automated Reinforcement Learning (AutoRL): A Survey and Open Problems." Journal of Artificial Intelligence Research 74 (June 1, 2022): 517–68. http://dx.doi.org/10.1613/jair.1.13596.

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The combination of Reinforcement Learning (RL) with deep learning has led to a series of impressive feats, with many believing (deep) RL provides a path towards generally capable agents. However, the success of RL agents is often highly sensitive to design choices in the training process, which may require tedious and error-prone manual tuning. This makes it challenging to use RL for new problems and also limits its full potential. In many other areas of machine learning, AutoML has shown that it is possible to automate such design choices, and AutoML has also yielded promising initial results
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Dissertations / Theses on the topic "AutoDL"

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PEREGO, RICCARDO. "Automated Deep Learning through Constrained Bayesian Optimization." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2021. http://hdl.handle.net/10281/314922.

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In un mondo sempre più tecnologico e interconnesso, la quantità di dati è in continua crescita e, di conseguenza, anche gli algoritmi di decision-making sono in continua evoluzione per adattarsi ad essi. Una delle principali fonti di questa grande quantità di dati è l'Internet of Things, in cui miliardi di sensori si scambiano informazioni attraverso la rete per svolgere vari tipi di attività come il monitoraggio industriale e medico. Negli ultimi anni lo sviluppo tecnologico ha permesso di definire nuove architetture hardware ad alte prestazioni per i sensori, detti microcontrollori, che hann
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Forien, Nicolas. "Autour de la criticité auto-organisée." Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASM012.

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De nombreux modèles physiques présentent un phénomène appelé transition de phase : il existe un point critique ou une courbe critique dans l'espace des paramètres qui sépare deux régimes distincts caractérisés par des propriétés macroscopiques très différentes. Le comportement de ces systèmes au point critique est particulièrement intéressant et fait apparaître des lois d'échelle qui sont souvent communes à tout un ensemble de systèmes très différents. Les physiciens Per Bak, Chao Tang et Kurt Wiesenfeld ont remarqué que ces comportements "critiques" sont étonnamment communs dans la nature, al
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Pietracola, Annamaria. "Une rencontre autour des blessures auto-infligées à l'infirmerie scolaire." Poitiers, 2011. http://www.theses.fr/2011POIT5015.

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Ces dernières années, des recherches montrent que la souffrance psychique à l’adolescence s’exprime au travers d’un mal-être qui touche le corps sous la forme de blessures auto-infligées. Un grand nombre de jeunes ne s’adresse pas tout de suite à des services hospitaliers, ou à des professionnels de santé. Ils préfèrent rester longtemps dans l’ombre. En dépit de cela, nous avons constaté une importante augmentation de jeunes qui se rendent à l’infirmerie scolaire. A partir de l’hypothèse de l’inconscient métapsychologique, peut-on supposer que, pour les adolescents qui s’y rendent, l’infirmeri
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Pimentel, Victor Costa de Andrade. "Plataforma embarcada de reconhecimento autom?tico da fala para o aux?lio de pessoas com mobilidade reduzida." reponame:Repositório Institucional do IFPB, 2016. http://repositorio.ifpb.edu.br/jspui/handle/177683/236.

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Submitted by Alex Sandro R?go (alex@ifpb.edu.br) on 2016-09-01T13:06:08Z No. of bitstreams: 1 Plataforma embarcada de reconhecimento automatico de fala_Victor Pimentel_DissertacaoFINAL.pdf: 7822088 bytes, checksum: c2489aba1b2861913d7d09e208e3798b (MD5)<br>Approved for entry into archive by Alex Sandro R?go (alex@ifpb.edu.br) on 2016-09-01T13:08:01Z (GMT) No. of bitstreams: 1 Plataforma embarcada de reconhecimento automatico de fala_Victor Pimentel_DissertacaoFINAL.pdf: 7822088 bytes, checksum: c2489aba1b2861913d7d09e208e3798b (MD5)<br>Made available in DSpace on 2016-09-01T13:08:01Z (GMT). No
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Santos, Sandro Roberto dos. "O direito de autor na obra musical: desequilíbrio do contrato e os novos rumos da proteção autoral." Universidade de São Paulo, 2009. http://www.teses.usp.br/teses/disponiveis/2/2131/tde-04082016-094412/.

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Trabalhando com a linha de pesquisa \"Direito moral e patrimonial de autor\" propõe-se um estudo sobre o Direito de Autor na obra musical, abordando mais especificamente as relações contratuais entre autores e empresas de gravação e distribuição, quais sejam: produtores fonográficos (gravadoras) e editoras musicais. O presente trabalho é dividido em três partes. A primeira trata da propriedade intelectual sobre obras imateriais, sua evolução histórica e os conceitos da doutrina autoralista, substrato intelectual necessário para compreensão do problema de pesquisa: o desequilíbrio contratual e
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Aldor, Edvard, and Daniel Helle. "Interweaving AutoML and Data Science Method." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-302504.

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The advent of automated machine learning (AutoML) tools promises to democratize machine learning to non-experts in the field. These tools certainly effect the process of applying machine learning for non-experts, but it is not clear how data scientists will be able to draw benefit from having access to automated machine learning systems. This bachelor thesis is an inquiry into how an application of one such AutoML system, Google Cloud AutoML, can improve upon commercial state of the art ensemble models. Specifically, inferences about unimportant features were examined and removed from an ensem
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Avilés, Arnillas Alexsandra Susana, Garcia Calderon Renzo David Balbin, Negrón Claudia Alejandra Hurtado, and Boza Ingrid Julieta Salazar. "Proyecto Empresarial Autoke." Bachelor's thesis, Universidad Peruana de Ciencias Aplicadas (UPC), 2017. http://hdl.handle.net/10757/623429.

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Autoke es un aplicativo para Smartphones que contará con un portal web informativo, la propuesta de valor del presente proyecto empresarial implica ofrecer un servicio online completo al automovilista por medio de un App, que le permita rápidamente contactarse con el taller de mecánica más cercano ante una necesidad de servicio. Para esto se ofrecerá a los talleres de mecánica un servicio de membresía que permita promocionar y publicitar sus servicios en el App de la empresa, y publicitar a través de enlaces sus servicios estableciendo conexión con los clientes tan solo al realizar un clic en
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Tetewsky, Avram Ross Jeff Soltz Arnold Vaughn Norman Anszperger Jan O'Brien Chris Graham Dave Craig Doug Lozow Jeff. "Making sense of inter-signal corrections : accounting for GPS satellite calibration parameters in legacy and modernized ionosphere correction algorithms /." [Eugene, Ore. : Gibbons Media & Research], 2009. http://www.insidegnss.com/auto/julyaug09-tetewsky-final.pdf.

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"Author biographies are available in the expanded on-line version of this article [http://www.insidegnss.com/auto/julyaug09-tetewsky-final.pdf]"<br>"July/August 2009." Web site title: Making Sense of GPS Inter-Signal Corrections : Satellite Calibration Parameters in Legacy and Modernized Ionosphere Correction Algorithms.
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Wild, Klaus. "Wahrnehmungsorientierte Schulentwicklung innere Schulentwicklung unter Berücksichtigung der Wahrnehmung von Schulqualität durch Lehrkräfte." Winzer Duschl, 2006. http://www.verlag-duschl.de/detail.php?table=autor&id=92.

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Salomon, Damien. "Croissance, propriétés optiques et intégration d'hétérostructures radiales InGaN/GaN autour de fils auto-assemblés de GaN crûs sur saphir et silicium." Thesis, Grenoble, 2013. http://www.theses.fr/2013GRENY052.

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Ce travail est consacré à la réalisation de diodes électroluminescentes visibles à base de fils de GaN crûs sur Si(111) par épitaxie en phase vapeur de précurseurs organo-métalliques. Nous cherchons en particulier à comprendre les mécanismes de croissance des fils de GaN et les propriétés structurales et optiques de puits quantiques InGaN/GaN cœur/coquille déposés autour de ceux-ci. La croissance de fils orientés le long de l'axe -c sur saphir est dans un premier temps détaillée et expliquée. Nous montrons que l'injection de silane pendant la croissance des fils permet de former une couche de
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Books on the topic "AutoDL"

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Trojanow, Ilija. Autopol. DTV Premium, 1997.

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Accascina, Guido. Automi. Iacobellieditore, 2020.

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Biteus, Jonas. Fault isolation in distributed embedded systems. Linko pings Universitet. Department of Computer and Information Science, 2007.

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Sanches, Hércoles Tecino. Legislação autoral. Editora LTr, 1999.

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Canali, Giuseppina Gagliardi. Carissimo autore--. Edizioni del Gallo cedrone, 1988.

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Dias, Maurício Cozer. Direito autoral. LZN Editora, 2002.

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Cultura, Brazil Ministério da, ed. Direito autoral. República Federativa do Brasil, Ministério da Cultura, 2006.

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Pašić, Milutin. Autori i dela. SVITAK izdavačka radionica, 1995.

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Jannidis, Fotis, Gerhard Lauer, Matias Martinez, and Simone Winko, eds. Rückkehr des Autors. DE GRUYTER, 1999. http://dx.doi.org/10.1515/9783110944754.

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Jüssi, Fred. Jäälõhkuja: Autori fotod. "Valgus", 1986.

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Book chapters on the topic "AutoDL"

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Li, Jiayi, Sheetal Satheesh, Stefan Heindorf, Diego Moussallem, René Speck, and Axel-Cyrille Ngonga Ngomo. "AutoCL: AutoML for Concept Learning." In Communications in Computer and Information Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-63787-2_7.

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Walsh, Barry. "AutoML, AutoAI, and the Rise of NoLo UIs." In Productionizing AI. Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-8817-7_6.

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Agrawal, Tanay. "Optuna and AutoML." In Hyperparameter Optimization in Machine Learning. Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6579-6_5.

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Zender, Alexander, and Bernhard G. Humm. "Ontologie-basiertes AutoML." In Hybride KI mit Machine Learning und Knowledge Graphs. Springer Fachmedien Wiesbaden, 2025. https://doi.org/10.1007/978-3-658-44781-6_8.

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Zusammenfassung Machine Learning (ML) ist in aller Munde als derzeit prominentestes Teilgebiet der künstlichen Intelligenz (KI). Zahlreiche ML-Anwendungen sind im täglichen Einsatz, von digitalen Assistenten über Entscheidungsunterstützungssysteme bis hin zu teilautonomen Fahrzeugen. Auch werden Datenanalysen in zahlreichen Unternehmen ML-basiert durchgeführt. Aber effektive und effiziente ML-Anwendungen zu entwickeln ist höchst anspruchsvoll und erfordert eine große Expertise.
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Gigena, Andrea Ivanna. "Autora." In Afrontar las crisis desde América Latina. Bielefeld University Press / transcript Verlag, 2023. http://dx.doi.org/10.14361/9783839459119-011.

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del Palacio, Celia. "Autora." In Periodismo de frontera en América Latina. Bielefeld University Press / transcript Verlag, 2023. http://dx.doi.org/10.14361/9783839464670-017.

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Viveros Vigoya, Mara. "AUTORA." In El oxímoron de las clases medias negras. Bielefeld University Press / transcript Verlag, 2022. http://dx.doi.org/10.14361/9783839459089-013.

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Jiao, Wencong, Tao Li, Guoqiang Zhong, and Li-Na Wang. "AutoML for DenseNet Compression." In Neural Information Processing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-36718-3_13.

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Bisong, Ekaba. "Google AutoML: Cloud Vision." In Building Machine Learning and Deep Learning Models on Google Cloud Platform. Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-4470-8_42.

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Weber, Robert, and Peter Seeberg. "AutoML und Guided Analytics." In KI in der Industrie. Carl Hanser Verlag GmbH & Co. KG, 2020. http://dx.doi.org/10.3139/9783446464278.003.

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Conference papers on the topic "AutoDL"

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Aslan, Sharon, Gabriela Dorfman Furman, and Oded Koren. "AutoML Anomaly Characterization Method." In 2024 4th International Conference on Advanced Enterprise Information System (AEIS). IEEE, 2024. https://doi.org/10.1109/aeis65978.2024.00018.

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S, Senthil Pandi, Kumar P, Nathaniel Abishek A, and Mohamed Hussain S. "Demand Prediction using AutoML Based Ensemble Algorithm." In 2025 International Conference on Artificial Intelligence and Data Engineering (AIDE). IEEE, 2025. https://doi.org/10.1109/aide64228.2025.10986835.

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de Oliveira, Gabriel Bianchin, Helio Pedrini, and Zanoni Dias. "Integrating Transformers and AutoML for Protein Function Prediction." In 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2024. https://doi.org/10.1109/embc53108.2024.10782139.

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Saber, Mohamed, Natalia Gusarova, and Vladimir Bogatyrev. "HAMI: Hybrid Adaptive AutoML for IoT Intrusion Detection." In 2025 Communication Strategies in Digital Society Seminar (ComSDS). IEEE, 2025. https://doi.org/10.1109/comsds65569.2025.10971316.

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Paiva da Cunha, Roberto, Jairson Rodrigues, and Alexandre Maciel. "Development of a Big Data Mechanism for AutoML." In 27th International Conference on Enterprise Information Systems. SCITEPRESS - Science and Technology Publications, 2025. https://doi.org/10.5220/0013348700003929.

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Gyimah, Nana Kankam, Robert Akinie, Judith Mwakalonge, et al. "An AutoML-Based Approach for Network Intrusion Detection." In SoutheastCon 2025. IEEE, 2025. https://doi.org/10.1109/southeastcon56624.2025.10971461.

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Shankar, Sneha, Sujay Doshi, Bhuvaneshwari R, and Praveen Joe I. R. "AutoDL: Enriching PowerBI AutoML Features with Deep Learning." In 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2023. http://dx.doi.org/10.1109/icccnt56998.2023.10306772.

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Hedhili, Aroua, and Imen Khelfa. "Agent Based Model for AUTODL Optimisation." In 16th International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and Technology Publications, 2024. http://dx.doi.org/10.5220/0012371700003636.

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Davy, N., U. Bin Waheed, A. Koeshidayatullah, and A. El-Husseiny. "Automated Deep Learning (AutoDL) for Facies Prediction: Implementation and Strategy." In 84th EAGE Annual Conference & Exhibition. European Association of Geoscientists & Engineers, 2023. http://dx.doi.org/10.3997/2214-4609.202310455.

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Wang, Yan-Hao, Hao-Ping Tsai, Hong-Yi Chen, Van-Linh Nguyen, and Ren-Hung Hwang. "Efficient AutoDL for Generating Denial-of-Service Defense Models in the Internet of Things." In GLOBECOM 2023 - 2023 IEEE Global Communications Conference. IEEE, 2023. http://dx.doi.org/10.1109/globecom54140.2023.10437653.

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Reports on the topic "AutoDL"

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Qi, Fei, Zhaohui Xia, Gaoyang Tang, et al. A Graph-based Evolutionary Algorithm for Automated Machine Learning. Web of Open Science, 2020. http://dx.doi.org/10.37686/ser.v1i2.77.

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As an emerging field, Automated Machine Learning (AutoML) aims to reduce or eliminate manual operations that require expertise in machine learning. In this paper, a graph-based architecture is employed to represent flexible combinations of ML models, which provides a large searching space compared to tree-based and stacking-based architectures. Based on this, an evolutionary algorithm is proposed to search for the best architecture, where the mutation and heredity operators are the key for architecture evolution. With Bayesian hyper-parameter optimization, the proposed approach can automate th
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Beliz, Gustavo. Seguridad humanizada: 6 transiciones: Más allá de falsas opciones. Inter-American Development Bank, 2005. http://dx.doi.org/10.18235/0007825.

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Esta presentación trata sobre la experiencia argentina en planes de seguridad. El autor elabora una secuencia de seis transiciones: 1) De ¿los leones¿ a la República; 2) De la subgobernabilidad ilegal a la gobernabilidad democrática; 3) Del juego suma-cero a los clusters de seguridad; 4) Del auto-gobierno policial al gobierno transparente de la seguridad; 5) Del combate a ciegas a la mano invisible; y 6) De la teleinseguridad a la información estratégica. Esta presentación formó parte del Foro Internacional Interamericano sobre Seguridad y Convivencia que tuvo lugar en Medellín, Colombia, el 1
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Napp, Anke. Nicolai, Friedrich (Autor). Technische Universität Dresden, Forschungsstelle für Vergleichende Ordensgeschichte (FOVOG - Dresden), 2023. http://dx.doi.org/10.25368/2023.170.

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Page, Janie, Rongxin Yin, Mary Piette, and Richard Brown. Exploration of PG&E AutoDR Incentive Options. Final project report. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1631675.

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Napp, Anke. Nerval, Gérard de (Autor). Technische Universität Dresden, Forschungsstelle für Vergleichende Ordensgeschichte (FOVOG - Dresden), 2023. http://dx.doi.org/10.25368/2023.169.

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Costa Albuquerque, Renata, and Cristiane Pereira. Coleta Seletiva - Qual é o meu papel? Editora da Universidade de Vassouras, 2024. http://dx.doi.org/10.21727/palestrapirairecicla.pdf.

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Mendonça, Emanuele, and Cristiane Pereira. Foco nos desperdícios de alimentos – Uma proposta para uma sequência didática. Editora da Universidade de Vassouras, 2024. http://dx.doi.org/10.21727/sequenciadidatica.pdf.

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Albuquerque, Renata, and Cristiane Pereira. Guia Prático da Coleta Seletiva em Piraí. Editora da Universidade de Vassouras, 2024. http://dx.doi.org/10.21727/guiacartilha.pdf.

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Azevedo, Maria Luiza, and Paloma Mendonça. Folder sobre o uso de Fitoterápicos. Editora da Universidade de Vassouras, 2024. http://dx.doi.org/10.21727/folderfitoterapico.pdf.

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Grant, A. C., and K. D. McAlpine. Chapitre 6: La Marge Continentale Autour de Terre-Neuve. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1990. http://dx.doi.org/10.4095/132707.

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