Artículos de revistas sobre el tema "Black-box learning algorithm"
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Hwangbo, Jemin, Christian Gehring, Hannes Sommer, Roland Siegwart y Jonas Buchli. "Policy Learning with an Efficient Black-Box Optimization Algorithm". International Journal of Humanoid Robotics 12, n.º 03 (septiembre de 2015): 1550029. http://dx.doi.org/10.1142/s0219843615500292.
Texto completoKirsch, Louis, Sebastian Flennerhag, Hado van Hasselt, Abram Friesen, Junhyuk Oh y Yutian Chen. "Introducing Symmetries to Black Box Meta Reinforcement Learning". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 7 (28 de junio de 2022): 7202–10. http://dx.doi.org/10.1609/aaai.v36i7.20681.
Texto completoXiang, Fengtao, Jiahui Xu, Wanpeng Zhang y Weidong Wang. "A Distributed Biased Boundary Attack Method in Black-Box Attack". Applied Sciences 11, n.º 21 (8 de noviembre de 2021): 10479. http://dx.doi.org/10.3390/app112110479.
Texto completoLIU, Yanhe, Michael AFNAN, Vincent CONTIZER, Cynthia RUDIN, Abhishek MISHRA, Julian SAVULESCU y Masoud AFNAN. "Embryo Selection by “Black-Box” Artificial Intelligence: The Ethical and Epistemic Considerations". Fertility & Reproduction 04, n.º 03n04 (septiembre de 2022): 147. http://dx.doi.org/10.1142/s2661318222740590.
Texto completoBausch, Johannes. "Fast Black-Box Quantum State Preparation". Quantum 6 (4 de agosto de 2022): 773. http://dx.doi.org/10.22331/q-2022-08-04-773.
Texto completoMIKE, KOBY y ORIT HAZZAN. "MACHINE LEARNING FOR NON-MAJORS: A WHITE BOX APPROACH". STATISTICS EDUCATION RESEARCH JOURNAL 21, n.º 2 (4 de julio de 2022): 10. http://dx.doi.org/10.52041/serj.v21i2.45.
Texto completoGarcía, Javier, Roberto Iglesias, Miguel A. Rodríguez y Carlos V. Regueiro. "Directed Exploration in Black-Box Optimization for Multi-Objective Reinforcement Learning". International Journal of Information Technology & Decision Making 18, n.º 03 (mayo de 2019): 1045–82. http://dx.doi.org/10.1142/s0219622019500093.
Texto completoMayr, Franz, Sergio Yovine y Ramiro Visca. "Property Checking with Interpretable Error Characterization for Recurrent Neural Networks". Machine Learning and Knowledge Extraction 3, n.º 1 (12 de febrero de 2021): 205–27. http://dx.doi.org/10.3390/make3010010.
Texto completoAnđelić, Nikola, Ivan Lorencin, Matko Glučina y Zlatan Car. "Mean Phase Voltages and Duty Cycles Estimation of a Three-Phase Inverter in a Drive System Using Machine Learning Algorithms". Electronics 11, n.º 16 (21 de agosto de 2022): 2623. http://dx.doi.org/10.3390/electronics11162623.
Texto completoVeugen, Thijs, Bart Kamphorst y Michiel Marcus. "Privacy-Preserving Contrastive Explanations with Local Foil Trees". Cryptography 6, n.º 4 (28 de octubre de 2022): 54. http://dx.doi.org/10.3390/cryptography6040054.
Texto completoPulatov, Damir y Lars Kotthoff. "Opening the Black Box: Automatically Characterizing Software for Algorithm Selection (Student Abstract)". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 10 (3 de abril de 2020): 13899–900. http://dx.doi.org/10.1609/aaai.v34i10.7222.
Texto completoBALL, NICHOLAS M. y ROBERT J. BRUNNER. "DATA MINING AND MACHINE LEARNING IN ASTRONOMY". International Journal of Modern Physics D 19, n.º 07 (julio de 2010): 1049–106. http://dx.doi.org/10.1142/s0218271810017160.
Texto completoYu, Wen y Francisco Vega. "Nonlinear system modeling using the takagi-sugeno fuzzy model and long-short term memory cells". Journal of Intelligent & Fuzzy Systems 39, n.º 3 (7 de octubre de 2020): 4547–56. http://dx.doi.org/10.3233/jifs-200491.
Texto completoMuñoz, Mario Andrés y Michael Kirley. "Sampling Effects on Algorithm Selection for Continuous Black-Box Optimization". Algorithms 14, n.º 1 (11 de enero de 2021): 19. http://dx.doi.org/10.3390/a14010019.
Texto completoMuñoz, Mario Andrés y Michael Kirley. "Sampling Effects on Algorithm Selection for Continuous Black-Box Optimization". Algorithms 14, n.º 1 (11 de enero de 2021): 19. http://dx.doi.org/10.3390/a14010019.
Texto completoŽlahtič, Bojan, Jernej Završnik, Helena Blažun Vošner, Peter Kokol, David Šuran y Tadej Završnik. "Agile Machine Learning Model Development Using Data Canyons in Medicine: A Step towards Explainable Artificial Intelligence and Flexible Expert-Based Model Improvement". Applied Sciences 13, n.º 14 (19 de julio de 2023): 8329. http://dx.doi.org/10.3390/app13148329.
Texto completoHOLZINGER, ANDREAS, MARKUS PLASS, KATHARINA HOLZINGER, GLORIA CERASELA CRIS¸AN, CAMELIA-M. PINTEA y VASILE PALADE. "A glass-box interactive machine learning approach for solving NP-hard problems with the human-in-the-loop". Creative Mathematics and Informatics 28, n.º 2 (20 de junio de 2019): 121–34. http://dx.doi.org/10.37193/cmi.2019.02.04.
Texto completoSaokani, Ukan, Mohamad Irfan, Dian Sa'adillah Maylawati, Rachmat Jaenal Abidin, Ichsan Taufik y Riyan Naufal Hay's. "Comparison of the Fisher-Yates Shuffle and the Linear Congruent Algorithm for Randomizing Questions in Nahwu Learning Multimedia". Khazanah Journal of Religion and Technology 1, n.º 1 (1 de junio de 2023): 10–14. http://dx.doi.org/10.15575/kjrt.v1i1.159.
Texto completoWongvibulsin, Shannon, Katherine C. Wu y Scott L. Zeger. "Improving Clinical Translation of Machine Learning Approaches Through Clinician-Tailored Visual Displays of Black Box Algorithms: Development and Validation". JMIR Medical Informatics 8, n.º 6 (9 de junio de 2020): e15791. http://dx.doi.org/10.2196/15791.
Texto completoLu, Li, Yizhong Wu, Qi Zhang y Ping Qiao. "A Transformation-Based Improved Kriging Method for the Black Box Problem in Reliability-Based Design Optimization". Mathematics 11, n.º 1 (1 de enero de 2023): 218. http://dx.doi.org/10.3390/math11010218.
Texto completoKerschke, Pascal y Heike Trautmann. "Automated Algorithm Selection on Continuous Black-Box Problems by Combining Exploratory Landscape Analysis and Machine Learning". Evolutionary Computation 27, n.º 1 (marzo de 2019): 99–127. http://dx.doi.org/10.1162/evco_a_00236.
Texto completoPossatto, André Bina. "Painting the black box white: Interpreting an algorithm-based trading strategy". Brazilian Review of Finance 20, n.º 3 (11 de septiembre de 2022): 105–38. http://dx.doi.org/10.12660/rbfin.v20n3.2022.81999.
Texto completoVerma, Pulkit, Shashank Rao Marpally y Siddharth Srivastava. "Asking the Right Questions: Learning Interpretable Action Models Through Query Answering". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 13 (18 de mayo de 2021): 12024–33. http://dx.doi.org/10.1609/aaai.v35i13.17428.
Texto completoZhu, Mingzhe, Jie Cheng, Tao Lei, Zhenpeng Feng, Xianda Zhou, Yuanjing Liu y Zhihan Chen. "C-RISE: A Post-Hoc Interpretation Method of Black-Box Models for SAR ATR". Remote Sensing 15, n.º 12 (14 de junio de 2023): 3103. http://dx.doi.org/10.3390/rs15123103.
Texto completoSudry, Matan y Erez Karpas. "Learning to Estimate Search Progress Using Sequence of States". Proceedings of the International Conference on Automated Planning and Scheduling 32 (13 de junio de 2022): 362–70. http://dx.doi.org/10.1609/icaps.v32i1.19821.
Texto completoEnglert, Peter y Marc Toussaint. "Learning manipulation skills from a single demonstration". International Journal of Robotics Research 37, n.º 1 (5 de diciembre de 2017): 137–54. http://dx.doi.org/10.1177/0278364917743795.
Texto completoYuan, Mu, Lan Zhang y Xiang-Yang Li. "MLink: Linking Black-Box Models for Collaborative Multi-Model Inference". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 9 (28 de junio de 2022): 9475–83. http://dx.doi.org/10.1609/aaai.v36i9.21180.
Texto completoWang, Fangwei, Yuanyuan Lu, Changguang Wang y Qingru Li. "Binary Black-Box Adversarial Attacks with Evolutionary Learning against IoT Malware Detection". Wireless Communications and Mobile Computing 2021 (30 de agosto de 2021): 1–9. http://dx.doi.org/10.1155/2021/8736946.
Texto completoCretu, Andrei. "Learning the Ashby Box: an experiment in second order cybernetic modeling". Kybernetes 49, n.º 8 (23 de noviembre de 2019): 2073–90. http://dx.doi.org/10.1108/k-06-2019-0439.
Texto completoLi, Zun y Michael Wellman. "Structure Learning for Approximate Solution of Many-Player Games". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 02 (3 de abril de 2020): 2119–27. http://dx.doi.org/10.1609/aaai.v34i02.5586.
Texto completoShahpouri, Saeid, Armin Norouzi, Christopher Hayduk, Reza Rezaei, Mahdi Shahbakhti y Charles Robert Koch. "Hybrid Machine Learning Approaches and a Systematic Model Selection Process for Predicting Soot Emissions in Compression Ignition Engines". Energies 14, n.º 23 (24 de noviembre de 2021): 7865. http://dx.doi.org/10.3390/en14237865.
Texto completoBizzo, Bernardo C., Shadi Ebrahimian, Mark E. Walters, Mark H. Michalski, Katherine P. Andriole, Keith J. Dreyer, Mannudeep K. Kalra, Tarik Alkasab y Subba R. Digumarthy. "Validation pipeline for machine learning algorithm assessment for multiple vendors". PLOS ONE 17, n.º 4 (29 de abril de 2022): e0267213. http://dx.doi.org/10.1371/journal.pone.0267213.
Texto completoMcTavish, Hayden, Chudi Zhong, Reto Achermann, Ilias Karimalis, Jacques Chen, Cynthia Rudin y Margo Seltzer. "Fast Sparse Decision Tree Optimization via Reference Ensembles". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 9 (28 de junio de 2022): 9604–13. http://dx.doi.org/10.1609/aaai.v36i9.21194.
Texto completoVan Calster, Ben, Laure Wynants, Dirk Timmerman, Ewout W. Steyerberg y Gary S. Collins. "Predictive analytics in health care: how can we know it works?" Journal of the American Medical Informatics Association 26, n.º 12 (2 de agosto de 2019): 1651–54. http://dx.doi.org/10.1093/jamia/ocz130.
Texto completoYin, Yiqiao y Yash Bingi. "Using Machine Learning to Classify Human Fetal Health and Analyze Feature Importance". BioMedInformatics 3, n.º 2 (1 de abril de 2023): 280–98. http://dx.doi.org/10.3390/biomedinformatics3020019.
Texto completoAslam, Nida, Irfan Ullah Khan, Samiha Mirza, Alanoud AlOwayed, Fatima M. Anis, Reef M. Aljuaid y Reham Baageel. "Interpretable Machine Learning Models for Malicious Domains Detection Using Explainable Artificial Intelligence (XAI)". Sustainability 14, n.º 12 (16 de junio de 2022): 7375. http://dx.doi.org/10.3390/su14127375.
Texto completoSoucha, Michal y Kirill Bogdanov. "Observation Tree Approach: Active Learning Relying on Testing". Computer Journal 63, n.º 9 (3 de julio de 2019): 1298–310. http://dx.doi.org/10.1093/comjnl/bxz056.
Texto completoPatil, Vishakha, Ganesh Ghalme, Vineet Nair y Y. Narahari. "Achieving Fairness in the Stochastic Multi-Armed Bandit Problem". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 04 (3 de abril de 2020): 5379–86. http://dx.doi.org/10.1609/aaai.v34i04.5986.
Texto completoLi, Yuancheng y Yimeng Wang. "Defense Against Adversarial Attacks in Deep Learning". Applied Sciences 9, n.º 1 (26 de diciembre de 2018): 76. http://dx.doi.org/10.3390/app9010076.
Texto completoSamaras, Agorastos-Dimitrios, Serafeim Moustakidis, Ioannis D. Apostolopoulos, Elpiniki Papageorgiou y Nikolaos Papandrianos. "Uncovering the Black Box of Coronary Artery Disease Diagnosis: The Significance of Explainability in Predictive Models". Applied Sciences 13, n.º 14 (12 de julio de 2023): 8120. http://dx.doi.org/10.3390/app13148120.
Texto completoRutten, Daan y Debankur Mukherjee. "Capacity Scaling Augmented With Unreliable Machine Learning Predictions". ACM SIGMETRICS Performance Evaluation Review 49, n.º 2 (17 de enero de 2022): 24–26. http://dx.doi.org/10.1145/3512798.3512808.
Texto completoOtt, Simon, Adriano Barbosa-Silva y Matthias Samwald. "LinkExplorer: predicting, explaining and exploring links in large biomedical knowledge graphs". Bioinformatics 38, n.º 8 (9 de febrero de 2022): 2371–73. http://dx.doi.org/10.1093/bioinformatics/btac068.
Texto completoWang, Yanan, Xuebing Han, Languang Lu, Yangquan Chen y Minggao Ouyang. "Sensitivity of Fractional-Order Recurrent Neural Network with Encoded Physics-Informed Battery Knowledge". Fractal and Fractional 6, n.º 11 (2 de noviembre de 2022): 640. http://dx.doi.org/10.3390/fractalfract6110640.
Texto completoKammüller, Florian y Dimpy Satija. "Explanation of Student Attendance AI Prediction with the Isabelle Infrastructure Framework". Information 14, n.º 8 (10 de agosto de 2023): 453. http://dx.doi.org/10.3390/info14080453.
Texto completoSalih, Dhiadeen Mohammed, Samsul Bahari Mohd Noor, Mohammad Hamiruce Merhaban y Raja Mohd Kamil. "Wavelet Network: Online Sequential Extreme Learning Machine for Nonlinear Dynamic Systems Identification". Advances in Artificial Intelligence 2015 (20 de septiembre de 2015): 1–10. http://dx.doi.org/10.1155/2015/184318.
Texto completoLuong, Ngoc Hoang, Han La Poutré y Peter A. N. Bosman. "Exploiting Linkage Information and Problem-Specific Knowledge in Evolutionary Distribution Network Expansion Planning". Evolutionary Computation 26, n.º 3 (septiembre de 2018): 471–505. http://dx.doi.org/10.1162/evco_a_00209.
Texto completoYiğit, Tuncay, Nilgün Şengöz, Özlem Özmen, Jude Hemanth y Ali Hakan Işık. "Diagnosis of Paratuberculosis in Histopathological Images Based on Explainable Artificial Intelligence and Deep Learning". Traitement du Signal 39, n.º 3 (30 de junio de 2022): 863–69. http://dx.doi.org/10.18280/ts.390311.
Texto completoDu, Xiaohu, Jie Yu, Zibo Yi, Shasha Li, Jun Ma, Yusong Tan y Qinbo Wu. "A Hybrid Adversarial Attack for Different Application Scenarios". Applied Sciences 10, n.º 10 (21 de mayo de 2020): 3559. http://dx.doi.org/10.3390/app10103559.
Texto completoSaleem, Sobia, Marcus Gallagher y Ian Wood. "Direct Feature Evaluation in Black-Box Optimization Using Problem Transformations". Evolutionary Computation 27, n.º 1 (marzo de 2019): 75–98. http://dx.doi.org/10.1162/evco_a_00247.
Texto completoBarkalov, Konstantin, Ilya Lebedev y Evgeny Kozinov. "Acceleration of Global Optimization Algorithm by Detecting Local Extrema Based on Machine Learning". Entropy 23, n.º 10 (28 de septiembre de 2021): 1272. http://dx.doi.org/10.3390/e23101272.
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