Journal articles on the topic 'Black-box learning algorithm'
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Hwangbo, Jemin, Christian Gehring, Hannes Sommer, Roland Siegwart, and Jonas Buchli. "Policy Learning with an Efficient Black-Box Optimization Algorithm." International Journal of Humanoid Robotics 12, no. 03 (2015): 1550029. http://dx.doi.org/10.1142/s0219843615500292.
Full textKirsch, Louis, Sebastian Flennerhag, Hado van Hasselt, Abram Friesen, Junhyuk Oh, and Yutian Chen. "Introducing Symmetries to Black Box Meta Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 7 (2022): 7202–10. http://dx.doi.org/10.1609/aaai.v36i7.20681.
Full textXiang, Fengtao, Jiahui Xu, Wanpeng Zhang, and Weidong Wang. "A Distributed Biased Boundary Attack Method in Black-Box Attack." Applied Sciences 11, no. 21 (2021): 10479. http://dx.doi.org/10.3390/app112110479.
Full textLIU, Yanhe, Michael AFNAN, Vincent CONTIZER, et al. "Embryo Selection by “Black-Box” Artificial Intelligence: The Ethical and Epistemic Considerations." Fertility & Reproduction 04, no. 03n04 (2022): 147. http://dx.doi.org/10.1142/s2661318222740590.
Full textBausch, Johannes. "Fast Black-Box Quantum State Preparation." Quantum 6 (August 4, 2022): 773. http://dx.doi.org/10.22331/q-2022-08-04-773.
Full textMIKE, KOBY, and ORIT HAZZAN. "MACHINE LEARNING FOR NON-MAJORS: A WHITE BOX APPROACH." STATISTICS EDUCATION RESEARCH JOURNAL 21, no. 2 (2022): 10. http://dx.doi.org/10.52041/serj.v21i2.45.
Full textGarcía, Javier, Roberto Iglesias, Miguel A. Rodríguez, and Carlos V. Regueiro. "Directed Exploration in Black-Box Optimization for Multi-Objective Reinforcement Learning." International Journal of Information Technology & Decision Making 18, no. 03 (2019): 1045–82. http://dx.doi.org/10.1142/s0219622019500093.
Full textMayr, Franz, Sergio Yovine, and Ramiro Visca. "Property Checking with Interpretable Error Characterization for Recurrent Neural Networks." Machine Learning and Knowledge Extraction 3, no. 1 (2021): 205–27. http://dx.doi.org/10.3390/make3010010.
Full textAnđelić, Nikola, Ivan Lorencin, Matko Glučina, and Zlatan Car. "Mean Phase Voltages and Duty Cycles Estimation of a Three-Phase Inverter in a Drive System Using Machine Learning Algorithms." Electronics 11, no. 16 (2022): 2623. http://dx.doi.org/10.3390/electronics11162623.
Full textVeugen, Thijs, Bart Kamphorst, and Michiel Marcus. "Privacy-Preserving Contrastive Explanations with Local Foil Trees." Cryptography 6, no. 4 (2022): 54. http://dx.doi.org/10.3390/cryptography6040054.
Full textPulatov, Damir, and Lars Kotthoff. "Opening the Black Box: Automatically Characterizing Software for Algorithm Selection (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 10 (2020): 13899–900. http://dx.doi.org/10.1609/aaai.v34i10.7222.
Full textBALL, NICHOLAS M., and ROBERT J. BRUNNER. "DATA MINING AND MACHINE LEARNING IN ASTRONOMY." International Journal of Modern Physics D 19, no. 07 (2010): 1049–106. http://dx.doi.org/10.1142/s0218271810017160.
Full textYu, Wen, and Francisco Vega. "Nonlinear system modeling using the takagi-sugeno fuzzy model and long-short term memory cells." Journal of Intelligent & Fuzzy Systems 39, no. 3 (2020): 4547–56. http://dx.doi.org/10.3233/jifs-200491.
Full textMuñoz, Mario Andrés, and Michael Kirley. "Sampling Effects on Algorithm Selection for Continuous Black-Box Optimization." Algorithms 14, no. 1 (2021): 19. http://dx.doi.org/10.3390/a14010019.
Full textMuñoz, Mario Andrés, and Michael Kirley. "Sampling Effects on Algorithm Selection for Continuous Black-Box Optimization." Algorithms 14, no. 1 (2021): 19. http://dx.doi.org/10.3390/a14010019.
Full textŽlahtič, Bojan, Jernej Završnik, Helena Blažun Vošner, Peter Kokol, David Šuran, and 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, no. 14 (2023): 8329. http://dx.doi.org/10.3390/app13148329.
Full textHOLZINGER, ANDREAS, MARKUS PLASS, KATHARINA HOLZINGER, GLORIA CERASELA CRIS¸AN, CAMELIA-M. PINTEA, and 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, no. 2 (2019): 121–34. http://dx.doi.org/10.37193/cmi.2019.02.04.
Full textSaokani, Ukan, Mohamad Irfan, Dian Sa'adillah Maylawati, Rachmat Jaenal Abidin, Ichsan Taufik, and 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, no. 1 (2023): 10–14. http://dx.doi.org/10.15575/kjrt.v1i1.159.
Full textWongvibulsin, Shannon, Katherine C. Wu, and 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, no. 6 (2020): e15791. http://dx.doi.org/10.2196/15791.
Full textLu, Li, Yizhong Wu, Qi Zhang, and Ping Qiao. "A Transformation-Based Improved Kriging Method for the Black Box Problem in Reliability-Based Design Optimization." Mathematics 11, no. 1 (2023): 218. http://dx.doi.org/10.3390/math11010218.
Full textKerschke, Pascal, and Heike Trautmann. "Automated Algorithm Selection on Continuous Black-Box Problems by Combining Exploratory Landscape Analysis and Machine Learning." Evolutionary Computation 27, no. 1 (2019): 99–127. http://dx.doi.org/10.1162/evco_a_00236.
Full textPossatto, André Bina. "Painting the black box white: Interpreting an algorithm-based trading strategy." Brazilian Review of Finance 20, no. 3 (2022): 105–38. http://dx.doi.org/10.12660/rbfin.v20n3.2022.81999.
Full textVerma, Pulkit, Shashank Rao Marpally, and Siddharth Srivastava. "Asking the Right Questions: Learning Interpretable Action Models Through Query Answering." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 13 (2021): 12024–33. http://dx.doi.org/10.1609/aaai.v35i13.17428.
Full textZhu, Mingzhe, Jie Cheng, Tao Lei, et al. "C-RISE: A Post-Hoc Interpretation Method of Black-Box Models for SAR ATR." Remote Sensing 15, no. 12 (2023): 3103. http://dx.doi.org/10.3390/rs15123103.
Full textSudry, Matan, and Erez Karpas. "Learning to Estimate Search Progress Using Sequence of States." Proceedings of the International Conference on Automated Planning and Scheduling 32 (June 13, 2022): 362–70. http://dx.doi.org/10.1609/icaps.v32i1.19821.
Full textEnglert, Peter, and Marc Toussaint. "Learning manipulation skills from a single demonstration." International Journal of Robotics Research 37, no. 1 (2017): 137–54. http://dx.doi.org/10.1177/0278364917743795.
Full textYuan, Mu, Lan Zhang, and Xiang-Yang Li. "MLink: Linking Black-Box Models for Collaborative Multi-Model Inference." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 9 (2022): 9475–83. http://dx.doi.org/10.1609/aaai.v36i9.21180.
Full textWang, Fangwei, Yuanyuan Lu, Changguang Wang, and Qingru Li. "Binary Black-Box Adversarial Attacks with Evolutionary Learning against IoT Malware Detection." Wireless Communications and Mobile Computing 2021 (August 30, 2021): 1–9. http://dx.doi.org/10.1155/2021/8736946.
Full textCretu, Andrei. "Learning the Ashby Box: an experiment in second order cybernetic modeling." Kybernetes 49, no. 8 (2019): 2073–90. http://dx.doi.org/10.1108/k-06-2019-0439.
Full textLi, Zun, and Michael Wellman. "Structure Learning for Approximate Solution of Many-Player Games." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 02 (2020): 2119–27. http://dx.doi.org/10.1609/aaai.v34i02.5586.
Full textShahpouri, Saeid, Armin Norouzi, Christopher Hayduk, Reza Rezaei, Mahdi Shahbakhti, and Charles Robert Koch. "Hybrid Machine Learning Approaches and a Systematic Model Selection Process for Predicting Soot Emissions in Compression Ignition Engines." Energies 14, no. 23 (2021): 7865. http://dx.doi.org/10.3390/en14237865.
Full textBizzo, Bernardo C., Shadi Ebrahimian, Mark E. Walters, et al. "Validation pipeline for machine learning algorithm assessment for multiple vendors." PLOS ONE 17, no. 4 (2022): e0267213. http://dx.doi.org/10.1371/journal.pone.0267213.
Full textMcTavish, Hayden, Chudi Zhong, Reto Achermann, et al. "Fast Sparse Decision Tree Optimization via Reference Ensembles." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 9 (2022): 9604–13. http://dx.doi.org/10.1609/aaai.v36i9.21194.
Full textVan Calster, Ben, Laure Wynants, Dirk Timmerman, Ewout W. Steyerberg, and Gary S. Collins. "Predictive analytics in health care: how can we know it works?" Journal of the American Medical Informatics Association 26, no. 12 (2019): 1651–54. http://dx.doi.org/10.1093/jamia/ocz130.
Full textYin, Yiqiao, and Yash Bingi. "Using Machine Learning to Classify Human Fetal Health and Analyze Feature Importance." BioMedInformatics 3, no. 2 (2023): 280–98. http://dx.doi.org/10.3390/biomedinformatics3020019.
Full textAslam, Nida, Irfan Ullah Khan, Samiha Mirza, et al. "Interpretable Machine Learning Models for Malicious Domains Detection Using Explainable Artificial Intelligence (XAI)." Sustainability 14, no. 12 (2022): 7375. http://dx.doi.org/10.3390/su14127375.
Full textSoucha, Michal, and Kirill Bogdanov. "Observation Tree Approach: Active Learning Relying on Testing." Computer Journal 63, no. 9 (2019): 1298–310. http://dx.doi.org/10.1093/comjnl/bxz056.
Full textPatil, Vishakha, Ganesh Ghalme, Vineet Nair, and Y. Narahari. "Achieving Fairness in the Stochastic Multi-Armed Bandit Problem." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 5379–86. http://dx.doi.org/10.1609/aaai.v34i04.5986.
Full textLi, Yuancheng, and Yimeng Wang. "Defense Against Adversarial Attacks in Deep Learning." Applied Sciences 9, no. 1 (2018): 76. http://dx.doi.org/10.3390/app9010076.
Full textSamaras, Agorastos-Dimitrios, Serafeim Moustakidis, Ioannis D. Apostolopoulos, Elpiniki Papageorgiou, and Nikolaos Papandrianos. "Uncovering the Black Box of Coronary Artery Disease Diagnosis: The Significance of Explainability in Predictive Models." Applied Sciences 13, no. 14 (2023): 8120. http://dx.doi.org/10.3390/app13148120.
Full textRutten, Daan, and Debankur Mukherjee. "Capacity Scaling Augmented With Unreliable Machine Learning Predictions." ACM SIGMETRICS Performance Evaluation Review 49, no. 2 (2022): 24–26. http://dx.doi.org/10.1145/3512798.3512808.
Full textOtt, Simon, Adriano Barbosa-Silva, and Matthias Samwald. "LinkExplorer: predicting, explaining and exploring links in large biomedical knowledge graphs." Bioinformatics 38, no. 8 (2022): 2371–73. http://dx.doi.org/10.1093/bioinformatics/btac068.
Full textWang, Yanan, Xuebing Han, Languang Lu, Yangquan Chen, and Minggao Ouyang. "Sensitivity of Fractional-Order Recurrent Neural Network with Encoded Physics-Informed Battery Knowledge." Fractal and Fractional 6, no. 11 (2022): 640. http://dx.doi.org/10.3390/fractalfract6110640.
Full textKammüller, Florian, and Dimpy Satija. "Explanation of Student Attendance AI Prediction with the Isabelle Infrastructure Framework." Information 14, no. 8 (2023): 453. http://dx.doi.org/10.3390/info14080453.
Full textSalih, Dhiadeen Mohammed, Samsul Bahari Mohd Noor, Mohammad Hamiruce Merhaban, and Raja Mohd Kamil. "Wavelet Network: Online Sequential Extreme Learning Machine for Nonlinear Dynamic Systems Identification." Advances in Artificial Intelligence 2015 (September 20, 2015): 1–10. http://dx.doi.org/10.1155/2015/184318.
Full textLuong, Ngoc Hoang, Han La Poutré, and Peter A. N. Bosman. "Exploiting Linkage Information and Problem-Specific Knowledge in Evolutionary Distribution Network Expansion Planning." Evolutionary Computation 26, no. 3 (2018): 471–505. http://dx.doi.org/10.1162/evco_a_00209.
Full textYiğit, Tuncay, Nilgün Şengöz, Özlem Özmen, Jude Hemanth, and Ali Hakan Işık. "Diagnosis of Paratuberculosis in Histopathological Images Based on Explainable Artificial Intelligence and Deep Learning." Traitement du Signal 39, no. 3 (2022): 863–69. http://dx.doi.org/10.18280/ts.390311.
Full textDu, Xiaohu, Jie Yu, Zibo Yi, et al. "A Hybrid Adversarial Attack for Different Application Scenarios." Applied Sciences 10, no. 10 (2020): 3559. http://dx.doi.org/10.3390/app10103559.
Full textSaleem, Sobia, Marcus Gallagher, and Ian Wood. "Direct Feature Evaluation in Black-Box Optimization Using Problem Transformations." Evolutionary Computation 27, no. 1 (2019): 75–98. http://dx.doi.org/10.1162/evco_a_00247.
Full textBarkalov, Konstantin, Ilya Lebedev, and Evgeny Kozinov. "Acceleration of Global Optimization Algorithm by Detecting Local Extrema Based on Machine Learning." Entropy 23, no. 10 (2021): 1272. http://dx.doi.org/10.3390/e23101272.
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