Artigos de revistas sobre o tema "Deep learning with uncertainty"
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Liu, Wei, Xiaodong Yue, Yufei Chen e Thierry Denoeux. "Trusted Multi-View Deep Learning with Opinion Aggregation". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 7 (28 de junho de 2022): 7585–93. http://dx.doi.org/10.1609/aaai.v36i7.20724.
Texto completo da fonteOh, Dongpin, e Bonggun Shin. "Improving Evidential Deep Learning via Multi-Task Learning". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 7 (28 de junho de 2022): 7895–903. http://dx.doi.org/10.1609/aaai.v36i7.20759.
Texto completo da fonteBajorath, Jürgen. "Understanding uncertainty in deep learning builds confidence". Artificial Intelligence in the Life Sciences 2 (dezembro de 2022): 100033. http://dx.doi.org/10.1016/j.ailsci.2022.100033.
Texto completo da fontevan den Berg, Cornelis A. T., e Ettore F. Meliadò. "Uncertainty Assessment for Deep Learning Radiotherapy Applications". Seminars in Radiation Oncology 32, n.º 4 (outubro de 2022): 304–18. http://dx.doi.org/10.1016/j.semradonc.2022.06.001.
Texto completo da fonteZheng, Rui, Shulin Zhang, Lei Liu, Yuhao Luo e Mingzhai Sun. "Uncertainty in Bayesian deep label distribution learning". Applied Soft Computing 101 (março de 2021): 107046. http://dx.doi.org/10.1016/j.asoc.2020.107046.
Texto completo da fonteLockwood, Owen, e Mei Si. "A Review of Uncertainty for Deep Reinforcement Learning". Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 18, n.º 1 (11 de outubro de 2022): 155–62. http://dx.doi.org/10.1609/aiide.v18i1.21959.
Texto completo da fonteKarimi, Hamed, e Reza Samavi. "Quantifying Deep Learning Model Uncertainty in Conformal Prediction". Proceedings of the AAAI Symposium Series 1, n.º 1 (3 de outubro de 2023): 142–48. http://dx.doi.org/10.1609/aaaiss.v1i1.27492.
Texto completo da fonteCaldeira, João, e Brian Nord. "Deeply uncertain: comparing methods of uncertainty quantification in deep learning algorithms". Machine Learning: Science and Technology 2, n.º 1 (4 de dezembro de 2020): 015002. http://dx.doi.org/10.1088/2632-2153/aba6f3.
Texto completo da fonteDa Silva, Felipe Leno, Pablo Hernandez-Leal, Bilal Kartal e Matthew E. Taylor. "Uncertainty-Aware Action Advising for Deep Reinforcement Learning Agents". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 04 (3 de abril de 2020): 5792–99. http://dx.doi.org/10.1609/aaai.v34i04.6036.
Texto completo da fonteKawano, Yasufumi, Yoshiki Nota, Rinpei Mochizuki e Yoshimitsu Aoki. "Non-Deep Active Learning for Deep Neural Networks". Sensors 22, n.º 14 (13 de julho de 2022): 5244. http://dx.doi.org/10.3390/s22145244.
Texto completo da fonteGou, Xiaohong, e Xuenong He. "Deep Learning-Based Detection and Diagnosis of Subarachnoid Hemorrhage". Journal of Healthcare Engineering 2021 (22 de novembro de 2021): 1–10. http://dx.doi.org/10.1155/2021/9639419.
Texto completo da fonteLoftus, Tyler J., Benjamin Shickel, Matthew M. Ruppert, Jeremy A. Balch, Tezcan Ozrazgat-Baslanti, Patrick J. Tighe, Philip A. Efron et al. "Uncertainty-aware deep learning in healthcare: A scoping review". PLOS Digital Health 1, n.º 8 (10 de agosto de 2022): e0000085. http://dx.doi.org/10.1371/journal.pdig.0000085.
Texto completo da fonteXu, Lei, Nengcheng Chen, Chao Yang, Hongchu Yu e Zeqiang Chen. "Quantifying the uncertainty of precipitation forecasting using probabilistic deep learning". Hydrology and Earth System Sciences 26, n.º 11 (14 de junho de 2022): 2923–38. http://dx.doi.org/10.5194/hess-26-2923-2022.
Texto completo da fontePham, Nam, Sergey Fomel e Dallas Dunlap. "Automatic channel detection using deep learning". Interpretation 7, n.º 3 (1 de agosto de 2019): SE43—SE50. http://dx.doi.org/10.1190/int-2018-0202.1.
Texto completo da fonteKabir, H. M. Dipu, Sadia Khanam, Fahime Khozeimeh, Abbas Khosravi, Subrota Kumar Mondal, Saeid Nahavandi e U. Rajendra Acharya. "Aleatory-aware deep uncertainty quantification for transfer learning". Computers in Biology and Medicine 143 (abril de 2022): 105246. http://dx.doi.org/10.1016/j.compbiomed.2022.105246.
Texto completo da fonteMorocho-Cayamcela, Manuel Eugenio, Martin Maier e Wansu Lim. "Breaking Wireless Propagation Environmental Uncertainty With Deep Learning". IEEE Transactions on Wireless Communications 19, n.º 8 (agosto de 2020): 5075–87. http://dx.doi.org/10.1109/twc.2020.2986202.
Texto completo da fonteGude, Vinayaka, Steven Corns e Suzanna Long. "Flood Prediction and Uncertainty Estimation Using Deep Learning". Water 12, n.º 3 (21 de março de 2020): 884. http://dx.doi.org/10.3390/w12030884.
Texto completo da fontePei, Zhihao, Angela M. Rojas-Arevalo, Fjalar J. de Haan, Nir Lipovetzky e Enayat A. Moallemi. "Reinforcement learning for decision-making under deep uncertainty". Journal of Environmental Management 359 (maio de 2024): 120968. http://dx.doi.org/10.1016/j.jenvman.2024.120968.
Texto completo da fontePeluso, Alina, Ioana Danciu, Hong-Jun Yoon, Jamaludin Mohd Yusof, Tanmoy Bhattacharya, Adam Spannaus, Noah Schaefferkoetter et al. "Deep learning uncertainty quantification for clinical text classification". Journal of Biomedical Informatics 149 (janeiro de 2024): 104576. http://dx.doi.org/10.1016/j.jbi.2023.104576.
Texto completo da fonteMurad, Abdulmajid, Frank Alexander Kraemer, Kerstin Bach e Gavin Taylor. "Uncertainty-aware autonomous sensing with deep reinforcement learning". Future Generation Computer Systems 156 (julho de 2024): 242–53. http://dx.doi.org/10.1016/j.future.2024.03.021.
Texto completo da fonteYoon, Young-In, e Hye-Young Jeong. "A Comparison of Uncertainty Quantification of Deep Learning models for Time Series". Korean Data Analysis Society 26, n.º 1 (29 de fevereiro de 2024): 163–74. http://dx.doi.org/10.37727/jkdas.2024.26.1.163.
Texto completo da fonteBhatia, Abhinav, Pradeep Varakantham e Akshat Kumar. "Resource Constrained Deep Reinforcement Learning". Proceedings of the International Conference on Automated Planning and Scheduling 29 (25 de maio de 2021): 610–20. http://dx.doi.org/10.1609/icaps.v29i1.3528.
Texto completo da fonteSerpell, Cristián, Ignacio A. Araya, Carlos Valle e Héctor Allende. "Addressing model uncertainty in probabilistic forecasting using Monte Carlo dropout". Intelligent Data Analysis 24 (4 de dezembro de 2020): 185–205. http://dx.doi.org/10.3233/ida-200015.
Texto completo da fonteSilva, Felipe Leno Da, Pablo Hernandez-Leal, Bilal Kartal e Matthew E. Taylor. "Providing Uncertainty-Based Advice for Deep Reinforcement Learning Agents (Student Abstract)". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 10 (3 de abril de 2020): 13913–14. http://dx.doi.org/10.1609/aaai.v34i10.7229.
Texto completo da fonteWang, Chun, e Jiquan Ma. "Uncertainty-Supervised Super-Resolution Deep Learning Network in Diffusion MRI". Highlights in Science, Engineering and Technology 45 (18 de abril de 2023): 7–10. http://dx.doi.org/10.54097/hset.v45i.7288.
Texto completo da fonteFeng, Zhiyuan, Kai Qi, Bin Shi, Hao Mei, Qinghua Zheng e Hua Wei. "Deep evidential learning in diffusion convolutional recurrent neural network". Electronic Research Archive 31, n.º 4 (2023): 2252–64. http://dx.doi.org/10.3934/era.2023115.
Texto completo da fonteChaudhary, Priyanka, João P. Leitão, Tabea Donauer, Stefano D’Aronco, Nathanaël Perraudin, Guillaume Obozinski, Fernando Perez-Cruz, Konrad Schindler, Jan Dirk Wegner e Stefania Russo. "Flood Uncertainty Estimation Using Deep Ensembles". Water 14, n.º 19 (22 de setembro de 2022): 2980. http://dx.doi.org/10.3390/w14192980.
Texto completo da fonteLi, Xingjian, Pengkun Yang, Yangcheng Gu, Xueying Zhan, Tianyang Wang, Min Xu e Chengzhong Xu. "Deep Active Learning with Noise Stability". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 12 (24 de março de 2024): 13655–63. http://dx.doi.org/10.1609/aaai.v38i12.29270.
Texto completo da fonteHong, Ming, Jianzhuang Liu, Cuihua Li e Yanyun Qu. "Uncertainty-Driven Dehazing Network". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 1 (28 de junho de 2022): 906–13. http://dx.doi.org/10.1609/aaai.v36i1.19973.
Texto completo da fonteKompa, Benjamin, Jasper Snoek e Andrew L. Beam. "Empirical Frequentist Coverage of Deep Learning Uncertainty Quantification Procedures". Entropy 23, n.º 12 (30 de novembro de 2021): 1608. http://dx.doi.org/10.3390/e23121608.
Texto completo da fonteYu, Yang, Danruo Deng, Furui Liu, Qi Dou, Yueming Jin, Guangyong Chen e Pheng Ann Heng. "ANEDL: Adaptive Negative Evidential Deep Learning for Open-Set Semi-supervised Learning". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 15 (24 de março de 2024): 16587–95. http://dx.doi.org/10.1609/aaai.v38i15.29597.
Texto completo da fonteKlotz, Daniel, Frederik Kratzert, Martin Gauch, Alden Keefe Sampson, Johannes Brandstetter, Günter Klambauer, Sepp Hochreiter e Grey Nearing. "Uncertainty estimation with deep learning for rainfall–runoff modeling". Hydrology and Earth System Sciences 26, n.º 6 (31 de março de 2022): 1673–93. http://dx.doi.org/10.5194/hess-26-1673-2022.
Texto completo da fonteLv, Xiaoming, Fajie Duan, Jia-Jia Jiang, Xiao Fu e Lin Gan. "Deep Active Learning for Surface Defect Detection". Sensors 20, n.º 6 (16 de março de 2020): 1650. http://dx.doi.org/10.3390/s20061650.
Texto completo da fonteBi, Wei, Wenhua Chen e Jun Pan. "Multidisciplinary Reliability Design Considering Hybrid Uncertainty Incorporating Deep Learning". Wireless Communications and Mobile Computing 2022 (18 de novembro de 2022): 1–11. http://dx.doi.org/10.1155/2022/5846684.
Texto completo da fonteCifci, Mehmet Akif. "A Deep Learning-Based Framework for Uncertainty Quantification in Medical Imaging Using the DropWeak Technique: An Empirical Study with Baresnet". Diagnostics 13, n.º 4 (20 de fevereiro de 2023): 800. http://dx.doi.org/10.3390/diagnostics13040800.
Texto completo da fonteKim, Mingyu, e Donghyun Lee. "Why Uncertainty in Deep Learning for Traffic Flow Prediction Is Needed". Sustainability 15, n.º 23 (22 de novembro de 2023): 16204. http://dx.doi.org/10.3390/su152316204.
Texto completo da fonteMaged, Ahmed, e Min Xie. "Uncertainty utilization in fault detection using Bayesian deep learning". Journal of Manufacturing Systems 64 (julho de 2022): 316–29. http://dx.doi.org/10.1016/j.jmsy.2022.07.002.
Texto completo da fonteFeng, Shijie, Chao Zuo, Yan Hu, Yixuan Li e Qian Chen. "Deep-learning-based fringe-pattern analysis with uncertainty estimation". Optica 8, n.º 12 (23 de novembro de 2021): 1507. http://dx.doi.org/10.1364/optica.434311.
Texto completo da fonteLoquercio, Antonio, Mattia Segu e Davide Scaramuzza. "A General Framework for Uncertainty Estimation in Deep Learning". IEEE Robotics and Automation Letters 5, n.º 2 (abril de 2020): 3153–60. http://dx.doi.org/10.1109/lra.2020.2974682.
Texto completo da fonteQin, Yu, Zhiwen Liu, Chenghao Liu, Yuxing Li, Xiangzhu Zeng e Chuyang Ye. "Super-Resolved q-Space deep learning with uncertainty quantification". Medical Image Analysis 67 (janeiro de 2021): 101885. http://dx.doi.org/10.1016/j.media.2020.101885.
Texto completo da fontePeng, Weiwen, Zhi-Sheng Ye e Nan Chen. "Bayesian Deep-Learning-Based Health Prognostics Toward Prognostics Uncertainty". IEEE Transactions on Industrial Electronics 67, n.º 3 (março de 2020): 2283–93. http://dx.doi.org/10.1109/tie.2019.2907440.
Texto completo da fonteXue, Yujia, Shiyi Cheng, Yunzhe Li e Lei Tian. "Reliable deep-learning-based phase imaging with uncertainty quantification". Optica 6, n.º 5 (7 de maio de 2019): 618. http://dx.doi.org/10.1364/optica.6.000618.
Texto completo da fonteAbdullah, Abdullah A., Masoud M. Hassan e Yaseen T. Mustafa. "Uncertainty Quantification for MLP-Mixer Using Bayesian Deep Learning". Applied Sciences 13, n.º 7 (3 de abril de 2023): 4547. http://dx.doi.org/10.3390/app13074547.
Texto completo da fonteHabibpour, Maryam, Hassan Gharoun, Mohammadreza Mehdipour, AmirReza Tajally, Hamzeh Asgharnezhad, Afshar Shamsi, Abbas Khosravi e Saeid Nahavandi. "Uncertainty-aware credit card fraud detection using deep learning". Engineering Applications of Artificial Intelligence 123 (agosto de 2023): 106248. http://dx.doi.org/10.1016/j.engappai.2023.106248.
Texto completo da fonteDas, Neha, Jonas Umlauft, Armin Lederer, Alexandre Capone, Thomas Beckers e Sandra Hirche. "Deep Learning based Uncertainty Decomposition for Real-time Control". IFAC-PapersOnLine 56, n.º 2 (2023): 847–53. http://dx.doi.org/10.1016/j.ifacol.2023.10.1671.
Texto completo da fonteKoh, D., A. Mishra e K. Terao. "Deep neural network uncertainty quantification for LArTPC reconstruction". Journal of Instrumentation 18, n.º 12 (1 de dezembro de 2023): P12013. http://dx.doi.org/10.1088/1748-0221/18/12/p12013.
Texto completo da fonteMurad, Abdulmajid, Frank Alexander Kraemer, Kerstin Bach e Gavin Taylor. "Probabilistic Deep Learning to Quantify Uncertainty in Air Quality Forecasting". Sensors 21, n.º 23 (30 de novembro de 2021): 8009. http://dx.doi.org/10.3390/s21238009.
Texto completo da fonteAldhahi, Waleed, e Sanghoon Sull. "Uncertain-CAM: Uncertainty-Based Ensemble Machine Voting for Improved COVID-19 CXR Classification and Explainability". Diagnostics 13, n.º 3 (26 de janeiro de 2023): 441. http://dx.doi.org/10.3390/diagnostics13030441.
Texto completo da fonteJi, Ying, Jianhui Wang, Jiacan Xu, Xiaoke Fang e Huaguang Zhang. "Real-Time Energy Management of a Microgrid Using Deep Reinforcement Learning". Energies 12, n.º 12 (15 de junho de 2019): 2291. http://dx.doi.org/10.3390/en12122291.
Texto completo da fonteJi, Ying, Jianhui Wang, Jiacan Xu e Donglin Li. "Data-Driven Online Energy Scheduling of a Microgrid Based on Deep Reinforcement Learning". Energies 14, n.º 8 (10 de abril de 2021): 2120. http://dx.doi.org/10.3390/en14082120.
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