Статті в журналах з теми "Deep learning with uncertainty"
Оформте джерело за APA, MLA, Chicago, Harvard та іншими стилями
Ознайомтеся з топ-50 статей у журналах для дослідження на тему "Deep learning with uncertainty".
Біля кожної праці в переліку літератури доступна кнопка «Додати до бібліографії». Скористайтеся нею – і ми автоматично оформимо бібліографічне посилання на обрану працю в потрібному вам стилі цитування: APA, MLA, «Гарвард», «Чикаго», «Ванкувер» тощо.
Також ви можете завантажити повний текст наукової публікації у форматі «.pdf» та прочитати онлайн анотацію до роботи, якщо відповідні параметри наявні в метаданих.
Переглядайте статті в журналах для різних дисциплін та оформлюйте правильно вашу бібліографію.
Liu, Wei, Xiaodong Yue, Yufei Chen, and Thierry Denoeux. "Trusted Multi-View Deep Learning with Opinion Aggregation." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 7 (June 28, 2022): 7585–93. http://dx.doi.org/10.1609/aaai.v36i7.20724.
Oh, Dongpin, and Bonggun Shin. "Improving Evidential Deep Learning via Multi-Task Learning." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 7 (June 28, 2022): 7895–903. http://dx.doi.org/10.1609/aaai.v36i7.20759.
Bajorath, Jürgen. "Understanding uncertainty in deep learning builds confidence." Artificial Intelligence in the Life Sciences 2 (December 2022): 100033. http://dx.doi.org/10.1016/j.ailsci.2022.100033.
van den Berg, Cornelis A. T., and Ettore F. Meliadò. "Uncertainty Assessment for Deep Learning Radiotherapy Applications." Seminars in Radiation Oncology 32, no. 4 (October 2022): 304–18. http://dx.doi.org/10.1016/j.semradonc.2022.06.001.
Zheng, Rui, Shulin Zhang, Lei Liu, Yuhao Luo, and Mingzhai Sun. "Uncertainty in Bayesian deep label distribution learning." Applied Soft Computing 101 (March 2021): 107046. http://dx.doi.org/10.1016/j.asoc.2020.107046.
Lockwood, Owen, and Mei Si. "A Review of Uncertainty for Deep Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 18, no. 1 (October 11, 2022): 155–62. http://dx.doi.org/10.1609/aiide.v18i1.21959.
Karimi, Hamed, and Reza Samavi. "Quantifying Deep Learning Model Uncertainty in Conformal Prediction." Proceedings of the AAAI Symposium Series 1, no. 1 (October 3, 2023): 142–48. http://dx.doi.org/10.1609/aaaiss.v1i1.27492.
Caldeira, João, and Brian Nord. "Deeply uncertain: comparing methods of uncertainty quantification in deep learning algorithms." Machine Learning: Science and Technology 2, no. 1 (December 4, 2020): 015002. http://dx.doi.org/10.1088/2632-2153/aba6f3.
Da Silva, Felipe Leno, Pablo Hernandez-Leal, Bilal Kartal, and Matthew E. Taylor. "Uncertainty-Aware Action Advising for Deep Reinforcement Learning Agents." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 5792–99. http://dx.doi.org/10.1609/aaai.v34i04.6036.
Kawano, Yasufumi, Yoshiki Nota, Rinpei Mochizuki, and Yoshimitsu Aoki. "Non-Deep Active Learning for Deep Neural Networks." Sensors 22, no. 14 (July 13, 2022): 5244. http://dx.doi.org/10.3390/s22145244.
Gou, Xiaohong, and Xuenong He. "Deep Learning-Based Detection and Diagnosis of Subarachnoid Hemorrhage." Journal of Healthcare Engineering 2021 (November 22, 2021): 1–10. http://dx.doi.org/10.1155/2021/9639419.
Loftus, 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, no. 8 (August 10, 2022): e0000085. http://dx.doi.org/10.1371/journal.pdig.0000085.
Xu, Lei, Nengcheng Chen, Chao Yang, Hongchu Yu, and Zeqiang Chen. "Quantifying the uncertainty of precipitation forecasting using probabilistic deep learning." Hydrology and Earth System Sciences 26, no. 11 (June 14, 2022): 2923–38. http://dx.doi.org/10.5194/hess-26-2923-2022.
Pham, Nam, Sergey Fomel, and Dallas Dunlap. "Automatic channel detection using deep learning." Interpretation 7, no. 3 (August 1, 2019): SE43—SE50. http://dx.doi.org/10.1190/int-2018-0202.1.
Kabir, H. M. Dipu, Sadia Khanam, Fahime Khozeimeh, Abbas Khosravi, Subrota Kumar Mondal, Saeid Nahavandi, and U. Rajendra Acharya. "Aleatory-aware deep uncertainty quantification for transfer learning." Computers in Biology and Medicine 143 (April 2022): 105246. http://dx.doi.org/10.1016/j.compbiomed.2022.105246.
Morocho-Cayamcela, Manuel Eugenio, Martin Maier, and Wansu Lim. "Breaking Wireless Propagation Environmental Uncertainty With Deep Learning." IEEE Transactions on Wireless Communications 19, no. 8 (August 2020): 5075–87. http://dx.doi.org/10.1109/twc.2020.2986202.
Gude, Vinayaka, Steven Corns, and Suzanna Long. "Flood Prediction and Uncertainty Estimation Using Deep Learning." Water 12, no. 3 (March 21, 2020): 884. http://dx.doi.org/10.3390/w12030884.
Pei, Zhihao, Angela M. Rojas-Arevalo, Fjalar J. de Haan, Nir Lipovetzky, and Enayat A. Moallemi. "Reinforcement learning for decision-making under deep uncertainty." Journal of Environmental Management 359 (May 2024): 120968. http://dx.doi.org/10.1016/j.jenvman.2024.120968.
Peluso, 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 (January 2024): 104576. http://dx.doi.org/10.1016/j.jbi.2023.104576.
Murad, Abdulmajid, Frank Alexander Kraemer, Kerstin Bach, and Gavin Taylor. "Uncertainty-aware autonomous sensing with deep reinforcement learning." Future Generation Computer Systems 156 (July 2024): 242–53. http://dx.doi.org/10.1016/j.future.2024.03.021.
Yoon, Young-In, and Hye-Young Jeong. "A Comparison of Uncertainty Quantification of Deep Learning models for Time Series." Korean Data Analysis Society 26, no. 1 (February 29, 2024): 163–74. http://dx.doi.org/10.37727/jkdas.2024.26.1.163.
Bhatia, Abhinav, Pradeep Varakantham, and Akshat Kumar. "Resource Constrained Deep Reinforcement Learning." Proceedings of the International Conference on Automated Planning and Scheduling 29 (May 25, 2021): 610–20. http://dx.doi.org/10.1609/icaps.v29i1.3528.
Serpell, Cristián, Ignacio A. Araya, Carlos Valle, and Héctor Allende. "Addressing model uncertainty in probabilistic forecasting using Monte Carlo dropout." Intelligent Data Analysis 24 (December 4, 2020): 185–205. http://dx.doi.org/10.3233/ida-200015.
Silva, Felipe Leno Da, Pablo Hernandez-Leal, Bilal Kartal, and Matthew E. Taylor. "Providing Uncertainty-Based Advice for Deep Reinforcement Learning Agents (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 10 (April 3, 2020): 13913–14. http://dx.doi.org/10.1609/aaai.v34i10.7229.
Wang, Chun, and Jiquan Ma. "Uncertainty-Supervised Super-Resolution Deep Learning Network in Diffusion MRI." Highlights in Science, Engineering and Technology 45 (April 18, 2023): 7–10. http://dx.doi.org/10.54097/hset.v45i.7288.
Feng, Zhiyuan, Kai Qi, Bin Shi, Hao Mei, Qinghua Zheng, and Hua Wei. "Deep evidential learning in diffusion convolutional recurrent neural network." Electronic Research Archive 31, no. 4 (2023): 2252–64. http://dx.doi.org/10.3934/era.2023115.
Chaudhary, Priyanka, João P. Leitão, Tabea Donauer, Stefano D’Aronco, Nathanaël Perraudin, Guillaume Obozinski, Fernando Perez-Cruz, Konrad Schindler, Jan Dirk Wegner, and Stefania Russo. "Flood Uncertainty Estimation Using Deep Ensembles." Water 14, no. 19 (September 22, 2022): 2980. http://dx.doi.org/10.3390/w14192980.
Li, Xingjian, Pengkun Yang, Yangcheng Gu, Xueying Zhan, Tianyang Wang, Min Xu, and Chengzhong Xu. "Deep Active Learning with Noise Stability." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 12 (March 24, 2024): 13655–63. http://dx.doi.org/10.1609/aaai.v38i12.29270.
Hong, Ming, Jianzhuang Liu, Cuihua Li, and Yanyun Qu. "Uncertainty-Driven Dehazing Network." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 1 (June 28, 2022): 906–13. http://dx.doi.org/10.1609/aaai.v36i1.19973.
Kompa, Benjamin, Jasper Snoek, and Andrew L. Beam. "Empirical Frequentist Coverage of Deep Learning Uncertainty Quantification Procedures." Entropy 23, no. 12 (November 30, 2021): 1608. http://dx.doi.org/10.3390/e23121608.
Yu, Yang, Danruo Deng, Furui Liu, Qi Dou, Yueming Jin, Guangyong Chen, and Pheng Ann Heng. "ANEDL: Adaptive Negative Evidential Deep Learning for Open-Set Semi-supervised Learning." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 15 (March 24, 2024): 16587–95. http://dx.doi.org/10.1609/aaai.v38i15.29597.
Klotz, Daniel, Frederik Kratzert, Martin Gauch, Alden Keefe Sampson, Johannes Brandstetter, Günter Klambauer, Sepp Hochreiter, and Grey Nearing. "Uncertainty estimation with deep learning for rainfall–runoff modeling." Hydrology and Earth System Sciences 26, no. 6 (March 31, 2022): 1673–93. http://dx.doi.org/10.5194/hess-26-1673-2022.
Lv, Xiaoming, Fajie Duan, Jia-Jia Jiang, Xiao Fu, and Lin Gan. "Deep Active Learning for Surface Defect Detection." Sensors 20, no. 6 (March 16, 2020): 1650. http://dx.doi.org/10.3390/s20061650.
Bi, Wei, Wenhua Chen, and Jun Pan. "Multidisciplinary Reliability Design Considering Hybrid Uncertainty Incorporating Deep Learning." Wireless Communications and Mobile Computing 2022 (November 18, 2022): 1–11. http://dx.doi.org/10.1155/2022/5846684.
Cifci, Mehmet Akif. "A Deep Learning-Based Framework for Uncertainty Quantification in Medical Imaging Using the DropWeak Technique: An Empirical Study with Baresnet." Diagnostics 13, no. 4 (February 20, 2023): 800. http://dx.doi.org/10.3390/diagnostics13040800.
Kim, Mingyu, and Donghyun Lee. "Why Uncertainty in Deep Learning for Traffic Flow Prediction Is Needed." Sustainability 15, no. 23 (November 22, 2023): 16204. http://dx.doi.org/10.3390/su152316204.
Maged, Ahmed, and Min Xie. "Uncertainty utilization in fault detection using Bayesian deep learning." Journal of Manufacturing Systems 64 (July 2022): 316–29. http://dx.doi.org/10.1016/j.jmsy.2022.07.002.
Feng, Shijie, Chao Zuo, Yan Hu, Yixuan Li, and Qian Chen. "Deep-learning-based fringe-pattern analysis with uncertainty estimation." Optica 8, no. 12 (November 23, 2021): 1507. http://dx.doi.org/10.1364/optica.434311.
Loquercio, Antonio, Mattia Segu, and Davide Scaramuzza. "A General Framework for Uncertainty Estimation in Deep Learning." IEEE Robotics and Automation Letters 5, no. 2 (April 2020): 3153–60. http://dx.doi.org/10.1109/lra.2020.2974682.
Qin, Yu, Zhiwen Liu, Chenghao Liu, Yuxing Li, Xiangzhu Zeng, and Chuyang Ye. "Super-Resolved q-Space deep learning with uncertainty quantification." Medical Image Analysis 67 (January 2021): 101885. http://dx.doi.org/10.1016/j.media.2020.101885.
Peng, Weiwen, Zhi-Sheng Ye, and Nan Chen. "Bayesian Deep-Learning-Based Health Prognostics Toward Prognostics Uncertainty." IEEE Transactions on Industrial Electronics 67, no. 3 (March 2020): 2283–93. http://dx.doi.org/10.1109/tie.2019.2907440.
Xue, Yujia, Shiyi Cheng, Yunzhe Li, and Lei Tian. "Reliable deep-learning-based phase imaging with uncertainty quantification." Optica 6, no. 5 (May 7, 2019): 618. http://dx.doi.org/10.1364/optica.6.000618.
Abdullah, Abdullah A., Masoud M. Hassan, and Yaseen T. Mustafa. "Uncertainty Quantification for MLP-Mixer Using Bayesian Deep Learning." Applied Sciences 13, no. 7 (April 3, 2023): 4547. http://dx.doi.org/10.3390/app13074547.
Habibpour, Maryam, Hassan Gharoun, Mohammadreza Mehdipour, AmirReza Tajally, Hamzeh Asgharnezhad, Afshar Shamsi, Abbas Khosravi, and Saeid Nahavandi. "Uncertainty-aware credit card fraud detection using deep learning." Engineering Applications of Artificial Intelligence 123 (August 2023): 106248. http://dx.doi.org/10.1016/j.engappai.2023.106248.
Das, Neha, Jonas Umlauft, Armin Lederer, Alexandre Capone, Thomas Beckers, and Sandra Hirche. "Deep Learning based Uncertainty Decomposition for Real-time Control." IFAC-PapersOnLine 56, no. 2 (2023): 847–53. http://dx.doi.org/10.1016/j.ifacol.2023.10.1671.
Koh, D., A. Mishra, and K. Terao. "Deep neural network uncertainty quantification for LArTPC reconstruction." Journal of Instrumentation 18, no. 12 (December 1, 2023): P12013. http://dx.doi.org/10.1088/1748-0221/18/12/p12013.
Murad, Abdulmajid, Frank Alexander Kraemer, Kerstin Bach, and Gavin Taylor. "Probabilistic Deep Learning to Quantify Uncertainty in Air Quality Forecasting." Sensors 21, no. 23 (November 30, 2021): 8009. http://dx.doi.org/10.3390/s21238009.
Aldhahi, Waleed, and Sanghoon Sull. "Uncertain-CAM: Uncertainty-Based Ensemble Machine Voting for Improved COVID-19 CXR Classification and Explainability." Diagnostics 13, no. 3 (January 26, 2023): 441. http://dx.doi.org/10.3390/diagnostics13030441.
Ji, Ying, Jianhui Wang, Jiacan Xu, Xiaoke Fang, and Huaguang Zhang. "Real-Time Energy Management of a Microgrid Using Deep Reinforcement Learning." Energies 12, no. 12 (June 15, 2019): 2291. http://dx.doi.org/10.3390/en12122291.
Ji, Ying, Jianhui Wang, Jiacan Xu, and Donglin Li. "Data-Driven Online Energy Scheduling of a Microgrid Based on Deep Reinforcement Learning." Energies 14, no. 8 (April 10, 2021): 2120. http://dx.doi.org/10.3390/en14082120.