Artykuły w czasopismach na temat „Probabilistic deep models”
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Masegosa, Andrés R., Rafael Cabañas, Helge Langseth, Thomas D. Nielsen i Antonio Salmerón. "Probabilistic Models with Deep Neural Networks". Entropy 23, nr 1 (18.01.2021): 117. http://dx.doi.org/10.3390/e23010117.
Pełny tekst źródłaVillanueva Llerena, Julissa, i Denis Deratani Maua. "Efficient Predictive Uncertainty Estimators for Deep Probabilistic Models". Proceedings of the AAAI Conference on Artificial Intelligence 34, nr 10 (3.04.2020): 13740–41. http://dx.doi.org/10.1609/aaai.v34i10.7142.
Pełny tekst źródłaKarami, Mahdi, i Dale Schuurmans. "Deep Probabilistic Canonical Correlation Analysis". Proceedings of the AAAI Conference on Artificial Intelligence 35, nr 9 (18.05.2021): 8055–63. http://dx.doi.org/10.1609/aaai.v35i9.16982.
Pełny tekst źródłaLu, Ming, Zhihao Duan, Fengqing Zhu i Zhan Ma. "Deep Hierarchical Video Compression". Proceedings of the AAAI Conference on Artificial Intelligence 38, nr 8 (24.03.2024): 8859–67. http://dx.doi.org/10.1609/aaai.v38i8.28733.
Pełny tekst źródłaMaroñas, Juan, Roberto Paredes i Daniel Ramos. "Calibration of deep probabilistic models with decoupled bayesian neural networks". Neurocomputing 407 (wrzesień 2020): 194–205. http://dx.doi.org/10.1016/j.neucom.2020.04.103.
Pełny tekst źródłaLi, Zhenjun, Xi Liu, Dawei Kou, Yi Hu, Qingrui Zhang i Qingxi Yuan. "Probabilistic Models for the Shear Strength of RC Deep Beams". Applied Sciences 13, nr 8 (12.04.2023): 4853. http://dx.doi.org/10.3390/app13084853.
Pełny tekst źródłaSerpell, Cristián, Ignacio A. Araya, Carlos Valle i Héctor Allende. "Addressing model uncertainty in probabilistic forecasting using Monte Carlo dropout". Intelligent Data Analysis 24 (4.12.2020): 185–205. http://dx.doi.org/10.3233/ida-200015.
Pełny tekst źródłaBoursin, Nicolas, Carl Remlinger i Joseph Mikael. "Deep Generators on Commodity Markets Application to Deep Hedging". Risks 11, nr 1 (23.12.2022): 7. http://dx.doi.org/10.3390/risks11010007.
Pełny tekst źródłaZuidberg Dos Martires, Pedro. "Probabilistic Neural Circuits". Proceedings of the AAAI Conference on Artificial Intelligence 38, nr 15 (24.03.2024): 17280–89. http://dx.doi.org/10.1609/aaai.v38i15.29675.
Pełny tekst źródłaRavuri, Suman, Karel Lenc, Matthew Willson, Dmitry Kangin, Remi Lam, Piotr Mirowski, Megan Fitzsimons i in. "Skilful precipitation nowcasting using deep generative models of radar". Nature 597, nr 7878 (29.09.2021): 672–77. http://dx.doi.org/10.1038/s41586-021-03854-z.
Pełny tekst źródłaAdams, Jadie. "Probabilistic Shape Models of Anatomy Directly from Images". Proceedings of the AAAI Conference on Artificial Intelligence 37, nr 13 (26.06.2023): 16107–8. http://dx.doi.org/10.1609/aaai.v37i13.26914.
Pełny tekst źródłaQian, Weizhu, Fabrice Lauri i Franck Gechter. "Supervised and semi-supervised deep probabilistic models for indoor positioning problems". Neurocomputing 435 (maj 2021): 228–38. http://dx.doi.org/10.1016/j.neucom.2020.12.131.
Pełny tekst źródłaSinha, Mourani, Mrinmoyee Bhattacharya, M. Seemanth i Suchandra A. Bhowmick. "Probabilistic Models and Deep Learning Models Assessed to Estimate Design and Operational Ocean Wave Statistics to Reduce Coastal Hazards". Geosciences 13, nr 12 (12.12.2023): 380. http://dx.doi.org/10.3390/geosciences13120380.
Pełny tekst źródłaAndrianomena, Sambatra. "Probabilistic learning for pulsar classification". Journal of Cosmology and Astroparticle Physics 2022, nr 10 (1.10.2022): 016. http://dx.doi.org/10.1088/1475-7516/2022/10/016.
Pełny tekst źródłaD’Andrea, Fabio, Pierre Gentine, Alan K. Betts i Benjamin R. Lintner. "Triggering Deep Convection with a Probabilistic Plume Model". Journal of the Atmospheric Sciences 71, nr 11 (29.10.2014): 3881–901. http://dx.doi.org/10.1175/jas-d-13-0340.1.
Pełny tekst źródłaMurad, Abdulmajid, Frank Alexander Kraemer, Kerstin Bach i Gavin Taylor. "Probabilistic Deep Learning to Quantify Uncertainty in Air Quality Forecasting". Sensors 21, nr 23 (30.11.2021): 8009. http://dx.doi.org/10.3390/s21238009.
Pełny tekst źródłaBuda-Ożóg, Lidia. "Probabilistic assessment of load-bearing capacity of deep beams designed by strut-and-tie method". MATEC Web of Conferences 262 (2019): 08001. http://dx.doi.org/10.1051/matecconf/201926208001.
Pełny tekst źródłaDuan, Yun. "A Novel Interval Energy-Forecasting Method for Sustainable Building Management Based on Deep Learning". Sustainability 14, nr 14 (13.07.2022): 8584. http://dx.doi.org/10.3390/su14148584.
Pełny tekst źródłaMashlakov, Aleksei, Toni Kuronen, Lasse Lensu, Arto Kaarna i Samuli Honkapuro. "Assessing the performance of deep learning models for multivariate probabilistic energy forecasting". Applied Energy 285 (marzec 2021): 116405. http://dx.doi.org/10.1016/j.apenergy.2020.116405.
Pełny tekst źródłaLiu, Mao-Yi, Zheng Li i Hang Zhang. "Probabilistic Shear Strength Prediction for Deep Beams Based on Bayesian-Optimized Data-Driven Approach". Buildings 13, nr 10 (28.09.2023): 2471. http://dx.doi.org/10.3390/buildings13102471.
Pełny tekst źródłaNye, Logan, Hamid Ghaednia i Joseph H. Schwab. "Generating synthetic samples of chondrosarcoma histopathology with a denoising diffusion probabilistic model." Journal of Clinical Oncology 41, nr 16_suppl (1.06.2023): e13592-e13592. http://dx.doi.org/10.1200/jco.2023.41.16_suppl.e13592.
Pełny tekst źródłaBentivoglio, Roberto, Elvin Isufi, Sebastian Nicolaas Jonkman i Riccardo Taormina. "Deep learning methods for flood mapping: a review of existing applications and future research directions". Hydrology and Earth System Sciences 26, nr 16 (25.08.2022): 4345–78. http://dx.doi.org/10.5194/hess-26-4345-2022.
Pełny tekst źródłaEdie, Stewart M., Peter D. Smits i David Jablonski. "Probabilistic models of species discovery and biodiversity comparisons". Proceedings of the National Academy of Sciences 114, nr 14 (21.03.2017): 3666–71. http://dx.doi.org/10.1073/pnas.1616355114.
Pełny tekst źródłaAvaylon, Matthew, Robbie Sadre, Zhe Bai i Talita Perciano. "Adaptable Deep Learning and Probabilistic Graphical Model System for Semantic Segmentation". Advances in Artificial Intelligence and Machine Learning 02, nr 01 (2022): 288–302. http://dx.doi.org/10.54364/aaiml.2022.1119.
Pełny tekst źródłaSansine, Vateanui, Pascal Ortega, Daniel Hissel i Franco Ferrucci. "Hybrid Deep Learning Model for Mean Hourly Irradiance Probabilistic Forecasting". Atmosphere 14, nr 7 (24.07.2023): 1192. http://dx.doi.org/10.3390/atmos14071192.
Pełny tekst źródłaHou, Yuxin, Ari Heljakka i Arno Solin. "Gaussian Process Priors for View-Aware Inference". Proceedings of the AAAI Conference on Artificial Intelligence 35, nr 9 (18.05.2021): 7762–70. http://dx.doi.org/10.1609/aaai.v35i9.16948.
Pełny tekst źródłaNguyen, Minh Truong, Viet-Hung Dang i Truong-Thang Nguyen. "Applying Bayesian neural network to evaluate the influence of specialized mini projects on final performance of engineering students: A case study". Ministry of Science and Technology, Vietnam 64, nr 4 (15.12.2022): 10–15. http://dx.doi.org/10.31276/vjste.64(4).10-15.
Pełny tekst źródłaNor, Ahmad Kamal Mohd. "Failure Prognostic of Turbofan Engines with Uncertainty Quantification and Explainable AI (XIA)". Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, nr 3 (11.04.2021): 3494–504. http://dx.doi.org/10.17762/turcomat.v12i3.1624.
Pełny tekst źródłaGhobadi, Fatemeh, i Doosun Kang. "Multi-Step Ahead Probabilistic Forecasting of Daily Streamflow Using Bayesian Deep Learning: A Multiple Case Study". Water 14, nr 22 (14.11.2022): 3672. http://dx.doi.org/10.3390/w14223672.
Pełny tekst źródłaBentsen, Lars Ødegaard, Narada Dilp Warakagoda, Roy Stenbro i Paal Engelstad. "Probabilistic Wind Park Power Prediction using Bayesian Deep Learning and Generative Adversarial Networks". Journal of Physics: Conference Series 2362, nr 1 (1.11.2022): 012005. http://dx.doi.org/10.1088/1742-6596/2362/1/012005.
Pełny tekst źródłaLee, Taehee, Devin Rand, Lorraine E. Lisiecki, Geoffrey Gebbie i Charles Lawrence. "Bayesian age models and stacks: combining age inferences from radiocarbon and benthic δ18O stratigraphic alignment". Climate of the Past 19, nr 10 (17.10.2023): 1993–2012. http://dx.doi.org/10.5194/cp-19-1993-2023.
Pełny tekst źródłaLi, Longyuan, Jihai Zhang, Junchi Yan, Yaohui Jin, Yunhao Zhang, Yanjie Duan i Guangjian Tian. "Synergetic Learning of Heterogeneous Temporal Sequences for Multi-Horizon Probabilistic Forecasting". Proceedings of the AAAI Conference on Artificial Intelligence 35, nr 10 (18.05.2021): 8420–28. http://dx.doi.org/10.1609/aaai.v35i10.17023.
Pełny tekst źródłaPang, Bo, Erik Nijkamp i Ying Nian Wu. "Deep Learning With TensorFlow: A Review". Journal of Educational and Behavioral Statistics 45, nr 2 (10.09.2019): 227–48. http://dx.doi.org/10.3102/1076998619872761.
Pełny tekst źródłaLim, Heejong, Kwanghun Chung i Sangbok Lee. "Probabilistic Forecasting for Demand of a Bike-Sharing Service Using a Deep-Learning Approach". Sustainability 14, nr 23 (29.11.2022): 15889. http://dx.doi.org/10.3390/su142315889.
Pełny tekst źródłaBi, Wei, Wenhua Chen i Jun Pan. "Multidisciplinary Reliability Design Considering Hybrid Uncertainty Incorporating Deep Learning". Wireless Communications and Mobile Computing 2022 (18.11.2022): 1–11. http://dx.doi.org/10.1155/2022/5846684.
Pełny tekst źródłaT, Ermolieva, Ermoliev Y, Zagorodniy) A, Bogdanov V, Borodina O, Havlik P, Komendantova N, Knopov P, Gorbachuk V i Zaslavskyi V. "Artificial Intelligence, Machine Learning, and Intelligent Decision Support Systems: Iterative “Learning” SQG-based procedures for Distributed Models’ Linkage". Artificial Intelligence 27, AI.2022.27(2) (29.12.2022): 92–97. http://dx.doi.org/10.15407/jai2022.02.092.
Pełny tekst źródłaLiu, Xi, Tao Wu, Yuanyuan An i Yang Liu. "Probabilistic models of the strut efficiency factor for RC deep beams with MCMC method". Structural Concrete 21, nr 3 (22.01.2020): 917–33. http://dx.doi.org/10.1002/suco.201900249.
Pełny tekst źródłade Zarzà, I., J. de Curtò, Gemma Roig i Carlos T. Calafate. "LLM Multimodal Traffic Accident Forecasting". Sensors 23, nr 22 (16.11.2023): 9225. http://dx.doi.org/10.3390/s23229225.
Pełny tekst źródłaAli, Abdullah Marish, Fuad A. Ghaleb, Mohammed Sultan Mohammed, Fawaz Jaber Alsolami i Asif Irshad Khan. "Web-Informed-Augmented Fake News Detection Model Using Stacked Layers of Convolutional Neural Network and Deep Autoencoder". Mathematics 11, nr 9 (23.04.2023): 1992. http://dx.doi.org/10.3390/math11091992.
Pełny tekst źródłaChipofya, Mapopa, Hilal Tayara i Kil To Chong. "Deep Probabilistic Learning Model for Prediction of Ionic Liquids Toxicity". International Journal of Molecular Sciences 23, nr 9 (9.05.2022): 5258. http://dx.doi.org/10.3390/ijms23095258.
Pełny tekst źródłaMeng, Fan, Kunlin Yang, Yichen Yao, Zhibin Wang i Tao Song. "Tropical Cyclone Intensity Probabilistic Forecasting System Based on Deep Learning". International Journal of Intelligent Systems 2023 (18.03.2023): 1–17. http://dx.doi.org/10.1155/2023/3569538.
Pełny tekst źródłaPomponi, Jary, Simone Scardapane i Aurelio Uncini. "A Probabilistic Re-Intepretation of Confidence Scores in Multi-Exit Models". Entropy 24, nr 1 (21.12.2021): 1. http://dx.doi.org/10.3390/e24010001.
Pełny tekst źródłaZhong, Z., i M. Mehltretter. "MIXED PROBABILITY MODELS FOR ALEATORIC UNCERTAINTY ESTIMATION IN THE CONTEXT OF DENSE STEREO MATCHING". ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-2-2021 (17.06.2021): 17–26. http://dx.doi.org/10.5194/isprs-annals-v-2-2021-17-2021.
Pełny tekst źródłaShao, Mingyue, Wei Song i Xiaobing Zhao. "Polymetallic Nodule Resource Assessment of Seabed Photography Based on Denoising Diffusion Probabilistic Models". Journal of Marine Science and Engineering 11, nr 8 (27.07.2023): 1494. http://dx.doi.org/10.3390/jmse11081494.
Pełny tekst źródłaXu, Duo, Jonathan C. Tan, Chia-Jung Hsu i Ye Zhu. "Denoising Diffusion Probabilistic Models to Predict the Density of Molecular Clouds". Astrophysical Journal 950, nr 2 (1.06.2023): 146. http://dx.doi.org/10.3847/1538-4357/accae5.
Pełny tekst źródłaPandarinathan, Mr, S. Velan i S. Deepak. "Human Emotion Detection Using Deep Learning". International Journal for Research in Applied Science and Engineering Technology 11, nr 5 (31.05.2023): 2225–29. http://dx.doi.org/10.22214/ijraset.2023.52016.
Pełny tekst źródłaCandela, Alberto, David R. Thompson, David Wettergreen, Kerry Cawse-Nicholson, Sven Geier, Michael L. Eastwood i Robert O. Green. "Probabilistic Super Resolution for Mineral Spectroscopy". Proceedings of the AAAI Conference on Artificial Intelligence 34, nr 08 (3.04.2020): 13241–47. http://dx.doi.org/10.1609/aaai.v34i08.7030.
Pełny tekst źródłaM. Rajalakshmi i V. Sulochana. "Enhancing deep learning model performance in air quality classification through probabilistic hyperparameter tuning with tree-structured parzen estimators". Scientific Temper 14, nr 04 (30.12.2023): 1244–50. http://dx.doi.org/10.58414/scientifictemper.2023.14.4.27.
Pełny tekst źródłaTürkmen, Ali Caner, Tim Januschowski, Yuyang Wang i Ali Taylan Cemgil. "Forecasting intermittent and sparse time series: A unified probabilistic framework via deep renewal processes". PLOS ONE 16, nr 11 (29.11.2021): e0259764. http://dx.doi.org/10.1371/journal.pone.0259764.
Pełny tekst źródłaLi, Zhanli, Xinyu Zhang, Fan Deng i Yun Zhang. "Integrating deep neural network with logic rules for credit scoring". Intelligent Data Analysis 27, nr 2 (15.03.2023): 483–500. http://dx.doi.org/10.3233/ida-216460.
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