Artículos de revistas sobre el tema "Deep Discriminative Probabilistic Models"
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Kamran, Fahad y Jenna Wiens. "Estimating Calibrated Individualized Survival Curves with Deep Learning". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 1 (18 de mayo de 2021): 240–48. http://dx.doi.org/10.1609/aaai.v35i1.16098.
Texto completoAl Moubayed, Noura, Stephen McGough y Bashar Awwad Shiekh Hasan. "Beyond the topics: how deep learning can improve the discriminability of probabilistic topic modelling". PeerJ Computer Science 6 (27 de enero de 2020): e252. http://dx.doi.org/10.7717/peerj-cs.252.
Texto completoBhattacharya, Debswapna. "refineD: improved protein structure refinement using machine learning based restrained relaxation". Bioinformatics 35, n.º 18 (13 de febrero de 2019): 3320–28. http://dx.doi.org/10.1093/bioinformatics/btz101.
Texto completoWu, Boxi, Jie Jiang, Haidong Ren, Zifan Du, Wenxiao Wang, Zhifeng Li, Deng Cai, Xiaofei He, Binbin Lin y Wei Liu. "Towards In-Distribution Compatible Out-of-Distribution Detection". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 9 (26 de junio de 2023): 10333–41. http://dx.doi.org/10.1609/aaai.v37i9.26230.
Texto completoRoy, Debaditya, Sarunas Girdzijauskas y Serghei Socolovschi. "Confidence-Calibrated Human Activity Recognition". Sensors 21, n.º 19 (30 de septiembre de 2021): 6566. http://dx.doi.org/10.3390/s21196566.
Texto completoTsuda, Koji, Motoaki Kawanabe, Gunnar Rätsch, Sören Sonnenburg y Klaus-Robert Müller. "A New Discriminative Kernel from Probabilistic Models". Neural Computation 14, n.º 10 (1 de octubre de 2002): 2397–414. http://dx.doi.org/10.1162/08997660260293274.
Texto completoAhmed, Nisar y Mark Campbell. "On estimating simple probabilistic discriminative models with subclasses". Expert Systems with Applications 39, n.º 7 (junio de 2012): 6659–64. http://dx.doi.org/10.1016/j.eswa.2011.12.042.
Texto completoDu, Fang, Jiangshe Zhang, Junying Hu y Rongrong Fei. "Discriminative multi-modal deep generative models". Knowledge-Based Systems 173 (junio de 2019): 74–82. http://dx.doi.org/10.1016/j.knosys.2019.02.023.
Texto completoChe, Tong, Xiaofeng Liu, Site Li, Yubin Ge, Ruixiang Zhang, Caiming Xiong y Yoshua Bengio. "Deep Verifier Networks: Verification of Deep Discriminative Models with Deep Generative Models". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 8 (18 de mayo de 2021): 7002–10. http://dx.doi.org/10.1609/aaai.v35i8.16862.
Texto completoMasegosa, Andrés R., Rafael Cabañas, Helge Langseth, Thomas D. Nielsen y Antonio Salmerón. "Probabilistic Models with Deep Neural Networks". Entropy 23, n.º 1 (18 de enero de 2021): 117. http://dx.doi.org/10.3390/e23010117.
Texto completoJong Kyoung Kim y Seungjin Choi. "Probabilistic Models for Semisupervised Discriminative Motif Discovery in DNA Sequences". IEEE/ACM Transactions on Computational Biology and Bioinformatics 8, n.º 5 (septiembre de 2011): 1309–17. http://dx.doi.org/10.1109/tcbb.2010.84.
Texto completoFang, Yi, Luo Si y Aditya P. Mathur. "Discriminative probabilistic models for expert search in heterogeneous information sources". Information Retrieval 14, n.º 2 (21 de agosto de 2010): 158–77. http://dx.doi.org/10.1007/s10791-010-9139-3.
Texto completoAhmed, Nisar y Mark Campbell. "Variational Bayesian Learning of Probabilistic Discriminative Models With Latent Softmax Variables". IEEE Transactions on Signal Processing 59, n.º 7 (julio de 2011): 3143–54. http://dx.doi.org/10.1109/tsp.2011.2144587.
Texto completoWu, Ying Nian, Ruiqi Gao, Tian Han y Song-Chun Zhu. "A tale of three probabilistic families: Discriminative, descriptive, and generative models". Quarterly of Applied Mathematics 77, n.º 2 (31 de diciembre de 2018): 423–65. http://dx.doi.org/10.1090/qam/1528.
Texto completoQin, Huafeng y Peng Wang. "Finger-Vein Verification Based on LSTM Recurrent Neural Networks". Applied Sciences 9, n.º 8 (24 de abril de 2019): 1687. http://dx.doi.org/10.3390/app9081687.
Texto completoVillanueva Llerena, Julissa y Denis Deratani Maua. "Efficient Predictive Uncertainty Estimators for Deep Probabilistic Models". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 10 (3 de abril de 2020): 13740–41. http://dx.doi.org/10.1609/aaai.v34i10.7142.
Texto completoChu, Joseph Lin y Adam Krzyźak. "The Recognition Of Partially Occluded Objects with Support Vector Machines, Convolutional Neural Networks and Deep Belief Networks". Journal of Artificial Intelligence and Soft Computing Research 4, n.º 1 (1 de enero de 2014): 5–19. http://dx.doi.org/10.2478/jaiscr-2014-0021.
Texto completoWang, Liwei, Xiong Li, Zhuowen Tu y Jiaya Jia. "Discriminative Clustering via Generative Feature Mapping". Proceedings of the AAAI Conference on Artificial Intelligence 26, n.º 1 (20 de septiembre de 2021): 1162–68. http://dx.doi.org/10.1609/aaai.v26i1.8305.
Texto completoBuscombe, Daniel y Paul Grams. "Probabilistic Substrate Classification with Multispectral Acoustic Backscatter: A Comparison of Discriminative and Generative Models". Geosciences 8, n.º 11 (30 de octubre de 2018): 395. http://dx.doi.org/10.3390/geosciences8110395.
Texto completoLuo, You-Wei, Chuan-Xian Ren, Pengfei Ge, Ke-Kun Huang y Yu-Feng Yu. "Unsupervised Domain Adaptation via Discriminative Manifold Embedding and Alignment". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 04 (3 de abril de 2020): 5029–36. http://dx.doi.org/10.1609/aaai.v34i04.5943.
Texto completoKarami, Mahdi y Dale Schuurmans. "Deep Probabilistic Canonical Correlation Analysis". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 9 (18 de mayo de 2021): 8055–63. http://dx.doi.org/10.1609/aaai.v35i9.16982.
Texto completoCui, Bo, Guyue Hu y Shan Yu. "DeepCollaboration: Collaborative Generative and Discriminative Models for Class Incremental Learning". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 2 (18 de mayo de 2021): 1175–83. http://dx.doi.org/10.1609/aaai.v35i2.16204.
Texto completoGordon, Jonathan y José Miguel Hernández-Lobato. "Combining deep generative and discriminative models for Bayesian semi-supervised learning". Pattern Recognition 100 (abril de 2020): 107156. http://dx.doi.org/10.1016/j.patcog.2019.107156.
Texto completoBai, Wenjun, Changqin Quan y Zhi-Wei Luo. "Improving Generative and Discriminative Modelling Performance by Implementing Learning Constraints in Encapsulated Variational Autoencoders". Applied Sciences 9, n.º 12 (21 de junio de 2019): 2551. http://dx.doi.org/10.3390/app9122551.
Texto completoLi, Fuqiang, Tongzhuang Zhang, Yong Liu y Feiqi Long. "Deep Residual Vector Encoding for Vein Recognition". Electronics 11, n.º 20 (13 de octubre de 2022): 3300. http://dx.doi.org/10.3390/electronics11203300.
Texto completoHu, Gang, Chahna Dixit y Guanqiu Qi. "Discriminative Shape Feature Pooling in Deep Neural Networks". Journal of Imaging 8, n.º 5 (20 de abril de 2022): 118. http://dx.doi.org/10.3390/jimaging8050118.
Texto completoCoto-Jiménez, Marvin. "Discriminative Multi-Stream Postfilters Based on Deep Learning for Enhancing Statistical Parametric Speech Synthesis". Biomimetics 6, n.º 1 (7 de febrero de 2021): 12. http://dx.doi.org/10.3390/biomimetics6010012.
Texto completoAdedigba, Adeyinka P., Steve A. Adeshina y Abiodun M. Aibinu. "Performance Evaluation of Deep Learning Models on Mammogram Classification Using Small Dataset". Bioengineering 9, n.º 4 (6 de abril de 2022): 161. http://dx.doi.org/10.3390/bioengineering9040161.
Texto completoAlshazly, Hammam, Christoph Linse, Erhardt Barth y Thomas Martinetz. "Ensembles of Deep Learning Models and Transfer Learning for Ear Recognition". Sensors 19, n.º 19 (24 de septiembre de 2019): 4139. http://dx.doi.org/10.3390/s19194139.
Texto completoMaroñas, Juan, Roberto Paredes y Daniel Ramos. "Calibration of deep probabilistic models with decoupled bayesian neural networks". Neurocomputing 407 (septiembre de 2020): 194–205. http://dx.doi.org/10.1016/j.neucom.2020.04.103.
Texto completoLi, Zhenjun, Xi Liu, Dawei Kou, Yi Hu, Qingrui Zhang y Qingxi Yuan. "Probabilistic Models for the Shear Strength of RC Deep Beams". Applied Sciences 13, n.º 8 (12 de abril de 2023): 4853. http://dx.doi.org/10.3390/app13084853.
Texto completoLiu, Shengyi. "Model Extraction Attack and Defense on Deep Generative Models". Journal of Physics: Conference Series 2189, n.º 1 (1 de febrero de 2022): 012024. http://dx.doi.org/10.1088/1742-6596/2189/1/012024.
Texto completoBai, Shuang. "Scene Categorization Through Using Objects Represented by Deep Features". International Journal of Pattern Recognition and Artificial Intelligence 31, n.º 09 (febrero de 2017): 1755013. http://dx.doi.org/10.1142/s0218001417550138.
Texto completoKumar, Parmod, D. Suganthi, K. Valarmathi, Mahendra Pratap Swain, Piyush Vashistha, Dharam Buddhi y Emmanuel Sey. "A Multi-Thresholding-Based Discriminative Neural Classifier for Detection of Retinoblastoma Using CNN Models". BioMed Research International 2023 (6 de febrero de 2023): 1–9. http://dx.doi.org/10.1155/2023/5803661.
Texto completoYu, Hee-Jin, Chang-Hwan Son y Dong Hyuk Lee. "Apple Leaf Disease Identification Through Region-of-Interest-Aware Deep Convolutional Neural Network". Journal of Imaging Science and Technology 64, n.º 2 (1 de marzo de 2020): 20507–1. http://dx.doi.org/10.2352/j.imagingsci.technol.2020.64.2.020507.
Texto completoBoursin, Nicolas, Carl Remlinger y Joseph Mikael. "Deep Generators on Commodity Markets Application to Deep Hedging". Risks 11, n.º 1 (23 de diciembre de 2022): 7. http://dx.doi.org/10.3390/risks11010007.
Texto completoD’Andrea, Fabio, Pierre Gentine, Alan K. Betts y Benjamin R. Lintner. "Triggering Deep Convection with a Probabilistic Plume Model". Journal of the Atmospheric Sciences 71, n.º 11 (29 de octubre de 2014): 3881–901. http://dx.doi.org/10.1175/jas-d-13-0340.1.
Texto completoSerpell, Cristián, Ignacio A. Araya, Carlos Valle y Héctor Allende. "Addressing model uncertainty in probabilistic forecasting using Monte Carlo dropout". Intelligent Data Analysis 24 (4 de diciembre de 2020): 185–205. http://dx.doi.org/10.3233/ida-200015.
Texto completoQian, Weizhu, Fabrice Lauri y Franck Gechter. "Supervised and semi-supervised deep probabilistic models for indoor positioning problems". Neurocomputing 435 (mayo de 2021): 228–38. http://dx.doi.org/10.1016/j.neucom.2020.12.131.
Texto completoWang, Wenzheng, Yuqi Han, Chenwei Deng y Zhen Li. "Hyperspectral Image Classification via Deep Structure Dictionary Learning". Remote Sensing 14, n.º 9 (8 de mayo de 2022): 2266. http://dx.doi.org/10.3390/rs14092266.
Texto completoAndrianomena, Sambatra. "Probabilistic learning for pulsar classification". Journal of Cosmology and Astroparticle Physics 2022, n.º 10 (1 de octubre de 2022): 016. http://dx.doi.org/10.1088/1475-7516/2022/10/016.
Texto completoKim, Hyesuk y Incheol Kim. "Dynamic Arm Gesture Recognition Using Spherical Angle Features and Hidden Markov Models". Advances in Human-Computer Interaction 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/785349.
Texto completoMurad, Abdulmajid, Frank Alexander Kraemer, Kerstin Bach y Gavin Taylor. "Probabilistic Deep Learning to Quantify Uncertainty in Air Quality Forecasting". Sensors 21, n.º 23 (30 de noviembre de 2021): 8009. http://dx.doi.org/10.3390/s21238009.
Texto completoAdams, Jadie. "Probabilistic Shape Models of Anatomy Directly from Images". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 13 (26 de junio de 2023): 16107–8. http://dx.doi.org/10.1609/aaai.v37i13.26914.
Texto completoRavuri, Suman, Karel Lenc, Matthew Willson, Dmitry Kangin, Remi Lam, Piotr Mirowski, Megan Fitzsimons et al. "Skilful precipitation nowcasting using deep generative models of radar". Nature 597, n.º 7878 (29 de septiembre de 2021): 672–77. http://dx.doi.org/10.1038/s41586-021-03854-z.
Texto completoHuang, Jiabo, Qi Dong, Shaogang Gong y Xiatian Zhu. "Unsupervised Deep Learning via Affinity Diffusion". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 07 (3 de abril de 2020): 11029–36. http://dx.doi.org/10.1609/aaai.v34i07.6757.
Texto completoCollins, Michael y Terry Koo. "Discriminative Reranking for Natural Language Parsing". Computational Linguistics 31, n.º 1 (marzo de 2005): 25–70. http://dx.doi.org/10.1162/0891201053630273.
Texto completoMashlakov, Aleksei, Toni Kuronen, Lasse Lensu, Arto Kaarna y Samuli Honkapuro. "Assessing the performance of deep learning models for multivariate probabilistic energy forecasting". Applied Energy 285 (marzo de 2021): 116405. http://dx.doi.org/10.1016/j.apenergy.2020.116405.
Texto completoDuan, Yun. "A Novel Interval Energy-Forecasting Method for Sustainable Building Management Based on Deep Learning". Sustainability 14, n.º 14 (13 de julio de 2022): 8584. http://dx.doi.org/10.3390/su14148584.
Texto completoKrogh, Anders y Søren Kamaric Riis. "Hidden Neural Networks". Neural Computation 11, n.º 2 (1 de febrero de 1999): 541–63. http://dx.doi.org/10.1162/089976699300016764.
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