Статті в журналах з теми "Deep Discriminative Probabilistic Models"
Оформте джерело за APA, MLA, Chicago, Harvard та іншими стилями
Ознайомтеся з топ-50 статей у журналах для дослідження на тему "Deep Discriminative Probabilistic Models".
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.
Kamran, Fahad, and Jenna Wiens. "Estimating Calibrated Individualized Survival Curves with Deep Learning." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 1 (May 18, 2021): 240–48. http://dx.doi.org/10.1609/aaai.v35i1.16098.
Повний текст джерелаAl Moubayed, Noura, Stephen McGough, and Bashar Awwad Shiekh Hasan. "Beyond the topics: how deep learning can improve the discriminability of probabilistic topic modelling." PeerJ Computer Science 6 (January 27, 2020): e252. http://dx.doi.org/10.7717/peerj-cs.252.
Повний текст джерелаBhattacharya, Debswapna. "refineD: improved protein structure refinement using machine learning based restrained relaxation." Bioinformatics 35, no. 18 (February 13, 2019): 3320–28. http://dx.doi.org/10.1093/bioinformatics/btz101.
Повний текст джерелаWu, Boxi, Jie Jiang, Haidong Ren, Zifan Du, Wenxiao Wang, Zhifeng Li, Deng Cai, Xiaofei He, Binbin Lin, and Wei Liu. "Towards In-Distribution Compatible Out-of-Distribution Detection." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 9 (June 26, 2023): 10333–41. http://dx.doi.org/10.1609/aaai.v37i9.26230.
Повний текст джерелаRoy, Debaditya, Sarunas Girdzijauskas, and Serghei Socolovschi. "Confidence-Calibrated Human Activity Recognition." Sensors 21, no. 19 (September 30, 2021): 6566. http://dx.doi.org/10.3390/s21196566.
Повний текст джерелаTsuda, Koji, Motoaki Kawanabe, Gunnar Rätsch, Sören Sonnenburg, and Klaus-Robert Müller. "A New Discriminative Kernel from Probabilistic Models." Neural Computation 14, no. 10 (October 1, 2002): 2397–414. http://dx.doi.org/10.1162/08997660260293274.
Повний текст джерелаAhmed, Nisar, and Mark Campbell. "On estimating simple probabilistic discriminative models with subclasses." Expert Systems with Applications 39, no. 7 (June 2012): 6659–64. http://dx.doi.org/10.1016/j.eswa.2011.12.042.
Повний текст джерелаDu, Fang, Jiangshe Zhang, Junying Hu, and Rongrong Fei. "Discriminative multi-modal deep generative models." Knowledge-Based Systems 173 (June 2019): 74–82. http://dx.doi.org/10.1016/j.knosys.2019.02.023.
Повний текст джерелаChe, Tong, Xiaofeng Liu, Site Li, Yubin Ge, Ruixiang Zhang, Caiming Xiong, and Yoshua Bengio. "Deep Verifier Networks: Verification of Deep Discriminative Models with Deep Generative Models." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 8 (May 18, 2021): 7002–10. http://dx.doi.org/10.1609/aaai.v35i8.16862.
Повний текст джерелаMasegosa, Andrés R., Rafael Cabañas, Helge Langseth, Thomas D. Nielsen, and Antonio Salmerón. "Probabilistic Models with Deep Neural Networks." Entropy 23, no. 1 (January 18, 2021): 117. http://dx.doi.org/10.3390/e23010117.
Повний текст джерелаJong Kyoung Kim and Seungjin Choi. "Probabilistic Models for Semisupervised Discriminative Motif Discovery in DNA Sequences." IEEE/ACM Transactions on Computational Biology and Bioinformatics 8, no. 5 (September 2011): 1309–17. http://dx.doi.org/10.1109/tcbb.2010.84.
Повний текст джерелаFang, Yi, Luo Si, and Aditya P. Mathur. "Discriminative probabilistic models for expert search in heterogeneous information sources." Information Retrieval 14, no. 2 (August 21, 2010): 158–77. http://dx.doi.org/10.1007/s10791-010-9139-3.
Повний текст джерелаAhmed, Nisar, and Mark Campbell. "Variational Bayesian Learning of Probabilistic Discriminative Models With Latent Softmax Variables." IEEE Transactions on Signal Processing 59, no. 7 (July 2011): 3143–54. http://dx.doi.org/10.1109/tsp.2011.2144587.
Повний текст джерелаWu, Ying Nian, Ruiqi Gao, Tian Han, and Song-Chun Zhu. "A tale of three probabilistic families: Discriminative, descriptive, and generative models." Quarterly of Applied Mathematics 77, no. 2 (December 31, 2018): 423–65. http://dx.doi.org/10.1090/qam/1528.
Повний текст джерелаQin, Huafeng, and Peng Wang. "Finger-Vein Verification Based on LSTM Recurrent Neural Networks." Applied Sciences 9, no. 8 (April 24, 2019): 1687. http://dx.doi.org/10.3390/app9081687.
Повний текст джерелаVillanueva Llerena, Julissa, and Denis Deratani Maua. "Efficient Predictive Uncertainty Estimators for Deep Probabilistic Models." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 10 (April 3, 2020): 13740–41. http://dx.doi.org/10.1609/aaai.v34i10.7142.
Повний текст джерелаChu, Joseph Lin, and 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, no. 1 (January 1, 2014): 5–19. http://dx.doi.org/10.2478/jaiscr-2014-0021.
Повний текст джерелаWang, Liwei, Xiong Li, Zhuowen Tu, and Jiaya Jia. "Discriminative Clustering via Generative Feature Mapping." Proceedings of the AAAI Conference on Artificial Intelligence 26, no. 1 (September 20, 2021): 1162–68. http://dx.doi.org/10.1609/aaai.v26i1.8305.
Повний текст джерелаBuscombe, Daniel, and Paul Grams. "Probabilistic Substrate Classification with Multispectral Acoustic Backscatter: A Comparison of Discriminative and Generative Models." Geosciences 8, no. 11 (October 30, 2018): 395. http://dx.doi.org/10.3390/geosciences8110395.
Повний текст джерелаLuo, You-Wei, Chuan-Xian Ren, Pengfei Ge, Ke-Kun Huang, and Yu-Feng Yu. "Unsupervised Domain Adaptation via Discriminative Manifold Embedding and Alignment." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 5029–36. http://dx.doi.org/10.1609/aaai.v34i04.5943.
Повний текст джерелаKarami, Mahdi, and Dale Schuurmans. "Deep Probabilistic Canonical Correlation Analysis." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 9 (May 18, 2021): 8055–63. http://dx.doi.org/10.1609/aaai.v35i9.16982.
Повний текст джерелаCui, Bo, Guyue Hu, and Shan Yu. "DeepCollaboration: Collaborative Generative and Discriminative Models for Class Incremental Learning." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 2 (May 18, 2021): 1175–83. http://dx.doi.org/10.1609/aaai.v35i2.16204.
Повний текст джерелаGordon, Jonathan, and José Miguel Hernández-Lobato. "Combining deep generative and discriminative models for Bayesian semi-supervised learning." Pattern Recognition 100 (April 2020): 107156. http://dx.doi.org/10.1016/j.patcog.2019.107156.
Повний текст джерелаBai, Wenjun, Changqin Quan, and Zhi-Wei Luo. "Improving Generative and Discriminative Modelling Performance by Implementing Learning Constraints in Encapsulated Variational Autoencoders." Applied Sciences 9, no. 12 (June 21, 2019): 2551. http://dx.doi.org/10.3390/app9122551.
Повний текст джерелаLi, Fuqiang, Tongzhuang Zhang, Yong Liu, and Feiqi Long. "Deep Residual Vector Encoding for Vein Recognition." Electronics 11, no. 20 (October 13, 2022): 3300. http://dx.doi.org/10.3390/electronics11203300.
Повний текст джерелаHu, Gang, Chahna Dixit, and Guanqiu Qi. "Discriminative Shape Feature Pooling in Deep Neural Networks." Journal of Imaging 8, no. 5 (April 20, 2022): 118. http://dx.doi.org/10.3390/jimaging8050118.
Повний текст джерелаCoto-Jiménez, Marvin. "Discriminative Multi-Stream Postfilters Based on Deep Learning for Enhancing Statistical Parametric Speech Synthesis." Biomimetics 6, no. 1 (February 7, 2021): 12. http://dx.doi.org/10.3390/biomimetics6010012.
Повний текст джерелаAdedigba, Adeyinka P., Steve A. Adeshina, and Abiodun M. Aibinu. "Performance Evaluation of Deep Learning Models on Mammogram Classification Using Small Dataset." Bioengineering 9, no. 4 (April 6, 2022): 161. http://dx.doi.org/10.3390/bioengineering9040161.
Повний текст джерелаAlshazly, Hammam, Christoph Linse, Erhardt Barth, and Thomas Martinetz. "Ensembles of Deep Learning Models and Transfer Learning for Ear Recognition." Sensors 19, no. 19 (September 24, 2019): 4139. http://dx.doi.org/10.3390/s19194139.
Повний текст джерелаMaroñas, Juan, Roberto Paredes, and Daniel Ramos. "Calibration of deep probabilistic models with decoupled bayesian neural networks." Neurocomputing 407 (September 2020): 194–205. http://dx.doi.org/10.1016/j.neucom.2020.04.103.
Повний текст джерелаLi, Zhenjun, Xi Liu, Dawei Kou, Yi Hu, Qingrui Zhang, and Qingxi Yuan. "Probabilistic Models for the Shear Strength of RC Deep Beams." Applied Sciences 13, no. 8 (April 12, 2023): 4853. http://dx.doi.org/10.3390/app13084853.
Повний текст джерелаLiu, Shengyi. "Model Extraction Attack and Defense on Deep Generative Models." Journal of Physics: Conference Series 2189, no. 1 (February 1, 2022): 012024. http://dx.doi.org/10.1088/1742-6596/2189/1/012024.
Повний текст джерелаBai, Shuang. "Scene Categorization Through Using Objects Represented by Deep Features." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 09 (February 2017): 1755013. http://dx.doi.org/10.1142/s0218001417550138.
Повний текст джерелаKumar, Parmod, D. Suganthi, K. Valarmathi, Mahendra Pratap Swain, Piyush Vashistha, Dharam Buddhi, and Emmanuel Sey. "A Multi-Thresholding-Based Discriminative Neural Classifier for Detection of Retinoblastoma Using CNN Models." BioMed Research International 2023 (February 6, 2023): 1–9. http://dx.doi.org/10.1155/2023/5803661.
Повний текст джерелаYu, Hee-Jin, Chang-Hwan Son, and Dong Hyuk Lee. "Apple Leaf Disease Identification Through Region-of-Interest-Aware Deep Convolutional Neural Network." Journal of Imaging Science and Technology 64, no. 2 (March 1, 2020): 20507–1. http://dx.doi.org/10.2352/j.imagingsci.technol.2020.64.2.020507.
Повний текст джерелаBoursin, Nicolas, Carl Remlinger, and Joseph Mikael. "Deep Generators on Commodity Markets Application to Deep Hedging." Risks 11, no. 1 (December 23, 2022): 7. http://dx.doi.org/10.3390/risks11010007.
Повний текст джерелаD’Andrea, Fabio, Pierre Gentine, Alan K. Betts, and Benjamin R. Lintner. "Triggering Deep Convection with a Probabilistic Plume Model." Journal of the Atmospheric Sciences 71, no. 11 (October 29, 2014): 3881–901. http://dx.doi.org/10.1175/jas-d-13-0340.1.
Повний текст джерелаSerpell, 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.
Повний текст джерелаQian, Weizhu, Fabrice Lauri, and Franck Gechter. "Supervised and semi-supervised deep probabilistic models for indoor positioning problems." Neurocomputing 435 (May 2021): 228–38. http://dx.doi.org/10.1016/j.neucom.2020.12.131.
Повний текст джерелаWang, Wenzheng, Yuqi Han, Chenwei Deng, and Zhen Li. "Hyperspectral Image Classification via Deep Structure Dictionary Learning." Remote Sensing 14, no. 9 (May 8, 2022): 2266. http://dx.doi.org/10.3390/rs14092266.
Повний текст джерелаAndrianomena, Sambatra. "Probabilistic learning for pulsar classification." Journal of Cosmology and Astroparticle Physics 2022, no. 10 (October 1, 2022): 016. http://dx.doi.org/10.1088/1475-7516/2022/10/016.
Повний текст джерелаKim, Hyesuk, and 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.
Повний текст джерелаMurad, 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.
Повний текст джерелаAdams, Jadie. "Probabilistic Shape Models of Anatomy Directly from Images." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 13 (June 26, 2023): 16107–8. http://dx.doi.org/10.1609/aaai.v37i13.26914.
Повний текст джерелаRavuri, 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, no. 7878 (September 29, 2021): 672–77. http://dx.doi.org/10.1038/s41586-021-03854-z.
Повний текст джерелаHuang, Jiabo, Qi Dong, Shaogang Gong, and Xiatian Zhu. "Unsupervised Deep Learning via Affinity Diffusion." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 11029–36. http://dx.doi.org/10.1609/aaai.v34i07.6757.
Повний текст джерелаCollins, Michael, and Terry Koo. "Discriminative Reranking for Natural Language Parsing." Computational Linguistics 31, no. 1 (March 2005): 25–70. http://dx.doi.org/10.1162/0891201053630273.
Повний текст джерелаMashlakov, Aleksei, Toni Kuronen, Lasse Lensu, Arto Kaarna, and Samuli Honkapuro. "Assessing the performance of deep learning models for multivariate probabilistic energy forecasting." Applied Energy 285 (March 2021): 116405. http://dx.doi.org/10.1016/j.apenergy.2020.116405.
Повний текст джерелаDuan, Yun. "A Novel Interval Energy-Forecasting Method for Sustainable Building Management Based on Deep Learning." Sustainability 14, no. 14 (July 13, 2022): 8584. http://dx.doi.org/10.3390/su14148584.
Повний текст джерелаKrogh, Anders, and Søren Kamaric Riis. "Hidden Neural Networks." Neural Computation 11, no. 2 (February 1, 1999): 541–63. http://dx.doi.org/10.1162/089976699300016764.
Повний текст джерела