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Статті в журналах з теми "Deep Discriminative Probabilistic Models"
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 джерелаДисертації з теми "Deep Discriminative Probabilistic Models"
Misino, Eleonora. "Deep Generative Models with Probabilistic Logic Priors." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/24058/.
Повний текст джерелаZhai, Menghua. "Deep Probabilistic Models for Camera Geo-Calibration." UKnowledge, 2018. https://uknowledge.uky.edu/cs_etds/74.
Повний текст джерелаGeorgatzis, Konstantinos. "Dynamical probabilistic graphical models applied to physiological condition monitoring." Thesis, University of Edinburgh, 2017. http://hdl.handle.net/1842/28838.
Повний текст джерелаWu, Di. "Human action recognition using deep probabilistic graphical models." Thesis, University of Sheffield, 2014. http://etheses.whiterose.ac.uk/6603/.
Повний текст джерелаSokolovska, Nataliya. "Contributions to the estimation of probabilistic discriminative models: semi-supervised learning and feature selection." Phd thesis, Télécom ParisTech, 2010. http://pastel.archives-ouvertes.fr/pastel-00006257.
Повний текст джерелаHager, Paul Andrew. "Investigation of connection between deep learning and probabilistic graphical models." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/119552.
Повний текст джерелаThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (page 21).
The field of machine learning (ML) has benefitted greatly from its relationship with the field of classical statistics. In support of that continued expansion, the following proposes an alternative perspective at the link between these fields. The link focuses on probabilistic graphical models in the context of reinforcement learning. Viewing certain algorithms as reinforcement learning gives one an ability to map ML concepts to statistics problems. Training a multi-layer nonlinear perceptron algorithm is equivalent to structure learning problems in probabilistic graphical models (PGMs). The technique of boosting weak rules into an ensemble is weighted sampling. Finally regularizing neural networks using the dropout technique is conditioning on certain observations in PGMs.
by Paul Andrew Hager.
M. Eng.
Azizpour, Hossein. "Visual Representations and Models: From Latent SVM to Deep Learning." Doctoral thesis, KTH, Datorseende och robotik, CVAP, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-192289.
Повний текст джерелаQC 20160908
Farouni, Tarek. "An Overview of Probabilistic Latent Variable Models with anApplication to the Deep Unsupervised Learning of ChromatinStates." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1492189894812539.
Повний текст джерелаQian, Weizhu. "Discovering human mobility from mobile data : probabilistic models and learning algorithms." Thesis, Bourgogne Franche-Comté, 2020. http://www.theses.fr/2020UBFCA025.
Повний текст джерелаSmartphone usage data can be used to study human indoor and outdoor mobility. In our work, we investigate both aspects in proposing machine learning-based algorithms adapted to the different information sources that can be collected.In terms of outdoor mobility, we use the collected GPS coordinate data to discover the daily mobility patterns of the users. To this end, we propose an automatic clustering algorithm using the Dirichlet process Gaussian mixture model (DPGMM) so as to cluster the daily GPS trajectories. This clustering method is based on estimating probability densities of the trajectories, which alleviate the problems caused by the data noise.By contrast, we utilize the collected WiFi fingerprint data to study indoor human mobility. In order to predict the indoor user location at the next time points, we devise a hybrid deep learning model, called the convolutional mixture density recurrent neural network (CMDRNN), which combines the advantages of different multiple deep neural networks. Moreover, as for accurate indoor location recognition, we presume that there exists a latent distribution governing the input and output at the same time. Based on this assumption, we develop a variational auto-encoder (VAE)-based semi-supervised learning model. In the unsupervised learning procedure, we employ a VAE model to learn a latent distribution of the input, the WiFi fingerprint data. In the supervised learning procedure, we use a neural network to compute the target, the user coordinates. Furthermore, based on the same assumption used in the VAE-based semi-supervised learning model, we leverage the information bottleneck theory to devise a variational information bottleneck (VIB)-based model. This is an end-to-end deep learning model which is easier to train and has better performance.Finally, we validate thees proposed methods on several public real-world datasets providing thus results that verify the efficiencies of our methods as compared to other existing methods generally used
SYED, MUHAMMAD FARRUKH SHAHID. "Data-Driven Approach based on Deep Learning and Probabilistic Models for PHY-Layer Security in AI-enabled Cognitive Radio IoT." Doctoral thesis, Università degli studi di Genova, 2021. http://hdl.handle.net/11567/1048543.
Повний текст джерелаКниги з теми "Deep Discriminative Probabilistic Models"
A Discriminative Approach to Bayesian Filtering with Applications to Human Neural Decoding. Providence, USA: Brown University, 2019.
Знайти повний текст джерелаOaksford, Mike, and Nick Chater. Causal Models and Conditional Reasoning. Edited by Michael R. Waldmann. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199399550.013.5.
Повний текст джерелаTrappenberg, Thomas P. Fundamentals of Machine Learning. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198828044.001.0001.
Повний текст джерелаЧастини книг з теми "Deep Discriminative Probabilistic Models"
Sucar, Luis Enrique. "Deep Learning and Graphical Models." In Probabilistic Graphical Models, 327–46. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61943-5_16.
Повний текст джерелаGustafsson, Fredrik K., Martin Danelljan, Goutam Bhat, and Thomas B. Schön. "Energy-Based Models for Deep Probabilistic Regression." In Computer Vision – ECCV 2020, 325–43. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58565-5_20.
Повний текст джерелаNkambule, Tshepo, and Ritesh Ajoodha. "Classification of Music by Genre Using Probabilistic Models and Deep Learning Models." In Proceedings of Sixth International Congress on Information and Communication Technology, 185–93. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2102-4_17.
Повний текст джерелаHung, Alex Ling Yu, Zhiqing Sun, Wanwen Chen, and John Galeotti. "Hierarchical Probabilistic Ultrasound Image Inpainting via Variational Inference." In Deep Generative Models, and Data Augmentation, Labelling, and Imperfections, 83–92. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88210-5_7.
Повний текст джерелаVölcker, Claas, Alejandro Molina, Johannes Neumann, Dirk Westermann, and Kristian Kersting. "DeepNotebooks: Deep Probabilistic Models Construct Python Notebooks for Reporting Datasets." In Machine Learning and Knowledge Discovery in Databases, 28–43. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43823-4_3.
Повний текст джерелаDinh, Xuan Truong, and Hai Van Pham. "Social Network Analysis Based on Combining Probabilistic Models with Graph Deep Learning." In Communication and Intelligent Systems, 975–86. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1089-9_76.
Повний текст джерелаLinghu, Yuan, Xiangxue Li, and Zhenlong Zhang. "Deep Learning vs. Traditional Probabilistic Models: Case Study on Short Inputs for Password Guessing." In Algorithms and Architectures for Parallel Processing, 468–83. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-38991-8_31.
Повний текст джерелаAbid, M., Y. Ouakrim, A. Mitiche, P. A. Vendittoli, N. Hagemeister, and N. Mezghani. "A Comparative Study of End-To-End Discriminative Deep Learning Models for Knee Joint Kinematic Time Series Classification." In Biomedical Signal Processing, 33–61. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-67494-6_2.
Повний текст джерелаLiu, Zheng, and Hao Wang. "Research on Process Diagnosis of Severe Accidents Based on Deep Learning and Probabilistic Safety Analysis." In Springer Proceedings in Physics, 624–34. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1023-6_54.
Повний текст джерелаRachmadi, Muhammad Febrian, Maria del C. Valdés-Hernández, Rizal Maulana, Joanna Wardlaw, Stephen Makin, and Henrik Skibbe. "Probabilistic Deep Learning with Adversarial Training and Volume Interval Estimation - Better Ways to Perform and Evaluate Predictive Models for White Matter Hyperintensities Evolution." In Predictive Intelligence in Medicine, 168–80. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87602-9_16.
Повний текст джерелаТези доповідей конференцій з теми "Deep Discriminative Probabilistic Models"
Wang, Xin, and Siu Ming Yiu. "Classification with Rejection: Scaling Generative Classifiers with Supervised Deep Infomax." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/412.
Повний текст джерелаWang, Mengqiu, and Luo Si. "Discriminative probabilistic models for passage based retrieval." In the 31st annual international ACM SIGIR conference. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1390334.1390407.
Повний текст джерелаLloyd, John, Yijiong Lin, and Nathan Lepora. "Probabilistic Discriminative Models address the Tactile Perceptual Aliasing Problem." In Robotics: Science and Systems 2021. Robotics: Science and Systems Foundation, 2021. http://dx.doi.org/10.15607/rss.2021.xvii.057.
Повний текст джерелаSokolovska, Nataliya, Olivier Cappé, and François Yvon. "The asymptotics of semi-supervised learning in discriminative probabilistic models." In the 25th international conference. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1390156.1390280.
Повний текст джерелаErkan, A. N., O. Kroemer, R. Detry, Y. Altun, J. Piater, and J. Peters. "Learning probabilistic discriminative models of grasp affordances under limited supervision." In 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2010). IEEE, 2010. http://dx.doi.org/10.1109/iros.2010.5650088.
Повний текст джерелаvan Dalen, R. C., J. Yang, H. Wang, A. Ragni, C. Zhang, and M. J. F. Gales. "Structured discriminative models using deep neural-network features." In 2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU). IEEE, 2015. http://dx.doi.org/10.1109/asru.2015.7404789.
Повний текст джерелаCetintas, Suleyman, Monica Rogati, Luo Si, and Yi Fang. "Identifying similar people in professional social networks with discriminative probabilistic models." In the 34th international ACM SIGIR conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2009916.2010123.
Повний текст джерелаZhuowen Tu. "Probabilistic boosting-tree: learning discriminative models for classification, recognition, and clustering." In Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1. IEEE, 2005. http://dx.doi.org/10.1109/iccv.2005.194.
Повний текст джерелаXie, Qianqian, Jimin Huang, Min Peng, Yihan Zhang, Kaifei Peng, and Hua Wang. "Discriminative Regularized Deep Generative Models for Semi-Supervised Learning." In 2019 IEEE International Conference on Data Mining (ICDM). IEEE, 2019. http://dx.doi.org/10.1109/icdm.2019.00076.
Повний текст джерелаLiu, Xixi, Che-Tsung Lin, and Christopher Zach. "Energy-based Models for Deep Probabilistic Regression." In 2022 26th International Conference on Pattern Recognition (ICPR). IEEE, 2022. http://dx.doi.org/10.1109/icpr56361.2022.9955636.
Повний текст джерела