Relevant bibliographies by topics / Probabilistic deep models

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Probabilistic deep models'

Author: Grafiati
Published: 25 May 2024
Last updated: 31 July 2025

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Journal articles on the topic "Probabilistic deep models"

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Masegosa, Andrés R., Rafael Cabañas, Helge Langseth, Thomas D. Nielsen, and Antonio Salmerón. "Probabilistic Models with Deep Neural Networks." Entropy 23, no. 1 (2021): 117. http://dx.doi.org/10.3390/e23010117.

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Recent advances in statistical inference have significantly expanded the toolbox of probabilistic modeling. Historically, probabilistic modeling has been constrained to very restricted model classes, where exact or approximate probabilistic inference is feasible. However, developments in variational inference, a general form of approximate probabilistic inference that originated in statistical physics, have enabled probabilistic modeling to overcome these limitations: (i) Approximate probabilistic inference is now possible over a broad class of probabilistic models containing a large number of
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Villanueva 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 (2020): 13740–41. http://dx.doi.org/10.1609/aaai.v34i10.7142.

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Deep Probabilistic Models (DPM) based on arithmetic circuits representation, such as Sum-Product Networks (SPN) and Probabilistic Sentential Decision Diagrams (PSDD), have shown competitive performance in several machine learning tasks with interesting properties (Poon and Domingos 2011; Kisa et al. 2014). Due to the high number of parameters and scarce data, DPMs can produce unreliable and overconfident inference. This research aims at increasing the robustness of predictive inference with DPMs by obtaining new estimators of the predictive uncertainty. This problem is not new and the literatu
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Karami, Mahdi, and Dale Schuurmans. "Deep Probabilistic Canonical Correlation Analysis." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 9 (2021): 8055–63. http://dx.doi.org/10.1609/aaai.v35i9.16982.

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We propose a deep generative framework for multi-view learning based on a probabilistic interpretation of canonical correlation analysis (CCA). The model combines a linear multi-view layer in the latent space with deep generative networks as observation models, to decompose the variability in multiple views into a shared latent representation that describes the common underlying sources of variation and a set of viewspecific components. To approximate the posterior distribution of the latent multi-view layer, an efficient variational inference procedure is developed based on the solution of pr
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Lu, Ming, Zhihao Duan, Fengqing Zhu, and Zhan Ma. "Deep Hierarchical Video Compression." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 8 (2024): 8859–67. http://dx.doi.org/10.1609/aaai.v38i8.28733.

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Recently, probabilistic predictive coding that directly models the conditional distribution of latent features across successive frames for temporal redundancy removal has yielded promising results. Existing methods using a single-scale Variational AutoEncoder (VAE) must devise complex networks for conditional probability estimation in latent space, neglecting multiscale characteristics of video frames. Instead, this work proposes hierarchical probabilistic predictive coding, for which hierarchal VAEs are carefully designed to characterize multiscale latent features as a family of flexible pri
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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.

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In recent years, deep learning models have been developed to address probabilistic forecasting tasks, assuming an implicit stochastic process that relates past observed values to uncertain future values. These models are capable of capturing the inherent uncertainty of the underlying process, but they ignore the model uncertainty that comes from the fact of not having infinite data. This work proposes addressing the model uncertainty problem using Monte Carlo dropout, a variational approach that assigns distributions to the weights of a neural network instead of simply using fixed values. This
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Jiang, Yiyang. "Research on Denoising Diffusion Probabilistic Models." Highlights in Science, Engineering and Technology 107 (August 15, 2024): 560–72. http://dx.doi.org/10.54097/sxd49274.

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Diffusion models represent the latest state-of-the-art in the domain of deep generative models, boasting remarkable performance across a broad spectrum of applications. Despite the widespread success of diffusion models in various tasks, the original formulations of these models exhibit notable limitations. The article uses DDPM as an example, thoroughly and deeply exploring and deriving the mathematical principles of the model from two different perspectives. Additionally, this article explores the relationship between diffusion models and five other types of generative models: Generative Adv
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Maroñas, 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.

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Li, 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 (2023): 4853. http://dx.doi.org/10.3390/app13084853.

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A new shear strength determination of reinforced concrete (RC) deep beams was proposed by using a statistical approach. The Bayesian–MCMC (Markov Chain Monte Carlo) method was introduced to establish a new shear prediction model and to improve seven existing deterministic models with a database of 645 experimental data. The bias correction terms of deterministic models were described by key explanatory terms identified by a systematic removal process. Considering multi-parameters, the Gibbs sampling was used to solve the high dimensional integration problem and to determine optimum and reliabl
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Zhang, Ruqi. "Scalable and Efficient Probabilistic Inference for Bayesian Deep Learning and Generative Modeling." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 27 (2025): 28737. https://doi.org/10.1609/aaai.v39i27.35129.

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Probabilistic inference is a fundamental challenge in machine learning, spanning tasks from approximate Bayesian inference to generative AI. In this talk, I will present theoretically-guaranteed scalable and efficient probabilistic inference with applications in Bayesian deep learning and generative modeling. First, I will introduce a new compute paradigm for probabilistic inference that leverages modern accelerators, specifically low-precision and sparsity, to significantly speed up inference while preserving accuracy. Next, I will present a new framework for efficient inference in discrete d
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Zheng, Chenyiqiu. "A comprehensive review of probabilistic and statistical methods in social network sentiment analysis." Advances in Engineering Innovation 16, no. 3 (2025): 38–43. https://doi.org/10.54254/2977-3903/2025.21918.

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In the era of rapid digital transformation, social networks generate huge amounts of textual data every day, making sentiment analysis an essential tool for understanding public opinion. This study focuses on the application of probabilistic and statistical methods to sentiment analysis in social networks, highlighting their effectiveness in dealing with uncertainty and modeling the distribution of emotions. The main objective is to evaluate the role of Nave Bayesian (NB), Hidden Markov models (HMMs), and Bayesian networks in emotion classification, emotion propagation, and dynamic emotion tra
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Dissertations / Theses on the topic "Probabilistic deep models"

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Misino, Eleonora. "Deep Generative Models with Probabilistic Logic Priors." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/24058/.

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Many different extensions of the VAE framework have been introduced in the past. How­ ever, the vast majority of them focused on pure sub­-symbolic approaches that are not sufficient for solving generative tasks that require a form of reasoning. In this thesis, we propose the probabilistic logic VAE (PLVAE), a neuro-­symbolic deep generative model that combines the representational power of VAEs with the reasoning ability of probabilistic ­logic programming. The strength of PLVAE resides in its probabilistic ­logic prior, which provides an interpretable structure to the latent space that can b
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Zhai, Menghua. "Deep Probabilistic Models for Camera Geo-Calibration." UKnowledge, 2018. https://uknowledge.uky.edu/cs_etds/74.

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The ultimate goal of image understanding is to transfer visual images into numerical or symbolic descriptions of the scene that are helpful for decision making. Knowing when, where, and in which direction a picture was taken, the task of geo-calibration makes it possible to use imagery to understand the world and how it changes in time. Current models for geo-calibration are mostly deterministic, which in many cases fails to model the inherent uncertainties when the image content is ambiguous. Furthermore, without a proper modeling of the uncertainty, subsequent processing can yield overly con
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Wu, Di. "Human action recognition using deep probabilistic graphical models." Thesis, University of Sheffield, 2014. http://etheses.whiterose.ac.uk/6603/.

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Building intelligent systems that are capable of representing or extracting high-level representations from high-dimensional sensory data lies at the core of solving many A.I. related tasks. Human action recognition is an important topic in computer vision that lies in high-dimensional space. Its applications include robotics, video surveillance, human-computer interaction, user interface design, and multi-media video retrieval amongst others. A number of approaches have been proposed to extract representative features from high-dimensional temporal data, most commonly hard wired geometric or
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Rossi, Simone. "Improving Scalability and Inference in Probabilistic Deep Models." Electronic Thesis or Diss., Sorbonne université, 2022. http://www.theses.fr/2022SORUS042.

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Au cours de la dernière décennie, l'apprentissage profond a atteint un niveau de maturité suffisant pour devenir le choix privilégié pour résoudre les problèmes liés à l'apprentissage automatique ou pour aider les processus de prise de décision.En même temps, l'apprentissage profond n'a généralement pas la capacité de quantifier avec précision l'incertitude de ses prédictions, ce qui rend ces modèles moins adaptés aux applications critiques en matière de risque.Une solution possible pour résoudre ce problème est d'utiliser une formulation bayésienne ; cependant, bien que cette solution soit él
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Hager, 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.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (page 21).<br>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
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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.

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Morales, quinga Katherine Tania. "Generative Markov models for sequential bayesian classification." Electronic Thesis or Diss., Institut polytechnique de Paris, 2024. http://www.theses.fr/2024IPPAS019.

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Cette thèse vise à modéliser des données séquentielles à travers l'utilisation de modèles probabilistes à variables latentes et paramétrés par des architectures de type réseaux de neurones profonds. Notre objectif est de développer des modèles dynamiques capables de capturer des dynamiques temporelles complexes inhérentes aux données séquentielles tout en étant applicables dans des domaines variés tels que la classification, la prédiction et la génération de données pour n'importe quel type de données séquentielles. Notre approche se concentre sur plusieurs problématiques liés à la modélisatio
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Qian, Weizhu. "Discovering human mobility from mobile data : probabilistic models and learning algorithms." Thesis, Bourgogne Franche-Comté, 2020. http://www.theses.fr/2020UBFCA025.

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Les données d'utilisation des smartphones peuvent être utilisées pour étudier la mobilité humaine que ce soit en environnement extérieur ouvert ou à l'intérieur de bâtiments. Dans ce travail, nous étudions ces deux aspects de la mobilité humaine en proposant des algorithmes de machine learning adapté aux sources d'information disponibles dans chacun des contextes.Pour l'étude de la mobilité en environnement extérieur, nous utilisons les données de coordonnées GPS collectées pour découvrir les schémas de mobilité quotidiens des utilisateurs. Pour cela, nous proposons un algorithme de clustering
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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.

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Cognitive Radio Internet of Things (CR-IoT) has revolutionized almost every field of life and reshaped the technological world. Several tiny devices are seamlessly connected in a CR-IoT network to perform various tasks in many applications. Nevertheless, CR-IoT surfers from malicious attacks that pulverize communication and perturb network performance. Therefore, recently it is envisaged to introduce higher-level Artificial Intelligence (AI) by incorporating Self-Awareness (SA) capabilities into CR-IoT objects to facilitate CR-IoT networks to establish secure transmission against vicious attac
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El-Shaer, Mennat Allah. "An Experimental Evaluation of Probabilistic Deep Networks for Real-time Traffic Scene Representation using Graphical Processing Units." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1546539166677894.

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Books on the topic "Probabilistic deep models"

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Oaksford, 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.

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There are deep intuitions that the meaning of conditional statements relate to probabilistic law-like dependencies. In this chapter it is argued that these intuitions can be captured by representing conditionals in causal Bayes nets (CBNs) and that this conjecture is theoretically productive. This proposal is borne out in a variety of results. First, causal considerations can provide a unified account of abstract and causal conditional reasoning. Second, a recent model (Fernbach &amp; Erb, 2013) can be extended to the explicit causal conditional reasoning paradigm (Byrne, 1989), making some no
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Tutino, Stefania. Uncertainty in Post-Reformation Catholicism. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190694098.001.0001.

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This book provides a historical account of the development and implications of early modern probabilism. First elaborated in the sixteenth century, probabilism represented a significant and controversial novelty in Catholic moral theology. Against a deep-seated tradition defending the strict application of moral rules, probabilist theologians maintained that in situations of uncertainty, the agent can legitimately follow any course of action supported by a probable opinion, no matter how disputable. By the second half of the seventeenth century, and thanks in part to Pascal’s influential antip
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Trappenberg, Thomas P. Fundamentals of Machine Learning. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198828044.001.0001.

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Machine learning is exploding, both in research and for industrial applications. This book aims to be a brief introduction to this area given the importance of this topic in many disciplines, from sciences to engineering, and even for its broader impact on our society. This book tries to contribute with a style that keeps a balance between brevity of explanations, the rigor of mathematical arguments, and outlining principle ideas. At the same time, this book tries to give some comprehensive overview of a variety of methods to see their relation on specialization within this area. This includes
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Levinson, Stephen C. Speech Acts. Edited by Yan Huang. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780199697960.013.22.

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The essential insight of speech act theory was that when we use language, we perform actions—in a more modern parlance, core language use in interaction is a form of joint action. Over the last thirty years, speech acts have been relatively neglected in linguistic pragmatics, although important work has been done especially in conversation analysis. Here we review the core issues—the identifying characteristics, the degree of universality, the problem of multiple functions, and the puzzle of speech act recognition. Special attention is drawn to the role of conversation structure, probabilistic
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Book chapters on the topic "Probabilistic deep models"

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Sucar, Luis Enrique. "Deep Learning and Graphical Models." In Probabilistic Graphical Models. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61943-5_16.

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Tomczak, Jakub M. "Probabilistic Modeling: From Mixture Models to Probabilistic Circuits." In Deep Generative Modeling. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-64087-2_2.

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Bahadir, Cagla Deniz, Benjamin Liechty, David J. Pisapia, and Mert R. Sabuncu. "Characterizing the Features of Mitotic Figures Using a Conditional Diffusion Probabilistic Model." In Deep Generative Models. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-53767-7_12.

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Gustafsson, Fredrik K., Martin Danelljan, Goutam Bhat, and Thomas B. Schön. "Energy-Based Models for Deep Probabilistic Regression." In Computer Vision – ECCV 2020. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58565-5_20.

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Kucharavy, Andrei. "From Deep Neural Language Models to LLMs." In Large Language Models in Cybersecurity. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-54827-7_1.

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AbstractLarge Language Models (LLMs) are scaled-up instances of Deep Neural Language Models—a type of Natural Language Processing (NLP) tools trained with Machine Learning (ML). To best understand how LLMs work, we must dive into what technologies they build on top of and what makes them different. To achieve this, an overview of the history of LLMs development, starting from the 1990s, is provided before covering the counterintuitive purely probabilistic nature of the Deep Neural Language Models, continuous token embedding spaces, recurrent neural networks-based models, what self-attention br
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Hung, 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. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88210-5_7.

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Nkambule, 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. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2102-4_17.

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Völcker, 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. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43823-4_3.

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Fernández-Rodríguez, Jose David, Jesús Benito-Picazo, Iván García-Aguilar, and Ezequiel López-Rubio. "Automated Semantic Labelling of Images Generated with Deep Diffusion Probabilistic Models." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-75013-7_4.

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Contreras, Victor, Michael Schumacher, and Davide Calvaresi. "Explanation of Deep Learning Models via Logic Rules Enhanced by Embeddings Analysis, and Probabilistic Models." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-70074-3_9.

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Conference papers on the topic "Probabilistic deep models"

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Zheng, Laura, Sanghyun Son, Jing Liang, Xijun Wang, Brian Clipp, and Ming C. Lin. "Deep Stochastic Kinematic Models for Probabilistic Motion Forecasting in Traffic." In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2024. https://doi.org/10.1109/iros58592.2024.10802164.

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Arif, Arooj. "TESTIFAI: Probabilistic Context-Aware Testing for Safe Deep Learning Models." In 2025 IEEE/ACM 47th International Conference on Software Engineering: Companion Proceedings (ICSE-Companion). IEEE, 2025. https://doi.org/10.1109/icse-companion66252.2025.00037.

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Sattarzadeh, Ali Reza, and Pubudu N. Pathirana. "Probabilistic Graph Models: A Key to Boosting Deep Reinforcement Learning in Urban Traffic Networks." In 2025 17th International Conference on Computer and Automation Engineering (ICCAE). IEEE, 2025. https://doi.org/10.1109/iccae64891.2025.10980562.

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Sutjiadi, Raymond, Siti Sendari, Heru Wahyu Herwanto, and Yosi Kristian. "Generating High-quality Synthetic Mammogram Images Using Denoising Diffusion Probabilistic Models: a Novel Approach for Augmenting Deep Learning Datasets." In 2024 International Conference on Information Technology Systems and Innovation (ICITSI). IEEE, 2024. https://doi.org/10.1109/icitsi65188.2024.10929446.

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Shahab, Mohammad, Sunidhi Bachawala, Marcial Gonzalez, Gintaras Reklaitis, and Zoltan Nagy. "Design Space Identification of the Rotary Tablet Press." In Foundations of Computer-Aided Process Design. PSE Press, 2024. http://dx.doi.org/10.69997/sct.156711.

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The determination of the design space (DS) in a pharmaceutical process is a crucial aspect of the quality-by-design (QbD) initiative which promotes quality built into the desired product. This is achieved through a deep understanding of how the critical quality attributes (CQAs) and process parameters (CPPs) interact that have been demonstrated to provide quality assurance. For computational inexpensive models, the original process model can be directly deployed to identify the design space. One such crucial process is the Tablet Press (TP), which directly compresses the powder blend into indi
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Kojima, Keisuke, Jianing Liu, and Roberto Paiella. "Inverse Design of Plasmonic Phase-Contrast Image Sensors Using Denoising Diffusion Probabilistic Model." In CLEO: Fundamental Science. Optica Publishing Group, 2024. http://dx.doi.org/10.1364/cleo_fs.2024.fth1r.4.

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We use a generative deep learning method based on denoising diffusion probabilistic model to design plasmonic phase-imaging sensors for broadband operation. This flexible method enables optimized inverse design for a wide range of nanophotonic devices.
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Li, Zhongya, An Yan, Junhao Zhao, et al. "Model-Free Deep Learning of Joint GS and PS for 300G Flexible Coherent PON based on Direct Information Interaction between OLT and ONUs." In Optical Fiber Communication Conference. Optica Publishing Group, 2025. https://doi.org/10.1364/ofc.2025.th1j.2.

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We proposed a model-free deep-learning of joint geometric- and probabilistic-shaping for optimization in a 20-km, 25-Gbaud, 300-Gbps FLCS-CPON using direct information exchanges between OLT and ONUs, achieving a 4 dB dynamic-range enhancement over traditional scheme.
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Ghahfarokhi, Sepehr Salem, Tyrell To, Julie Jorns, Tina Yen, Bing Yu, and Dong Hye Ye. "Deep Learning for Automated Detection of Breast Cancer in Deep Ultraviolet Fluorescence Images with Diffusion Probabilistic Model." In 2024 IEEE International Symposium on Biomedical Imaging (ISBI). IEEE, 2024. http://dx.doi.org/10.1109/isbi56570.2024.10635349.

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Syed, Shakir, Lakshminarayana Reddy Kothapalli Sondinti, Puneet Sapra, Amit Joshi, Shobana M, and Deepan Chakravarthi A. V. "Probabilistic Prediction for Start-Up Success through Deep Learning based Stacked DAE Model." In 2025 3rd International Conference on Integrated Circuits and Communication Systems (ICICACS). IEEE, 2025. https://doi.org/10.1109/icicacs65178.2025.10967987.

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Sidheekh, Sahil, Saurabh Mathur, Athresh Karanam, and Sriraam Natarajan. "Deep Tractable Probabilistic Models." In CODS-COMAD 2024: 7th Joint International Conference on Data Science & Management of Data (11th ACM IKDD CODS and 29th COMAD). ACM, 2024. http://dx.doi.org/10.1145/3632410.3633295.

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Reports on the topic "Probabilistic deep models"

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Pasupuleti, Murali Krishna. Stochastic Computation for AI: Bayesian Inference, Uncertainty, and Optimization. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv325.

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Abstract: Stochastic computation is a fundamental approach in artificial intelligence (AI) that enables probabilistic reasoning, uncertainty quantification, and robust decision-making in complex environments. This research explores the theoretical foundations, computational techniques, and real-world applications of stochastic methods, focusing on Bayesian inference, Monte Carlo methods, stochastic optimization, and uncertainty-aware AI models. Key topics include probabilistic graphical models, Markov Chain Monte Carlo (MCMC), variational inference, stochastic gradient descent (SGD), and Bayes
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Pasupuleti, Murali Krishna. Decision Theory and Model-Based AI: Probabilistic Learning, Inference, and Explainability. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv525.

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Abstract Decision theory and model-based AI provide the foundation for probabilistic learning, optimal inference, and explainable decision-making, enabling AI systems to reason under uncertainty, optimize long-term outcomes, and provide interpretable predictions. This research explores Bayesian inference, probabilistic graphical models, reinforcement learning (RL), and causal inference, analyzing their role in AI-driven decision systems across various domains, including healthcare, finance, robotics, and autonomous systems. The study contrasts model-based and model-free approaches in decision-
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Pasupuleti, Murali Krishna. Quantum-Enhanced Machine Learning: Harnessing Quantum Computing for Next-Generation AI Systems. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv125.

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Abstract Quantum-enhanced machine learning (QML) represents a paradigm shift in artificial intelligence by integrating quantum computing principles to solve complex computational problems more efficiently than classical methods. By leveraging quantum superposition, entanglement, and parallelism, QML has the potential to accelerate deep learning training, optimize combinatorial problems, and enhance feature selection in high-dimensional spaces. This research explores foundational quantum computing concepts relevant to AI, including quantum circuits, variational quantum algorithms, and quantum k
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