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Journal articles on the topic 'Probabilistic deep models'

Author: Grafiati

Published: 25 May 2024

Last updated: 31 July 2025

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1

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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Zuidberg Dos Martires, Pedro. "Probabilistic Neural Circuits." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 15 (2024): 17280–89. http://dx.doi.org/10.1609/aaai.v38i15.29675.

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Probabilistic circuits (PCs) have gained prominence in recent years as a versatile framework for discussing probabilistic models that support tractable queries and are yet expressive enough to model complex probability distributions. Nevertheless, tractability comes at a cost: PCs are less expressive than neural networks. In this paper we introduce probabilistic neural circuits (PNCs), which strike a balance between PCs and neural nets in terms of tractability and expressive power. Theoretically, we show that PNCs can be interpreted as deep mixtures of Bayesian networks. Experimentally, we dem

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Boursin, Nicolas, Carl Remlinger, and Joseph Mikael. "Deep Generators on Commodity Markets Application to Deep Hedging." Risks 11, no. 1 (2022): 7. http://dx.doi.org/10.3390/risks11010007.

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Four deep generative methods for time series are studied on commodity markets and compared with classical probabilistic models. The lack of data in the case of deep hedgers is a common flaw, which deep generative methods seek to address. In the specific case of commodities, it turns out that these generators can also be used to refine the price models by tackling the high-dimensional challenges. In this work, the synthetic time series of commodity prices produced by such generators are studied and then used to train deep hedgers on various options. A fully data-driven approach to commodity ris

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Tomczak, Jakub M. "Deep Generative Modeling: From Probabilistic Framework to Generative AI." Entropy 27, no. 3 (2025): 238. https://doi.org/10.3390/e27030238.

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Sinha, Mourani, Mrinmoyee Bhattacharya, M. Seemanth, and Suchandra A. Bhowmick. "Probabilistic Models and Deep Learning Models Assessed to Estimate Design and Operational Ocean Wave Statistics to Reduce Coastal Hazards." Geosciences 13, no. 12 (2023): 380. http://dx.doi.org/10.3390/geosciences13120380.

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Probabilistic models for long-term estimations and deep learning models for short-term predictions have been evaluated and analyzed for ocean wave parameters. Estimation of design and operational wave parameters for long-term return periods is essential for various coastal and ocean engineering applications. Three probability distributions, namely generalized extreme value distribution (EV), generalized Pareto distribution (PD), and Weibull distribution (WD), have been considered in this work. The design wave parameter considered is the maximal wave height for a specified return period, and th

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Andrianomena, Sambatra. "Probabilistic learning for pulsar classification." Journal of Cosmology and Astroparticle Physics 2022, no. 10 (2022): 016. http://dx.doi.org/10.1088/1475-7516/2022/10/016.

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Abstract In this work, we explore the possibility of using probabilistic learning to identify pulsar candidates. We make use of Deep Gaussian Process (DGP) and Deep Kernel Learning (DKL). Trained on a balanced training set in order to avoid the effect of class imbalance, the performance of the models, achieving relatively high probability of differentiating the positive class from the negative one (roc-auc ∼ 0.98), is very promising overall. We estimate the predictive entropy of each model predictions and find that DKL is more confident than DGP in its predictions and provides better uncertain

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Karimanzira, Divas, Lucas Richter, Desiree Hilbring, Michaela Lödige, and Jonathan Vogl. "Probabilistic multi-step ahead streamflow forecast based on deep learning." at - Automatisierungstechnik 72, no. 6 (2024): 518–27. http://dx.doi.org/10.1515/auto-2024-0033.

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Abstract The use of deep learning methods for fluvial flood forecasting is rapidly gaining traction, offering a promising solution to the challenges associated with accurate yet time-consuming numerical models. This paper presents two physics-inspired deep learning approaches specifically designed for fluvial flood forecasting, each embracing different learning principles: centralized and federated learning. The centralized model utilizes an Encoder-Decoder technique to handle input data of varying types and scales, while the federated model is based on a node-link graph with a seq2seq interna

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Adams, Jadie. "Probabilistic Shape Models of Anatomy Directly from Images." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 13 (2023): 16107–8. http://dx.doi.org/10.1609/aaai.v37i13.26914.

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Statistical shape modeling (SSM) is an enabling tool in medical image analysis as it allows for population-based quantitative analysis. The traditional pipeline for landmark-based SSM from images requires painstaking and cost-prohibitive steps. My thesis aims to leverage probabilistic deep learning frameworks to streamline the adoption of SSM in biomedical research and practice. The expected outcomes of this work will be new frameworks for SSM that (1) provide reliable and calibrated uncertainty quantification, (2) are effective given limited or sparsely annotated/incomplete data, and (3) can

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Ravuri, Suman, Karel Lenc, Matthew Willson, et al. "Skilful precipitation nowcasting using deep generative models of radar." Nature 597, no. 7878 (2021): 672–77. http://dx.doi.org/10.1038/s41586-021-03854-z.

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AbstractPrecipitation nowcasting, the high-resolution forecasting of precipitation up to two hours ahead, supports the real-world socioeconomic needs of many sectors reliant on weather-dependent decision-making1,2. State-of-the-art operational nowcasting methods typically advect precipitation fields with radar-based wind estimates, and struggle to capture important non-linear events such as convective initiations3,4. Recently introduced deep learning methods use radar to directly predict future rain rates, free of physical constraints5,6. While they accurately predict low-intensity rainfall, t

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D’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 (2014): 3881–901. http://dx.doi.org/10.1175/jas-d-13-0340.1.

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Abstract A model unifying the representation of the planetary boundary layer and dry, shallow, and deep convection, the probabilistic plume model (PPM), is presented. Its capacity to reproduce the triggering of deep convection over land is analyzed in detail. The model accurately reproduces the timing of shallow convection and of deep convection onset over land, which is a major issue in many current general climate models. PPM is based on a distribution of plumes with varying thermodynamic states (potential temperature and specific humidity) induced by surface-layer turbulence. Precipitation

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Qian, 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.

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Murad, Abdulmajid, Frank Alexander Kraemer, Kerstin Bach, and Gavin Taylor. "Probabilistic Deep Learning to Quantify Uncertainty in Air Quality Forecasting." Sensors 21, no. 23 (2021): 8009. http://dx.doi.org/10.3390/s21238009.

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Data-driven forecasts of air quality have recently achieved more accurate short-term predictions. However, despite their success, most of the current data-driven solutions lack proper quantifications of model uncertainty that communicate how much to trust the forecasts. Recently, several practical tools to estimate uncertainty have been developed in probabilistic deep learning. However, there have not been empirical applications and extensive comparisons of these tools in the domain of air quality forecasts. Therefore, this work applies state-of-the-art techniques of uncertainty quantification

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Buda-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.

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This paper presents probabilistic assessment of load-bearing capacity and reliability for different STM of deep beams. Six deep beams having different reinforcement arrangement obtained on the basis of STM but the same overall geometry and loading pattern were analysed. The used strut-and-tie models for D-regions of analysed elements have been verified and optimised by different researchers. In order to assess load-bearing capacity of these elements probabilistically, stochastic modelling was performed. In the presented probabilistic analysis of deep beams designed, the ATENA software, the SAR

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Duan, Yun. "A Novel Interval Energy-Forecasting Method for Sustainable Building Management Based on Deep Learning." Sustainability 14, no. 14 (2022): 8584. http://dx.doi.org/10.3390/su14148584.

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Energy conservation in buildings has increasingly become a hot issue for the Chinese government. Compared to deterministic load prediction, probabilistic load forecasting is more suitable for long-term planning and management of building energy consumption. In this study, we propose a probabilistic load-forecasting method for daily and weekly indoor load. The methodology is based on the long short-term memory (LSTM) model and penalized quantile regression (PQR). A comprehensive analysis for a time period of a year is conducted using the proposed method, and back propagation neural networks (BP

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Zheng, Zhuo, Yanfei Zhong, Ji Zhao, Ailong Ma, and Liangpei Zhang. "Unifying remote sensing change detection via deep probabilistic change models: From principles, models to applications." ISPRS Journal of Photogrammetry and Remote Sensing 215 (September 2024): 239–55. http://dx.doi.org/10.1016/j.isprsjprs.2024.07.001.

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Liu, Mao-Yi, Zheng Li, and Hang Zhang. "Probabilistic Shear Strength Prediction for Deep Beams Based on Bayesian-Optimized Data-Driven Approach." Buildings 13, no. 10 (2023): 2471. http://dx.doi.org/10.3390/buildings13102471.

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To ensure the safety of buildings, accurate and robust prediction of a reinforced concrete deep beam’s shear capacity is necessary to avoid unpredictable accidents caused by brittle failure. However, the failure mechanism of reinforced concrete deep beams is very complicated, has not been fully elucidated, and cannot be accurately described by simple equations. To solve this issue, machine learning techniques have been utilized and corresponding prediction models have been developed. Nevertheless, these models can only provide deterministic prediction results of the scalar type, and the confid

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Nye, Logan, Hamid Ghaednia, and Joseph H. Schwab. "Generating synthetic samples of chondrosarcoma histopathology with a denoising diffusion probabilistic model." Journal of Clinical Oncology 41, no. 16_suppl (2023): e13592-e13592. http://dx.doi.org/10.1200/jco.2023.41.16_suppl.e13592.

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e13592 Background: The emergence of digital pathology — an image-based environment for the acquisition, management and interpretation of pathology information supported by computational techniques for data extraction and analysis — is changing the pathology ecosystem. The development of machine-learning approaches for the extraction of information from image data, allows for tissue interrogation in a way that was not previously possible. However, creating digital pathology algorithms requires large volumes of training data, often on the order of thousands of histopathology slides. This becomes

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Mashlakov, 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.

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Bentivoglio, Roberto, Elvin Isufi, Sebastian Nicolaas Jonkman, and Riccardo Taormina. "Deep learning methods for flood mapping: a review of existing applications and future research directions." Hydrology and Earth System Sciences 26, no. 16 (2022): 4345–78. http://dx.doi.org/10.5194/hess-26-4345-2022.

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Abstract. Deep learning techniques have been increasingly used in flood management to overcome the limitations of accurate, yet slow, numerical models and to improve the results of traditional methods for flood mapping. In this paper, we review 58 recent publications to outline the state of the art of the field, identify knowledge gaps, and propose future research directions. The review focuses on the type of deep learning models used for various flood mapping applications, the flood types considered, the spatial scale of the studied events, and the data used for model development. The results

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Gayathri, G. Roopa. "Benchmarking Probabilistic Deep Learning Methods for License Plate Recognition." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47103.

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Abstract: This study benchmarks probabilistic deep learning methods for license plate recognition (LPR), focusing on enhancing accuracy and reliability under real-world conditions. Utilizing a dataset of license plate images, the approach includes comprehensive preprocessing steps such as resizing, normalization, augmentation, and super-resolution to handle low-quality inputs. The dataset is split into training, validation, and testing subsets, with the test set emphasizing out-of-distribution (OOD) scenarios. The system employs convolutional neural networks (CNNs), probabilistic models like S

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Edie, Stewart M., Peter D. Smits, and David Jablonski. "Probabilistic models of species discovery and biodiversity comparisons." Proceedings of the National Academy of Sciences 114, no. 14 (2017): 3666–71. http://dx.doi.org/10.1073/pnas.1616355114.

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Inferring large-scale processes that drive biodiversity hinges on understanding the phylogenetic and spatial pattern of species richness. However, clades and geographic regions are accumulating newly described species at an uneven rate, potentially affecting the stability of currently observed diversity patterns. Here, we present a probabilistic model of species discovery to assess the uncertainty in diversity levels among clades and regions. We use a Bayesian time series regression to estimate the long-term trend in the rate of species description for marine bivalves and find a distinct spati

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YM He. "Online Assessment of Mental Health Micromedia for College Students Incorporating Bayesian Network Algorithm." International Journal of Maritime Engineering 1, no. 1 (2024): 83–96. http://dx.doi.org/10.5750/ijme.v1i1.1340.

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Mental health issues among college students are a growing concern, necessitating effective assessment methods to identify individuals at risk and provide timely interventions. In this paper, we propose and evaluate several computational models for mental health assessment based on demographic, academic, and psychological factors. Hence, this paper implemented the Probabilistic Deep Belief Bayesian Network (PDBBN) to classify students' mental health attributes. The proposed PDBBN network computes the probabilistic value of the mental health assessment of the students. With the estimation of the

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Hou, Yuxin, Ari Heljakka, and Arno Solin. "Gaussian Process Priors for View-Aware Inference." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 9 (2021): 7762–70. http://dx.doi.org/10.1609/aaai.v35i9.16948.

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While frame-independent predictions with deep neural networks have become the prominent solutions to many computer vision tasks, the potential benefits of utilizing correlations between frames have received less attention. Even though probabilistic machine learning provides the ability to encode correlation as prior knowledge for inference, there is a tangible gap between the theory and practice of applying probabilistic methods to modern vision problems. For this, we derive a principled framework to combine information coupling between camera poses (translation and orientation) with deep mode

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Avaylon, Matthew, Robbie Sadre, Zhe Bai, and Talita Perciano. "Adaptable Deep Learning and Probabilistic Graphical Model System for Semantic Segmentation." Advances in Artificial Intelligence and Machine Learning 02, no. 01 (2022): 288–302. http://dx.doi.org/10.54364/aaiml.2022.1119.

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Semantic segmentation algorithms based on deep learning architectures have been applied to a diverse set of problems. Consequently, new methodologies have emerged to push the state-of-the-art in this field forward, and the need for powerful user-friendly software increased significantly. The combination of Conditional Random Fields (CRFs) and Convolutional Neural Networks (CNNs) boosted the results of pixel-level classification predictions. Recent work using a fully integrated CRF-RNN layer have shown strong advantages in segmentation benchmarks over the base models. Despite this success, the

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Nguyen, Minh Truong, Viet-Hung Dang, and 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, no. 4 (2022): 10–15. http://dx.doi.org/10.31276/vjste.64(4).10-15.

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In this article, deep learning probabilistic models are applied to a case study on evaluating the influence of specialized mini projects (SMPs) on the performance of engineering students on their final year project (FYP) and cumulative grade point average (CGPA). This approach also creates a basis to predict the final performance of undergraduate students based on their SMP scores, which is a vital characteristic of engineering training. The study is conducted in two steps: (i) establishing a database by collecting 2890 SMP and FYP scores and the associated CGPA of a group of engineering stude

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De Smet, Lennert, Gabriele Venturato, Luc De Raedt, and Giuseppe Marra. "Relational Neurosymbolic Markov Models." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 15 (2025): 16181–89. https://doi.org/10.1609/aaai.v39i15.33777.

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Sequential problems are ubiquitous in AI, such as in reinforcement learning or natural language processing. State-of-the-art deep sequential models, like transformers, excel in these settings but fail to guarantee the satisfaction of constraints necessary for trustworthy deployment. In contrast, neurosymbolic AI (NeSy) provides a sound formalism to enforce constraints in deep probabilistic models but scales exponentially on sequential problems. To overcome these limitations, we introduce relational neurosymbolic Markov models (NeSy-MMs), a new class of end-to-end differentiable sequential mode

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Sansine, Vateanui, Pascal Ortega, Daniel Hissel, and Franco Ferrucci. "Hybrid Deep Learning Model for Mean Hourly Irradiance Probabilistic Forecasting." Atmosphere 14, no. 7 (2023): 1192. http://dx.doi.org/10.3390/atmos14071192.

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For grid stability, operation, and planning, solar irradiance forecasting is crucial. In this paper, we provide a method for predicting the Global Horizontal Irradiance (GHI) mean values one hour in advance. Sky images are utilized for training the various forecasting models along with measured meteorological data in order to account for the short-term variability of solar irradiance, which is mostly caused by the presence of clouds in the sky. Additionally, deep learning models like the multilayer perceptron (MLP), convolutional neural networks (CNN), long short-term memory (LSTM), or their h

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Nor, Ahmad Kamal Mohd. "Failure Prognostic of Turbofan Engines with Uncertainty Quantification and Explainable AI (XIA)." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 3 (2021): 3494–504. http://dx.doi.org/10.17762/turcomat.v12i3.1624.

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Deep learning is quickly becoming essential to human ecosystem. However, the opacity of certain deep learning models poses a legal barrier in its adoption for greater purposes. Explainable AI (XAI) is a recent paradigm intended to tackle this issue. It explains the prediction mechanism produced by black box AI models, making it extremely practical for safety, security or financially important decision making. In another aspect, most deep learning studies are based on point estimate prediction with no measure of uncertainty which is vital for decision making. Obviously, these works are not suit

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Lee, Taehee, Devin Rand, Lorraine E. Lisiecki, Geoffrey Gebbie та Charles Lawrence. "Bayesian age models and stacks: combining age inferences from radiocarbon and benthic δ¹⁸O stratigraphic alignment". Climate of the Past 19, № 10 (2023): 1993–2012. http://dx.doi.org/10.5194/cp-19-1993-2023.

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Abstract. Previously developed software packages that generate probabilistic age models for ocean sediment cores are designed to either interpolate between different age proxies at discrete depths (e.g., radiocarbon, tephra layers, or tie points) or perform a probabilistic stratigraphic alignment to a dated target (e.g., of benthic δ18O) and cannot combine age inferences from both techniques. Furthermore, many radiocarbon dating packages are not specifically designed for marine sediment cores, and the default settings may not accurately reflect the probability of sedimentation rate variability

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Jasmin, Praful Bharadiya. "A Review of Bayesian Machine Learning Principles, Methods, and Applications." International Journal of Innovative Science and Research Technology 8, no. 5 (2023): 2033–38. https://doi.org/10.5281/zenodo.8020825.

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Bayesian machine learning is a subfield of machine learning that incorporates Bayesian principles and probabilistic models into the learning process. It provides a principled framework for modeling uncertainty, making predictions, and updating beliefs based on observed data. This review article aims to provide an overview of Bayesian machine learning, discussing its foundational concepts, algorithms, and applications. We explore key topics such as Bayesian inference, probabilistic graphical models, Bayesian neural networks, variational inference, Markov chain Monte Carlo methods, and Bayesian

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Jiang, jialong. "Towards a Theoretical Framework for the Explainability of Deep Learning Models." Global Academic Frontiers 3, no. 2 (2025): 149–59. https://doi.org/10.5281/zenodo.15582910.

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Deep learning models have demonstrated outstanding performance in various domains, yet their opaque nature remains a fundamental issue. Explainability aims to bridge this gap by providing insights into model decision-making processes. This paper explores the theoretical foundations of explainability in deep learning, emphasizing mathematical and conceptual perspectives. We investigate the limitations of current approaches and discuss how interdisciplinary methodologies can enhance our understanding of deep learning systems. Additionally, we explore the potential of combining explainability wit

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Ghobadi, Fatemeh, and Doosun Kang. "Multi-Step Ahead Probabilistic Forecasting of Daily Streamflow Using Bayesian Deep Learning: A Multiple Case Study." Water 14, no. 22 (2022): 3672. http://dx.doi.org/10.3390/w14223672.

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In recent decades, natural calamities such as drought and flood have caused widespread economic and social damage. Climate change and rapid urbanization contribute to the occurrence of natural disasters. In addition, their destructive impact has been altered, posing significant challenges to the efficiency, equity, and sustainability of water resources allocation and management. Uncertainty estimation in hydrology is essential for water resources management. By quantifying the associated uncertainty of reliable hydrological forecasting, an efficient water resources management plan is obtained.

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Bentsen, Lars Ødegaard, Narada Dilp Warakagoda, Roy Stenbro, and Paal Engelstad. "Probabilistic Wind Park Power Prediction using Bayesian Deep Learning and Generative Adversarial Networks." Journal of Physics: Conference Series 2362, no. 1 (2022): 012005. http://dx.doi.org/10.1088/1742-6596/2362/1/012005.

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The rapid depletion of fossil-based energy supplies, along with the growing reliance on renewable resources, has placed supreme importance on the predictability of renewables. Research focusing on wind park power modelling has mainly been concerned with point estimators, while most probabilistic studies have been reserved for forecasting. In this paper, a few different approaches to estimate probability distributions for individual turbine powers in a real off-shore wind farm were studied. Two variational Bayesian inference models were used, one employing a multilayered perceptron and another

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Li, Longyuan, Jihai Zhang, Junchi Yan, et al. "Synergetic Learning of Heterogeneous Temporal Sequences for Multi-Horizon Probabilistic Forecasting." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 10 (2021): 8420–28. http://dx.doi.org/10.1609/aaai.v35i10.17023.

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Time-series is ubiquitous across applications, such as transportation, finance and healthcare. Time-series is often influenced by external factors, especially in the form of asynchronous events, making forecasting difficult. However, existing models are mainly designated for either synchronous time-series or asynchronous event sequence, and can hardly provide a synthetic way to capture the relation between them. We propose Variational Synergetic Multi-Horizon Network (VSMHN), a novel deep conditional generative model. To learn complex correlations across heterogeneous sequences, a tailored enc

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Aulia, Hartika, Syaharuddin Syaharuddin*, Vera Mandailina, Hamenyimana Emanuel Gervas, and Hameed Ashraf. "Probabilistic Forecasting of Energy Consumption using Bayesian Dynamic Linear Models." Aceh International Journal of Science and Technology 13, no. 1 (2024): 68–78. http://dx.doi.org/10.13170/aijst.13.1.38291.

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This study aims to conduct a systematic literature review on the development of mathematical models for forecasting energy consumption using a probabilistic approach, particularly focusing on the Bayesian Dynamic Linear Model (BDLM). The research method employed is Systematic Literature Review (SLR), utilizing literature sources indexed in Scopus, DOAJ, and Google Scholar, with publication dates ranging from 2014 to 2024. The findings of the research indicate that the application of BDLM has made a significant contribution to the optimization of energy management, especially in sectors such as

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Lim, Heejong, Kwanghun Chung, and Sangbok Lee. "Probabilistic Forecasting for Demand of a Bike-Sharing Service Using a Deep-Learning Approach." Sustainability 14, no. 23 (2022): 15889. http://dx.doi.org/10.3390/su142315889.

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Efficient and sustainable bike-sharing service (BSS) operations require accurate demand forecasting for bike inventory management and rebalancing. Probabilistic forecasting provides a set of information on uncertainties in demand forecasting, and thus it is suitable for use in stochastic inventory management. Our research objective is to develop probabilistic time-series forecasting for BSS demand. We use an RNN–LSTM-based model, called DeepAR, for the station-wise bike-demand forecasting problem. The deep-learning structure of DeepAR captures complex demand patterns and correlations between t

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Billah, Muhammad Maruf, Abdullah Al Rakib, Md Shakawat Hossain, Mst Kamrun Nahar Borsha, Nazmul Nahid, and Md Nahidul Islam. "A Hybrid Approach to Brain Tumor Detection: Combining Deep Convolutional Networks with Traditional Image Processing Methods for Enhanced MRI Classification." International Journal of Multidisciplinary Research in Science, Engineering and Technology 7, no. 10 (2024): 15001–6. http://dx.doi.org/10.15680/ijmrset.2024.0710001.

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Brain tumour detection is a critical medical procedure where early and accurate diagnosis significantly improves patient outcomes. This study explores the application of deep learning models, specifically VGG19 and InceptionV3, for detecting brain tumours in MRI images. We fine-tuned both models using transfer learning and evaluated them on a dataset of MRI scans. InceptionV3 achieved 100% validation accuracy, while VGG19 achieved 95%, demonstrating their high efficacy in medical image classification. In addition to deep learning, the study integrates insights from traditional image processing

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Zhang, Fahong, Zhiyuan Leng, Lu Chen, and Yongchuan Zhang. "Joint Probabilistic Forecasting of Wind and Solar Power Exploiting Spatiotemporal Complementarity." Sustainability 17, no. 8 (2025): 3584. https://doi.org/10.3390/su17083584.

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Reliable and precise joint probabilistic forecasting of wind and solar power is crucial for optimizing renewable energy utilization and maintaining the safety and stability of modern power systems. This paper presents an innovative joint probabilistic forecasting model designed to address probabilistic spatiotemporal power output forecasting challenges. Leveraging a multi-network deep learning framework, the model integrated the temporal convolutional network for temporal feature extraction, the convolutional neural network for spatial feature analysis, and the attention mechanism for spatiote

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T, Ermolieva, Ermoliev Y, Zagorodniy) A, et al. "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) (2022): 92–97. http://dx.doi.org/10.15407/jai2022.02.092.

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In this paper we discuss the on-going joint work contributing to the IIASA (International Institute for Applied Systems Analysis, Laxenburg, Austria) and National Academy of Science of Ukraine projects on “Modeling and management of dynamic stochastic interdependent systems for food-water-energy-health security nexus” (see [1-2] and references therein). The project develops methodological and modeling tools aiming to create Intelligent multimodel Decision Support System (IDSS) and Platform (IDSP), which can integrate national Food, Water, Energy, Social models with the models operating at the

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Pang, Bo, Erik Nijkamp, and Ying Nian Wu. "Deep Learning With TensorFlow: A Review." Journal of Educational and Behavioral Statistics 45, no. 2 (2019): 227–48. http://dx.doi.org/10.3102/1076998619872761.

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This review covers the core concepts and design decisions of TensorFlow. TensorFlow, originally created by researchers at Google, is the most popular one among the plethora of deep learning libraries. In the field of deep learning, neural networks have achieved tremendous success and gained wide popularity in various areas. This family of models also has tremendous potential to promote data analysis and modeling for various problems in educational and behavioral sciences given its flexibility and scalability. We give the reader an overview of the basics of neural network models such as the mul

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Ali, Abdullah Marish, Fuad A. Ghaleb, Mohammed Sultan Mohammed, Fawaz Jaber Alsolami, and Asif Irshad Khan. "Web-Informed-Augmented Fake News Detection Model Using Stacked Layers of Convolutional Neural Network and Deep Autoencoder." Mathematics 11, no. 9 (2023): 1992. http://dx.doi.org/10.3390/math11091992.

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Today, fake news is a growing concern due to its devastating impacts on communities. The rise of social media, which many users consider the main source of news, has exacerbated this issue because individuals can easily disseminate fake news more quickly and inexpensive with fewer checks and filters than traditional news media. Numerous approaches have been explored to automate the detection and prevent the spread of fake news. However, achieving accurate detection requires addressing two crucial aspects: obtaining the representative features of effective news and designing an appropriate mode

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