Download or read book Mixture Density Networks for Distribution and Uncertainty Estimation written by Axel Brando Guillaumes and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The deep learning techniques have made neural networks the leading option for solving some computational problems and it has been shown the production of the state-of-the-art results in many fields like computer vision, automatic speech recognition, natural language processing, and audio recognition. In fact, we may be tempted to make use of neural networks directly, as we know them nowadays, in order to make predictions and solve many problems, but if the decision that has to be taken is of high risk. For instance, we could have a problem regarding the control of a nuclear power plant or the prediction of the shares evolution in the market; in this case, it would be important to look for methods that allowed us to add more information concerning the certainty of those predictions. This Master's thesis is divided into three parts: Firstly, we will analyse the state-of-the-art regarding Mixture Density Network models to predict an entire probability distribution for the output and we will develop an implementation to give solutions for many of the numerical stability problems that characterise this type of models. Secondly, in order to propose an initial solution for the uncertainty problems introduced above, we will focus on the extraction of a confidence factor by using neural network outputs of a problem for which we are only interested in the prediction of something if we have a minimum certainty about the prediction we made. In order to do it, we will compile the current literature methods to measure uncertainty through Mixture Density Networks and we will implement all of these works. Consequently, we are going to to go into detail about the concept of uncertainty and we will see to what extent we are able to propose a solution by using neural network models for the different aspects that include such concept. Finally, the third part will refer to several proposals to measure the confidence factor obtained with the use of Mixture Density Network concerning the problem proposed. After all the work, our goals will be achieved: we are going to make a stable implementation for all the problems that we have proposed for Mixture Density Networks and we will publish it publicly in our GitHub repository[9]. We will be able to implement the state-of-theart methods that will allow us to obtain a confidence factor and finally we will be able to propose a method that obtains the expected results regarding the parameters that represent the confidence factor.
Download or read book Performance Engineering and Stochastic Modeling written by Paolo Ballarini and published by Springer Nature. This book was released on 2021-11-26 with total page 499 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 17th European Workshop on Computer Performance Engineering, EPEW 2021, and the 26th International Conference, on Analytical and Stochastic Modelling Techniques and Applications, ASMTA 2021, held in December 2021. The conference was held virtually due to COVID 19 pandemic. The 29 papers presented in this volume were carefully reviewed and selected from 39 submissions. The papers presented at the workshop reflect the diversity of modern performance evaluation, with topics ranging from modeling and analysis of network/control protocols and high performance/big data information systems, analysis of scheduling, blockchain technology, analytical modeling and simulation of computer and network systems.
Download or read book Finite Mixture Distributions written by B. Everitt and published by Springer. This book was released on 1981-05-28 with total page 160 pages. Available in PDF, EPUB and Kindle. Book excerpt: General introduction; Mixtures of normal distributions; Mixtures of exponential and other continuous distributions; Mixtures of discrete distributions; Miscellaneous topics.
Download or read book A Mixture based Framework for Nonparametric Density Estimation written by Chew-Seng Chee and published by . This book was released on 2011 with total page 142 pages. Available in PDF, EPUB and Kindle. Book excerpt: The primary goal of this thesis is to provide a mixture-based framework for nonparametric density estimation. This framework advocates the use of a mixture model with a nonparametric mixing distribution to approximate the distribution of the data. The implementation of a mixture-based nonparametric density estimator generally requires the specification of parameters in a mixture model and the choice of the bandwidth parameter. Consequently, a nonparametric methodology consisting of both the estimation and selection steps is described. For the estimation of parameters in mixture models, we employ the minimum disparity estimation framework within which there exist several estimation approaches differing in the way smoothing is incorporated in the disparity objective function. For the selection of the bandwidth parameter, we study some popular methods such as cross-validation and information criteria-based model selection methods. Also, new algorithms are developed for the computation of the mixture-based nonparametric density estimates. A series of studies on mixture-based nonparametric density estimators is presented, ranging from the problems of nonparametric density estimation in general to estimation under constraints. The problem of estimating symmetric densities is firstly investigated, followed by an extension in which the interest lies in estimating finite mixtures of symmetric densities. The third study utilizes the idea of double smoothing in defining the least squares criterion for mixture-based nonparametric density estimation. For these problems, numerical studies whether using both simulated and real data examples suggest that the performance of the mixture-based nonparametric density estimators is generally better than or at least competitive with that of the kernel-based nonparametric density estimators. The last but not least concern is nonparametric estimation of continuous and discrete distributions under shape constraints. Particularly, a new model called the discrete k-monotone is proposed for estimating the number of unknown species. In fact, the discrete k- monotone distribution is a mixture of specific discrete beta distributions. Empirica results indicate that the new model outperforms the commonly used nonparametric Poisson mixture model in the context of species richness estimation. Although there remain issues to be resolved, the promising results from our series of studies make the mixture-based framework a valuable tool for nonparametric density estimation.
Download or read book Probabilistic Parametric Curves for Sequence Modeling written by Hug, Ronny and published by KIT Scientific Publishing. This book was released on 2022-07-12 with total page 224 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work proposes a probabilistic extension to Bézier curves as a basis for effectively modeling stochastic processes with a bounded index set. The proposed stochastic process model is based on Mixture Density Networks and Bézier curves with Gaussian random variables as control points. A key advantage of this model is given by the ability to generate multi-mode predictions in a single inference step, thus avoiding the need for Monte Carlo simulation.
Download or read book Statistical Analysis of Finite Mixture Distributions written by D. M. Titterington and published by . This book was released on 1985 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book, the authors give a complete account of the applications, mathematical structure and statistical analysis of finite mixture distributions.
Download or read book Artificial Neural Networks and Machine Learning ICANN 2023 written by Lazaros Iliadis and published by Springer Nature. This book was released on 2023-09-21 with total page 619 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 10-volume set LNCS 14254-14263 constitutes the proceedings of the 32nd International Conference on Artificial Neural Networks and Machine Learning, ICANN 2023, which took place in Heraklion, Crete, Greece, during September 26–29, 2023. The 426 full papers, 9 short papers and 9 abstract papers included in these proceedings were carefully reviewed and selected from 947 submissions. ICANN is a dual-track conference, featuring tracks in brain inspired computing on the one hand, and machine learning on the other, with strong cross-disciplinary interactions and applications.
Download or read book Improved Gaussian Mixture Density Estimates Using Bayesian Penalty Terms and Network Averaging written by Dirk Ormoneit and published by . This book was released on 1995 with total page 12 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Pattern Recognition ICPR International Workshops and Challenges written by Alberto Del Bimbo and published by Springer Nature. This book was released on 2021-03-04 with total page 749 pages. Available in PDF, EPUB and Kindle. Book excerpt: This 8-volumes set constitutes the refereed of the 25th International Conference on Pattern Recognition Workshops, ICPR 2020, held virtually in Milan, Italy and rescheduled to January 10 - 11, 2021 due to Covid-19 pandemic. The 416 full papers presented in these 8 volumes were carefully reviewed and selected from about 700 submissions. The 46 workshops cover a wide range of areas including machine learning, pattern analysis, healthcare, human behavior, environment, surveillance, forensics and biometrics, robotics and egovision, cultural heritage and document analysis, retrieval, and women at ICPR2020.
Download or read book Music and AI written by Alexandra Bonnici and published by Frontiers Media SA. This book was released on 2021-03-16 with total page 170 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Topics in Density Estimation written by Niklaus Walter Hengartner and published by . This book was released on 1993 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Computer Vision ECCV 2020 written by Andrea Vedaldi and published by Springer Nature. This book was released on 2020-12-03 with total page 847 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 30-volume set, comprising the LNCS books 12346 until 12375, constitutes the refereed proceedings of the 16th European Conference on Computer Vision, ECCV 2020, which was planned to be held in Glasgow, UK, during August 23-28, 2020. The conference was held virtually due to the COVID-19 pandemic. The 1360 revised papers presented in these proceedings were carefully reviewed and selected from a total of 5025 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation.
Download or read book Computer Vision ACCV 2022 written by Lei Wang and published by Springer Nature. This book was released on 2023-03-03 with total page 687 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 7-volume set of LNCS 13841-13847 constitutes the proceedings of the 16th Asian Conference on Computer Vision, ACCV 2022, held in Macao, China, December 2022. The total of 277 contributions included in the proceedings set was carefully reviewed and selected from 836 submissions during two rounds of reviewing and improvement. The papers focus on the following topics: Part I: 3D computer vision; optimization methods; Part II: applications of computer vision, vision for X; computational photography, sensing, and display; Part III: low-level vision, image processing; Part IV: face and gesture; pose and action; video analysis and event recognition; vision and language; biometrics; Part V: recognition: feature detection, indexing, matching, and shape representation; datasets and performance analysis; Part VI: biomedical image analysis; deep learning for computer vision; Part VII: generative models for computer vision; segmentation and grouping; motion and tracking; document image analysis; big data, large scale methods.
Download or read book Data Management Analytics and Innovation written by Neha Sharma and published by Springer Nature. This book was released on 2020-09-18 with total page 454 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the latest findings in the areas of data management and smart computing, big data management, artificial intelligence and data analytics, along with advances in network technologies. Gathering peer-reviewed research papers presented at the Fourth International Conference on Data Management, Analytics and Innovation (ICDMAI 2020), held on 17–19 January 2020 at the United Services Institute (USI), New Delhi, India, it addresses cutting-edge topics and discusses challenges and solutions for future development. Featuring original, unpublished contributions by respected experts from around the globe, the book is mainly intended for a professional audience of researchers and practitioners in academia and industry.
Download or read book Statistical Postprocessing of Ensemble Forecasts written by Stéphane Vannitsem and published by Elsevier. This book was released on 2018-05-17 with total page 364 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical Postprocessing of Ensemble Forecasts brings together chapters contributed by international subject-matter experts describing the current state of the art in the statistical postprocessing of ensemble forecasts. The book illustrates the use of these methods in several important applications including weather, hydrological and climate forecasts, and renewable energy forecasting. After an introductory section on ensemble forecasts and prediction systems, the second section of the book is devoted to exposition of the methods available for statistical postprocessing of ensemble forecasts: univariate and multivariate ensemble postprocessing are first reviewed by Wilks (Chapters 3), then Schefzik and Möller (Chapter 4), and the more specialized perspective necessary for postprocessing forecasts for extremes is presented by Friederichs, Wahl, and Buschow (Chapter 5). The second section concludes with a discussion of forecast verification methods devised specifically for evaluation of ensemble forecasts (Chapter 6 by Thorarinsdottir and Schuhen). The third section of this book is devoted to applications of ensemble postprocessing. Practical aspects of ensemble postprocessing are first detailed in Chapter 7 (Hamill), including an extended and illustrative case study. Chapters 8 (Hemri), 9 (Pinson and Messner), and 10 (Van Schaeybroeck and Vannitsem) discuss ensemble postprocessing specifically for hydrological applications, postprocessing in support of renewable energy applications, and postprocessing of long-range forecasts from months to decades. Finally, Chapter 11 (Messner) provides a guide to the ensemble-postprocessing software available in the R programming language, which should greatly help readers implement many of the ideas presented in this book. Edited by three experts with strong and complementary expertise in statistical postprocessing of ensemble forecasts, this book assesses the new and rapidly developing field of ensemble forecast postprocessing as an extension of the use of statistical corrections to traditional deterministic forecasts. Statistical Postprocessing of Ensemble Forecasts is an essential resource for researchers, operational practitioners, and students in weather, seasonal, and climate forecasting, as well as users of such forecasts in fields involving renewable energy, conventional energy, hydrology, environmental engineering, and agriculture. - Consolidates, for the first time, the methodologies and applications of ensemble forecasts in one succinct place - Provides real-world examples of methods used to formulate forecasts - Presents the tools needed to make the best use of multiple model forecasts in a timely and efficient manner
Download or read book Statistical Foundations of Actuarial Learning and its Applications written by Mario V. Wüthrich and published by Springer Nature. This book was released on 2022-11-22 with total page 611 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book discusses the statistical modeling of insurance problems, a process which comprises data collection, data analysis and statistical model building to forecast insured events that may happen in the future. It presents the mathematical foundations behind these fundamental statistical concepts and how they can be applied in daily actuarial practice. Statistical modeling has a wide range of applications, and, depending on the application, the theoretical aspects may be weighted differently: here the main focus is on prediction rather than explanation. Starting with a presentation of state-of-the-art actuarial models, such as generalized linear models, the book then dives into modern machine learning tools such as neural networks and text recognition to improve predictive modeling with complex features. Providing practitioners with detailed guidance on how to apply machine learning methods to real-world data sets, and how to interpret the results without losing sight of the mathematical assumptions on which these methods are based, the book can serve as a modern basis for an actuarial education syllabus.
Download or read book Intelligent Control Systems and Signal Processing 2003 written by M. G. Ruano and published by Elsevier Publishing Company. This book was released on 2003 with total page 560 pages. Available in PDF, EPUB and Kindle. Book excerpt: KEY FEATURES: The first IFAC conference and thus proceedings to be specifically devoted to this field Presents the findings of experts and practitioners from the major soft- computing themes Provides an overview of the theory and applications of intelligent control systems and signal processing Intelligent control systems and signal processing 2003 contains the selection of papers presented at the IFAC International Conference on Intelligent Control systems and Signal Processing (ICONS) 2003. The conference was sponsored by the most important organizations in the field, among them were the Institue of Electrical and Electronic Engineers (IEEE), and the Control Systems Society (CSS) This proceedings volume contains 98 papers, with three separate reviewers having reviewed all papers, Including six plenary lectures given by leading experts in the field.
