Uncertainties In Neural Networks
DOWNLOAD
Download Uncertainties In Neural Networks PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Uncertainties In Neural Networks book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
Bayesian Learning For Neural Networks
DOWNLOAD
Author : Radford M. Neal
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Bayesian Learning For Neural Networks written by Radford M. Neal and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Mathematics categories.
Artificial "neural networks" are widely used as flexible models for classification and regression applications, but questions remain about how the power of these models can be safely exploited when training data is limited. This book demonstrates how Bayesian methods allow complex neural network models to be used without fear of the "overfitting" that can occur with traditional training methods. Insight into the nature of these complex Bayesian models is provided by a theoretical investigation of the priors over functions that underlie them. A practical implementation of Bayesian neural network learning using Markov chain Monte Carlo methods is also described, and software for it is freely available over the Internet. Presupposing only basic knowledge of probability and statistics, this book should be of interest to researchers in statistics, engineering, and artificial intelligence.
Artificial Neural Network Modelling
DOWNLOAD
Author : Subana Shanmuganathan
language : en
Publisher: Springer
Release Date : 2016-02-03
Artificial Neural Network Modelling written by Subana Shanmuganathan and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-02-03 with Technology & Engineering categories.
This book covers theoretical aspects as well as recent innovative applications of Artificial Neural networks (ANNs) in natural, environmental, biological, social, industrial and automated systems. It presents recent results of ANNs in modelling small, large and complex systems under three categories, namely, 1) Networks, Structure Optimisation, Robustness and Stochasticity 2) Advances in Modelling Biological and Environmental Systems and 3) Advances in Modelling Social and Economic Systems. The book aims at serving undergraduates, postgraduates and researchers in ANN computational modelling.
Uncertainty For Safe Utilization Of Machine Learning In Medical Imaging And Graphs In Biomedical Image Analysis
DOWNLOAD
Author : Carole H. Sudre
language : en
Publisher: Springer Nature
Release Date : 2020-10-05
Uncertainty For Safe Utilization Of Machine Learning In Medical Imaging And Graphs In Biomedical Image Analysis written by Carole H. Sudre and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-05 with Computers categories.
This book constitutes the refereed proceedings of the Second International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2020, and the Third International Workshop on Graphs in Biomedical Image Analysis, GRAIL 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020. The workshops were held virtually due to the COVID-19 pandemic. For UNSURE 2020, 10 papers from 18 submissions were accepted for publication. They focus on developing awareness and encouraging research in the field of uncertainty modelling to enable safe implementation of machine learning tools in the clinical world. GRAIL 2020 accepted 10 papers from the 12 submissions received. The workshop aims to bring together scientists that use and develop graph-based models for the analysis of biomedical images and to encourage the exploration of graph-based models for difficult clinical problems within a variety of biomedical imaging contexts.
Uncertainty For Safe Utilization Of Machine Learning In Medical Imaging
DOWNLOAD
Author : Carole H. Sudre
language : en
Publisher: Springer Nature
Release Date : 2023-10-06
Uncertainty For Safe Utilization Of Machine Learning In Medical Imaging written by Carole H. Sudre and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-10-06 with Computers categories.
This book constitutes the refereed proceedings of the 5th Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2023, held in conjunction with MICCAI 2023 in Vancouver, Canada, in October 2023. For this workshop, 21 papers from 32 submissions were accepted for publication. The accepted papers cover the fields of uncertainty estimation and modeling, as well as out of distribution management, domain shift robustness, Bayesian deep learning and uncertainty calibration.
Advances In Intelligent Data Analysis Xix
DOWNLOAD
Author : Pedro Henriques Abreu
language : en
Publisher: Springer Nature
Release Date : 2021-04-12
Advances In Intelligent Data Analysis Xix written by Pedro Henriques Abreu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-12 with Computers categories.
This book constitutes the proceedings of the 19th International Symposium on Intelligent Data Analysis, IDA 2021, which was planned to take place in Porto, Portugal. Due to the COVID-19 pandemic the conference was held online during April 26-28, 2021. The 35 papers included in this book were carefully reviewed and selected from 113 submissions. The papers were organized in topical sections named: modeling with neural networks; modeling with statistical learning; modeling language and graphs; and modeling special data formats.
Machine Learning In Chemistry
DOWNLOAD
Author : Jon Paul Janet
language : en
Publisher: American Chemical Society
Release Date : 2020-05-28
Machine Learning In Chemistry written by Jon Paul Janet and has been published by American Chemical Society this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-05-28 with Science categories.
Recent advances in machine learning or artificial intelligence for vision and natural language processing that have enabled the development of new technologies such as personal assistants or self-driving cars have brought machine learning and artificial intelligence to the forefront of popular culture. The accumulation of these algorithmic advances along with the increasing availability of large data sets and readily available high performance computing has played an important role in bringing machine learning applications to such a wide range of disciplines. Given the emphasis in the chemical sciences on the relationship between structure and function, whether in biochemistry or in materials chemistry, adoption of machine learning by chemistsderivations where they are important
Mathematical Uncertainties And Their Applications
DOWNLOAD
Author : Illia Danilishyn
language : en
Publisher: Primedia eLaunch LLC
Release Date : 2025-05-30
Mathematical Uncertainties And Their Applications written by Illia Danilishyn and has been published by Primedia eLaunch LLC this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-30 with Mathematics categories.
The monograph first task: to understand hierarchy of energies in the Universe and the principles of functioning of living energy (living organism, in particular, human, subtle energies), and then using these principles to "construct" artificial living energies (let's call them pseudo-living energies). It is possible to significantly expand the horizons of science, in particular physics, by studying the subtle energies in the Universe. On the basis of mathematical uncertainties, new mathematical structures are formed, allowing us to describe processes and objects that are fundamentally not determined by conventional deterministic methods. Here is considered new mathematical uncertainties. Objective uncertainties in any case can mean manifestations of processes and objects that are fundamentally not determined by conventional deterministic methods. Many energies are indeterminate because they are based on uncertainties from the perspective of traditional science—large concentrations of specific energy in a chaotic state. The foundation of dynamic mathematics lies in working with uncertainties, which makes it possible to manipulate these indeterminate energies using direct-accumulative direct-parallel neural networks. The second task of the monograph is to construct a new mathematical apparatus for neural networks of a fundamentally new type: direct-parallel and direct-accumulative action. We construct models of singularities for singular work with them through neural networks - analogues of the human CNS. Ordinary regular work with them in ordinary science is fundamentally unable to realize their capabilities. Therefore, singular science realized on a neural network - an analogue of the human CNS - will be much more natural. Unfortunately, we do not have funding to perform the necessary experiments and the practical creation of a technical model of such a neural network. There is a need to develop an instrumental mathematical base for new technologies. The task of the work is to create new approaches for this by introducing new concepts and methods. Our mathematics is unusual for a mathematician, because here the fulcrum is the action, and not the result of the action as in classical mathematics. Therefore, our mathematics is adapted not only to obtain results, but also to directly control actions, which will certainly show its benefits on a fundamentally new type of neural networks with directly parallel calculations, for which it was created. Any action has much greater potential than its result. It is time for physicists to begin studying not only the manifestations of living energies, but also the living energies themselves, which are by no means expressed through objectivity and ordinary energies, although they are capable of manifesting themselves through a lower level - objectivity and ordinary energies. We, as mathematicians, offer a new corresponding apparatus for understanding nature and studying living energies. Significance of the article: in a new qualitatively different approach to the study of complex processes through new mathematical, hierarchical, dynamic structures, in particular those processes that are dealt with by Synergetics. The significance of our article is in the formation of the presumptive mathematical structure of subtle energies, this is being done for the first time in science, and the presumptive classification of the mathematical structures of subtle energies for the first time. The experiments of the 2022 Nobel laureates Asle Ahlen, John Clauser, Anton Zeilinger and the experiments in chemistry Nazhipa Valitov eloquently demonstrate that we are right and that these studies are necessary. Be that as it may, we created classes of new mathematical structures, new mathematical singularities, i.e., made a contribution to the development of mathematics.
Decision Economics Complexity Of Decisions And Decisions For Complexity
DOWNLOAD
Author : Edgardo Bucciarelli
language : en
Publisher: Springer Nature
Release Date : 2020-02-07
Decision Economics Complexity Of Decisions And Decisions For Complexity written by Edgardo Bucciarelli and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-02-07 with Technology & Engineering categories.
This book is based on the International Conference on Decision Economics (DECON 2019). Highlighting the fact that important decision-making takes place in a range of critical subject areas and research fields, including economics, finance, information systems, psychology, small and international business, management, operations, and production, the book focuses on analytics as an emerging synthesis of sophisticated methodology and large data systems used to guide economic decision-making in an increasingly complex business environment. DECON 2019 was organised by the University of Chieti-Pescara (Italy), the National Chengchi University of Taipei (Taiwan), and the University of Salamanca (Spain), and was held at the Escuela politécnica Superior de Ávila, Spain, from 26th to 28th June, 2019. Sponsored by IEEE Systems Man and Cybernetics Society, Spain Section Chapter, and IEEE Spain Section (Technical Co-Sponsor), IBM, Indra, Viewnext, Global Exchange, AEPIA-and-APPIA, with the funding supporting of the Junta de Castilla y León, Spain (ID: SA267P18-Project co-financed with FEDER funds)
Neural Machine Translation
DOWNLOAD
Author : Philipp Koehn
language : en
Publisher: Cambridge University Press
Release Date : 2020-06-18
Neural Machine Translation written by Philipp Koehn and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-18 with Computers categories.
Learn how to build machine translation systems with deep learning from the ground up, from basic concepts to cutting-edge research.
Uncertainties In Neural Networks
DOWNLOAD
Author : Magnus Malmström
language : en
Publisher: Linköping University Electronic Press
Release Date : 2021-04-06
Uncertainties In Neural Networks written by Magnus Malmström and has been published by Linköping University Electronic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-06 with Electronic books categories.
In science, technology, and engineering, creating models of the environment to predict future events has always been a key component. The models could be everything from how the friction of a tire depends on the wheels slip to how a pathogen is spread throughout society. As more data becomes available, the use of data-driven black-box models becomes more attractive. In many areas they have shown promising results, but for them to be used widespread in safety-critical applications such as autonomous driving some notion of uncertainty in the prediction is required. An example of such a black-box model is neural networks (NNs). This thesis aims to increase the usefulness of NNs by presenting an method where uncertainty in the prediction is obtained by linearization of the model. In system identification and sensor fusion, under the condition that the model structure is identifiable, this is a commonly used approach to get uncertainty in the prediction from a nonlinear model. If the model structure is not identifiable, such as for NNs, the ambiguities that cause this have to be taken care of in order to make the approach applicable. This is handled in the first part of the thesis where NNs are analyzed from a system identification perspective, and sources of uncertainty are discussed. Another problem with data-driven black-box models is that it is difficult to know how flexible the model needs to be in order to correctly model the true system. One solution to this problem is to use a model that is more flexible than necessary to make sure that the model is flexible enough. But how would that extra flexibility affect the uncertainty in the prediction? This is handled in the later part of the thesis where it is shown that the uncertainty in the prediction is bounded from below by the uncertainty in the prediction of the model with lowest flexibility required for representing true system accurately. In the literature, many other approaches to handle the uncertainty in predictions by NNs have been suggested, of which some are summarized in this work. Furthermore, a simulation and an experimental studies inspired by autonomous driving are conducted. In the simulation study, different sources of uncertainty are investigated, as well as how large the uncertainty in the predictions by NNs are in areas without training data. In the experimental study, the uncertainty in predictions done by different models are investigated. The results show that, compared to existing methods, the linearization method produces similar results for the uncertainty in predictions by NNs. An introduction video is available at https://youtu.be/O4ZcUTGXFN0 Inom forskning och utveckling har det har alltid varit centralt att skapa modeller av verkligheten. Dessa modeller har bland annat använts till att förutspå framtida händelser eller för att styra ett system till att bete sig som man önskar. Modellerna kan beskriva allt från hur friktionen hos ett bildäck påverkas av hur mycket hjulen glider till hur ett virus kan sprida sig i ett samhälle. I takt med att mer och mer data blir tillgänglig ökar potentialen för datadrivna black-box modeller. Dessa modeller är universella approximationer vilka ska kunna representera vilken godtycklig funktion som helst. Användningen av dessa modeller har haft stor framgång inom många områden men för att verkligen kunna etablera sig inom säkerhetskritiska områden såsom självkörande farkoster behövs en förståelse för osäkerhet i prediktionen från modellen. Neuronnät är ett exempel på en sådan black-box modell. I denna avhandling kommer olika sätt att tillförskaffa sig kunskap om osäkerhet i prediktionen av neuronnät undersökas. En metod som bygger på linjärisering av modellen för att tillförskaffa sig osäkerhet i prediktionen av neuronnätet kommer att presenteras. Denna metod är välbeprövad inom systemidentifiering och sensorfusion under antagandet att modellen är identifierbar. För modeller såsom neuronnät, vilka inte är identifierbara behövs det att det tas hänsyn till tvetydigheterna i modellen. En annan utmaning med datadrivna black-box modeller, är att veta om den valda modellmängden är tillräckligt generell för att kunna modellera det sanna systemet. En lösning på detta problem är att använda modeller som har mer flexibilitet än vad som behövs, det vill säga en överparameteriserad modell. Men hur påverkas osäkerheten i prediktionen av detta? Detta är något som undersöks i denna avhandling, vilken visar att osäkerheten i den överparameteriserad modellen kommer att vara begränsad underifrån av modellen med minst flexibilitet som ändå är tillräckligt generell för att modellera det sanna systemet. Som avslutning kommer dessa resultat att demonstreras i både en simuleringsstudie och en experimentstudie inspirerad av självkörande farkoster. Fokuset i simuleringsstudien är hur osäkerheten hos modellen är i områden med och utan tillgång till träningsdata medan experimentstudien fokuserar på jämförelsen mellan osäkerheten i olika typer av modeller.Resultaten från dessa studier visar att metoden som bygger på linjärisering ger liknande resultat för skattningen av osäkerheten i prediktionen av neuronnät, jämfört med existerande metoder.