321,724 research outputs found
Neural Network Parameterizations of Electromagnetic Nucleon Form Factors
The electromagnetic nucleon form-factors data are studied with artificial
feed forward neural networks. As a result the unbiased model-independent
form-factor parametrizations are evaluated together with uncertainties. The
Bayesian approach for the neural networks is adapted for chi2 error-like
function and applied to the data analysis. The sequence of the feed forward
neural networks with one hidden layer of units is considered. The given neural
network represents a particular form-factor parametrization. The so-called
evidence (the measure of how much the data favor given statistical model) is
computed with the Bayesian framework and it is used to determine the best form
factor parametrization.Comment: The revised version is divided into 4 sections. The discussion of the
prior assumptions is added. The manuscript contains 4 new figures and 2 new
tables (32 pages, 15 figures, 2 tables
Efficient Algorithms for Artificial Neural Networks and Explainable AI
Artificial neural networks have allowed some remarkable progress in fields such as pattern recognition and computer vision. However, the increasing complexity of artificial neural networks presents a challenge for efficient computation. In this thesis, we first introduce a novel matrix multiplication method to reduce the complexity of artificial neural networks, where we demonstrate its suitability to compress fully connected layers of artificial neural networks. Our method outperforms other state-of-the-art methods when tested on standard publicly available datasets. This thesis then focuses on Explainable AI, which can be critical in fields like finance and medicine, as it can provide explanations for some decisions taken by sub-symbolic AI models behaving like a black box such as Artificial neural networks and transformation based learning approaches. We have also developed a new framework that facilitates the use of Explainable AI with tabular datasets. Our new framework Exmed, enables nonexpert users to prepare data, train models, and apply Explainable AI techniques effectively.Additionally, we propose a new algorithm that identifies the overall influence of input features and minimises the perturbations that alter the decision taken by a given model. Overall, this thesis introduces innovative and comprehensive techniques to enhance the efficiency of fully connected layers in artificial neural networks and provide a new approach to explain their decisions. These methods have significant practical applications in various fields, including portable medical devices
ADANN: Automatic Design of Artificial Neural Networks
Proceeding of: Genetic and Evolutionary Computation Conference, GECCO-08. July 12-16, 2008, Atlanta, Georgia, USA.In this work an improvement of an initial approach to design Artificial Neural Networks to forecast Time Series is tackled, and the automatic process to design Artificial Neural Networks is carried out by a Genetic Algorithm. A key issue for these kinds of approaches is what information is included in the chromosome that represents an Artificial Neural Network. In this approach new information will be included into the chromosome so it will be possible to compare these results with those obtained in a previous approach. There are two principal ideas to take into account: first, the chromosome contains information about parameters of the topology, architecture, learning parameters, etc. of the Artificial Neural Network, i.e. Direct Encoding Scheme; second, the chromosome contains the necessary information so that a constructive method gives rise to an Artificial Neural Network topology (or architecture), i.e. Indirect Encoding Scheme. The results for a Direct Encoding Scheme (in order to compare with Indirect Encoding Schemes developed in future works) to design Artificial Neural Networks to forecast Time Series are shown.The research reported here has been supported by the Ministry of Education and Science under project TRA2007-67374-C02-02
Explainable Artificial Intelligence for Mechanics: Physics-Explaining Neural Networks for Constitutive Models
(Artificial) neural networks have become increasingly popular in mechanics and materials sciences to accelerate computations with model order reduction techniques and as universal models for a wide variety of materials. However, the major disadvantage of neural networks remains: their numerous parameters are challenging to interpret and explain. Thus, neural networks are often labeled as black boxes, and their results often elude human interpretation. The new and active field of physics-informed neural networks attempts to mitigate this disadvantage by designing deep neural networks on the basis of mechanical knowledge. By using this a priori knowledge, deeper and more complex neural networks became feasible, since the mechanical assumptions can be explained. However, the internal reasoning and explanation of neural network parameters remain mysterious. Complementary to the physics-informed approach, we propose a first step towards a physics-explaining approach, which interprets neural networks trained on mechanical data a posteriori. This proof-of-concept explainable artificial intelligence approach aims at elucidating the black box of neural networks and their high-dimensional representations. Therein, the principal component analysis decorrelates the distributed representations in cell states of RNNs and allows the comparison to known and fundamental functions. The novel approach is supported by a systematic hyperparameter search strategy that identifies the best neural network architectures and training parameters. The findings of three case studies on fundamental constitutive models (hyperelasticity, elastoplasticity, and viscoelasticity) imply that the proposed strategy can help identify numerical and analytical closed-form solutions to characterize new materials
Artificial Neural Networks. A New Approach to Modelling Interregional Telecommunication Flows
During the last thirty years there has been much research effort in regional science
devoted to modelling interactions over geographic space. Theoretical approaches for
studying these phenomena have been modified considerably. This paper suggests a 'new
modelling approach, based upon a general nested sigmoid neural network model. Its
feasibility is illustrated in the context of modelling interregional telecommunication traffic in
Austria and its performance is evaluated in comparison with the classical regression
approach of the gravity type. The application of this neural network approach may be
viewed as a three-stage process. The first stage refers to the identification of an
appropriate network from the family of two-layered feedforward networks with 3 input
nodes, one layer of (sigmoidal) intermediate nodes and one (sigmoidal) output node
(logistic activation function). There is no general procedure to address this problem. We
solved this issue experimentally. The input-output dimensions have been chosen in order
to make the comparison with the gravity model as close as possible. The second stage
involves the estimation of the network parameters of the selected neural network model.
This is perlormed via the adaptive setting of the network parameters (training, estimation)
by means of the application of a least mean squared error goal and the error back
propagating technique, a recursive learning procedure using a gradient search to
minimize the error goal. Particular emphasis is laid on the sensitivity of the network
perlormance to the choice of the initial network parameters as well as on the problem of
overlitting. The final stage of applying the neural network approach refers to the testing of
the interregional teletraffic flows predicted. Prediction quality is analysed by means of two
perlormance measures, average relative variance and the coefficient of determination, as
well as by the use of residual analysis. The analysis shows that the neural network model
approach outperlorms the classical regression approach to modelling telecommunication
traffic in Austria. (authors' abstract)Series: Discussion Papers of the Institute for Economic Geography and GIScienc
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