Neural networks for calibration tomography
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Neural networks for calibration tomography

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Published by National Aeronautics and Space Administration, National Technical Information Service, distributor in [Washington, DC, Springfield, Va .
Written in English

Subjects:

  • Tomography.,
  • Neural networks (Computer science)

Book details:

Edition Notes

StatementArthur Decker.
SeriesNASA technical memorandum -- 106164., NASA technical memorandum -- 106164.
ContributionsUnited States. National Aeronautics and Space Administration.
The Physical Object
FormatMicroform
Pagination1 v.
ID Numbers
Open LibraryOL14697918M

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Artificial neural networks are suitable for performing pattern-to-pattern calibrations. These calibrations are potentially useful for facilities operations in aeronautics, the control of optical alignment, and the like. This paper compares computed tomography with neural net calibration tomography for estimating density from its x-ray by: 6. Artificial neural networks are suitable for performing pattern-to-pattern calibrations. These calibrations are potentially useful for facilities operations in aeronautics, the control of optical alignment, and the like. Computed tomography is compared with neural net calibration tomography for estimating density from its x-ray : Arthur Decker. Neural network (NN) utilization is an alternative approach to tomographic problem solution. Neural networks have a number of favorable features, the most important qualities of them being adaptability and generalization. The adaptability of NN to specific conditions of the problem is the result of training.   We discover that modern neural networks, unlike those from a decade ago, are poorly calibrated. Through extensive experiments, we observe that depth, width, weight decay, and Batch Normalization are important factors influencing calibration. We evaluate the performance of various post-processing calibration Cited by:

Calibration through neural networks The calibration problem can been reduced to finding a neural net-work to approximate. The problem is split into two: a training phase, which would normally be done offline, and the evaluation, which gives the model parameters for a given input Training phase: 1 Collect large training set of calibrated examples. A Neural Network Approach to Real-Time Discrete Tomography K.J. Batenburg 1,2 and W.A. Kosters 1 Leiden University, Leiden, The Netherlands [email protected] 2 CWI, Amsterdam, The Netherlands [email protected] Abstract. Tomography deals with thereconstruction of thedensity dis- tribution inside an unknown object from its projections in several direc-. neural networks typically produce well-calibrated proba-bilities on binary classification tasks. While neural net-works today are undoubtedly more accurate than they were a decade ago, we discover with great surprise that mod-ern neural networks are no longer well-calibrated. This is visualized inFigure 1, which compares a 5-layer LeNetFile Size: 1MB.   Model Calibration with Neural Networks. Contribute to Andres-Hernandez/CalibrationNN development by creating an account on GitHub.

Get this from a library! Neural networks for calibration tomography. [Arthur J Decker; United States. National Aeronautics and Space Administration.]. Typically, the ANN calibration procedure is made up of two steps. First, the neural network is trained with market data to set its node weights. Second, the parameters of some standard pricing models, such as the Black-Scholes model, are calibrated using the trained network, so . On Calibration of Modern Neural Networks Chuan Guo * 1 Geoff Pleiss * 1 Yu Sun * 1 Kilian Q. Weinberger 1 Abstract Condence calibration the problem of predict-ing probability estimates representative of the true correctness likelihood is important for classication models in many applications. We discover that modern neural networks, unlikeCited by: Neural Networks Calibration Introduction Universal approximation Training Neuron An ANN is simply a network of regression units stacked in a particular configuration. Each regression unit, called a neuron, takes input from the previous layer 1,combinesthatinputac-cording to a rule, and applies a function on the result: x 1 w 1 x n w n Σ b.