NDLI: Neural Networks for Astronomical Data Analysis and Bayesian Inference

Content Provider	IEEE Xplore Digital Library
Author	Graff, P. Feroz, F. Hobson, M.P. Lasenby, A.
Copyright Year	2013
Description	Author affiliation: Astrophys. Group, Cavendish Lab., Cambridge, UK (Feroz, F.; Hobson, M.P.) \|\| Kavli Inst. for Cosmol., Cambridge, UK (Lasenby, A.) \|\| NASA Goddard Space Flight Center, Gravitational Astrophys. Lab., Greenbelt, MD, USA (Graff, P.)
Abstract	We present our generic neural network training algorithm, called Sky Net and the accelerated Bayesian inference algorithm, BAMBI. Sky Net combines multiple techniques already developed individually in the literature to create an efficient and robust machine-learning tool that is able to train large and deep feed-forward neural networks for use in a wide range of learning applications, such as regression, classification, density estimation, clustering and dimensionality reduction. Sky Net uses a powerful `pre-training' method, to obtain a set of network parameters close to the true global maximum of the training objective function, followed by further optimisation using an automatically-regularised variant of Newton's method that uses second-order derivative information to improve convergence, but without the need to evaluate or store the full Hessian matrix, by using a fast approximate method to calculate Hessian-vector products. This combination of methods allows for the training of complicated networks that are difficult to optimise using standard back propagation techniques. The blind accelerated multimodal Bayesian inference (BAMBI) algorithm implements the MultiNest package for nested sampling as well as the training of an artificial neural network by Sky Net to learn the likelihood function. In the case of computationally expensive likelihoods, this allows the substitution of a much more rapid approximation in order to increase significantly the speed of the analysis. Astrophysical examples are provided for both Sky Net and BAMBI.
Sponsorship	Toshiba
Starting Page	16
Ending Page	23
File Size	372557
Page Count	8
File Format	PDF
e-ISBN	9781479931422
DOI	10.1109/ICDMW.2013.82
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Training data Artificial neural networks Inference algorithms Bayes methods Approximation methods
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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