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Elementary Derivative Tasks and Neural Net Multiscale Analysis of Tasks
Neural nets are known to be universal approximators. In particular, formal
neurons implementing wavelets have been shown to build nets able to approximate
any multidimensional task. Such very specialized formal neurons may be,
however, difficult to obtain biologically and/or industrially. In this paper we
relax the constraint of a strict ``Fourier analysis'' of tasks. Rather, we use
a finite number of more realistic formal neurons implementing elementary tasks
such as ``window'' or ``Mexican hat'' responses, with adjustable widths. This
is shown to provide a reasonably efficient, practical and robust,
multifrequency analysis. A training algorithm, optimizing the task with respect
to the widths of the responses, reveals two distinct training modes. The first
mode induces some of the formal neurons to become identical, hence promotes
``derivative tasks''. The other mode keeps the formal neurons distinct.Comment: latex neurondlt.tex, 7 files, 6 figures, 9 pages [SPhT-T01/064],
submitted to Phys. Rev.
PVAMU Report of the President 1989-1992
https://digitalcommons.pvamu.edu/priority-tasks-report/1003/thumbnail.jp
Accounting and reporting guide for insurance agents and brokers; Exposure draft (American Institute of Certified Public Accountants), 1989, May 3
https://egrove.olemiss.edu/aicpa_sop/1527/thumbnail.jp
Catechesis and its six tasks
The aim of catechesis is not simply to give information to interlocutors about the Catholic faith. Catechesis seeks to do much more than this, with its ultimate aim being that of putting the human being in communion with Christ. This is achieved through the six tasks of catechesis which need to be deeply embedded in human experiences. The six tasks through which catechesis seeks to achieve its endeavours include: knowledge of the faith, liturgical education, moral formation, formation in prayer and methods of prayer, education for community life and missionary initiation.peer-reviewe
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