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Statistical Explorations of Determin...

Hoyt, Christopher Richard,

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Statistical Explorations of Deterministic Functions /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Statistical Explorations of Deterministic Functions // Christopher Richard Hoyt.
作者:	Hoyt, Christopher Richard,
面頁冊數:	1 electronic resource (143 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-04, Section: B.
Contained By:	Dissertations Abstracts International85-04B.
標題:	Decomposition. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30615117
ISBN:	9798380484169

Statistical Explorations of Deterministic Functions /
Hoyt, Christopher Richard,

Statistical Explorations of Deterministic Functions /Christopher Richard Hoyt. - 1 electronic resource (143 pages)

Source: Dissertations Abstracts International, Volume: 85-04, Section: B.

In this thesis, we discuss several ways that statistical approaches can be used to investigate black box functions. In the first part of the thesis, we consider the mean dimension, a statistical property of functions concerning how important the interactions between the inputs matter. We show that the mean dimension for some functions, like Gaussian radial basis functions or ridge functions, can vary widely while other functions, like multiquadric radial basis functions, converge to one under weak conditions. In the second part of the talk, we consider probing neural networks, an strategy in which we examine the intermediate outputs as a way to learn more about the mechanics of the overall network. By using tSNE and a class-specific analogue of principle components, we can visualize the progression of how the classifications are made. Further examination are made with tours, an visualization technique that animates interpolations between pairs of projections.

English

ISBN: 9798380484169Subjects--Topical Terms:

3561186
Decomposition.

Statistical Explorations of Deterministic Functions /
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520 $a In this thesis, we discuss several ways that statistical approaches can be used to investigate black box functions. In the first part of the thesis, we consider the mean dimension, a statistical property of functions concerning how important the interactions between the inputs matter. We show that the mean dimension for some functions, like Gaussian radial basis functions or ridge functions, can vary widely while other functions, like multiquadric radial basis functions, converge to one under weak conditions. In the second part of the talk, we consider probing neural networks, an strategy in which we examine the intermediate outputs as a way to learn more about the mechanics of the overall network. By using tSNE and a class-specific analogue of principle components, we can visualize the progression of how the classifications are made. Further examination are made with tours, an visualization technique that animates interpolations between pairs of projections.
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