Lower Bounds on Complexity of Shallow Perceptron Networks

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F16%3A00460704" target="_blank" >RIV/67985807:_____/16:00460704 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-319-44188-7_21" target="_blank" >http://dx.doi.org/10.1007/978-3-319-44188-7_21</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-44188-7_21" target="_blank" >10.1007/978-3-319-44188-7_21</a>

Result language
angličtina
Original language name
Lower Bounds on Complexity of Shallow Perceptron Networks
Original language description
Model complexity of shallow (one-hidden-layer) perceptron networks computing multivariable functions on finite domains is investigated. Lower bounds are derived on growth of the number of network units or sizes of output weights in terms of variations of functions to be computed. A concrete construction of a class of functions which cannot be computed by perceptron networks with considerably smaller numbers of units and output weights than the sizes of the function’s domains is presented. In particular, functions on Boolean d-dimensional cubes are constructed which cannot be computed by shallow perceptron networks with numbers of hidden units and sizes of output weights depending on d polynomially. A subclass of these functions is described whose elements can be computed by two-hidden-layer networks with the number of units depending on d linearly.
Czech name
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Czech description
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Project
<a href="/en/project/GA15-18108S" target="_blank" >GA15-18108S: Model complexity of neural, radial, and kernel networks</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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