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_c5315 _d5315 |
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| 001 | 00005315 | ||
| 003 | FR-PaOEC | ||
| 005 | 20211006062616.0 | ||
| 006 | a o d i | ||
| 007 | cr || |||m|n|| | ||
| 008 | 190601s2017 ||| o i|0| 0 eng d | ||
| 035 | _a(FR-PaOEC) | ||
| 040 |
_aFR-PaOEC _cES-MaONT |
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| 100 |
_aSamek, Wojciech _93410 |
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| 245 | 1 | 0 |
_aExplainable artificial intelligence : _bUnderstanding, visualizing and interpreting deep learning models / _cWojciech Samek, Thomas Wiegand and Klaus-Robert Müller |
| 260 |
_aGeneva : _bInternational Telecommunication Union, _c2017. |
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| 300 | _a10 p. | ||
| 336 |
_atexto (visual) _2isbdcontent |
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| 337 |
_aelectrónico _2isbdmedia |
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| 338 |
_arecurso en línea _2rdacarrier |
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| 520 | 3 | _aWith the availability of large databases and recent improvements in deep learning methodology, the performance of AI systems is reaching, or even exceeding, the human level on an increasing number of complex tasks. Impressive examples of this development can be found in domains such as image classification, sentiment analysis, speech understanding or strategic game playing. However, because of their nested non-linear structure, these highly successful machine learning and artificial intelligence models are usually applied in a black-box manner, i.e. no information is provided about what exactly makes them arrive at their predictions. Since this lack of transparency can be a major drawback, e.g. in medical applications, the development of methods for visualizing, explaining and interpreting deep learning models has recently attracted increasing attention. This paper summarizes recent developments in this field and makes a plea for more interpretability in artificial intelligence. Furthermore, it presents two approaches to explaining predictions of deep learning models, one method which computes the sensitivity of the prediction with respect to changes in the input and one approach which meaningfully decomposes the decision in terms of the input variables. These methods are evaluated on three classification tasks. | |
| 650 | 0 |
_aTecnologías habilitadoras digitales _918 |
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| 653 | 4 | _aScience and Technology | |
| 700 |
_aWiegand, Thomas _93413 |
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| 700 | 1 |
_aMüller, Klaus-Robert _93330 |
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| 773 | 0 |
_04843 _tITU Journal: ICT Discoveries _gVol. 2018, no. 1, p. 39-48 _q2018:1<39 _x2616-8375 |
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| 856 | 4 | 0 |
_aitu-ilibrary.org _uhttps://www.itu.int/dms_pub/itu-s/opb/journal/S-JOURNAL-ICTS.V1I1-2017-5-PDF-E.pdf _yAcceso al documento _x0 _qpdf |
| 942 |
_cART _2udc |
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