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Zwiększanie zrozumiałości sieci neuronowych

Zwiększanie zrozumiałości sieci neuronowych

Enoch Arulprakash / Martin Aruldoss

121,00 €
IVA incluido
Disponible
Editorial:
KS OmniScriptum Publishing
Año de edición:
2025
Materia
Empresa y gestión
ISBN:
9786202371162
121,00 €
IVA incluido
Disponible

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Sztuczna inteligencja (AI) oparta na sieciach neuronowych ma kluczowe znaczenie w wielu zastosowaniach, takich jak systemy rekomendacji, tłumaczenia językowe, media społecznościowe, chatboty, sprawdzanie pisowni itp. Sieci te są jednak często krytykowane za to, że są 'czarnymi skrzynkami', co budzi obawy o ich wyjaśnialność, zwłaszcza w delikatnych dziedzinach, takich jak opieka zdrowotna, autonomiczna jazda itp. Istniejące metody zwiększania wyjaśnialności, takie jak znaczenie cech, często nie są jasne i łatwe do interpretacji. Aby temu zaradzić, opracowano Object-Oriented Neural Network for Improved Explainability (OONNIE). OONNIE wykorzystuje modelowanie obiektowe do łączenia strat i wag połączeń w celu obliczania znaczenia funkcji i integruje reguły specyficzne dla domeny dzięki rozszerzalności OOP. Model kładzie nacisk na przejrzystość algorytmów, szczegółowo opisując każdy etap szkolenia. Oceniany na funkcjach XOR i XNOR, OONNIE wykazuje obiecujące wyniki w zakresie znaczenia cech, szybszej redukcji strat i lepszych prognoz po zintegrowaniu reguł domenowych. Stanowi to znaczący wkład w wyjaśnialną sztuczną inteligencję, dzięki czemu OONNIE jest cennym narzędziem do opracowywania godnych zaufania systemów sztucznej inteligencji.

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