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Self-supervised Gaussian Process (SSL-GP)

Michakato ya Gaussian ni modeli ya uwezekano ambayo hairudishi tu utabiri bali pia usambazaji kamili wa kutokuwa na uhakika — lakini kernel yake lazima iwe na muundo unaofaa wa data, na kuchagua muundo huo ni vigumu bila mifano ya kutosha yenye lebo. Mafunzo ya awali ya kujifundisha yanatatua hili kwa kwanza kufundisha GP (au kichanganuzi cha kina kinachoingia kwenye GP) kurekebisha upya pembejeo zilizofichwa, kutabiri maadili yajayo, au kutatua kazi zingine za awali zisizo na dalili kwenye data isiyo na lebo. Kufikia wakati mifano yenye lebo inapoletwa, GP tayari inaelewa jiometri ya msingi na utofauti wa data, kwa hivyo makadirio yake ya kutokuwa na uhakika yamerekebishwa zaidi na utabiri wake wa wastani ni sahihi zaidi.

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Vyanzo

  1. Fortuin, V., Rätsch, G., & Mandt, S. (2020). GP-VAE: Deep probabilistic time series imputation using Gaussian process variational autoencoders. Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), PMLR 108, 1651–1661. link
  2. Gaussian process. Wikipedia. link

Jinsi ya kunukuu ukurasa huu

ScholarGate. (2026, June 3). Self-supervised Gaussian Process (SSL-GP). ScholarGate. https://scholargate.app/sw/machine-learning/self-supervised-gaussian-process

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ScholarGateSelf-supervised Gaussian Process (Self-supervised Gaussian Process (SSL-GP)). Imepatikana 2026-06-15 kutoka https://scholargate.app/sw/machine-learning/self-supervised-gaussian-process · Seti ya data: https://doi.org/10.5281/zenodo.20539026