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Autor:
Gallego M, Joseph
The main goal of this thesis is to develop efficient non-parametric density estimation methods that can be integrated with deep learning architectures, for instance, convolutional neural networks and transformers. Density estimation methods can be ap
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https://explore.openaire.eu/search/publication?articleId=doi_dedup___::604c3e8f709da6e852fc5d94e6e68321
Autor:
Gallego M, Joseph
Our work shows that estimating the mean in a feature space induced by certain1 kinds of kernels is the same as doing a robust mean estimation using an M-estimator2 in the original problem space. In particular, we show that calculating the average on3
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c88000e0108f422308c0f3ac9793a2b1
Autor:
Gallego M, Joseph, Gonzalez O, Fabio
This paper shows that least-square estimation (mean calculation) in a reproducing kernel Hilbert space (RKHS) $\mathcal{F}$ corresponds to different M-estimators in the original space depending on the kernel function associated with $\mathcal{F}$. In
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::30be9ada16b6e14082eca5526e65e806