Zobrazeno 1 - 5
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pro vyhledávání: '"Peychev, Momchil"'
While the ImageNet dataset has been driving computer vision research over the past decade, significant label noise and ambiguity have made top-1 accuracy an insufficient measure of further progress. To address this, new label-sets and evaluation prot
Externí odkaz:
http://arxiv.org/abs/2401.02430
Autor:
Dorner, Florian E., Peychev, Momchil, Konstantinov, Nikola, Goel, Naman, Ash, Elliott, Vechev, Martin
Text classifiers have promising applications in high-stake tasks such as resume screening and content moderation. These classifiers must be fair and avoid discriminatory decisions by being invariant to perturbations of sensitive attributes such as ge
Externí odkaz:
http://arxiv.org/abs/2212.10154
Fair representation learning transforms user data into a representation that ensures fairness and utility regardless of the downstream application. However, learning individually fair representations, i.e., guaranteeing that similar individuals are t
Externí odkaz:
http://arxiv.org/abs/2111.13650
The notion of disentangled autoencoders was proposed as an extension to the variational autoencoder by introducing a disentanglement parameter $\beta$, controlling the learning pressure put on the possible underlying latent representations. For certa
Externí odkaz:
http://arxiv.org/abs/1711.09159
Autor:
Peychev, Momchil Pavlinov1 momchil.peychev@gmail.com
Publikováno v:
Proceedings of the International Conference on Information Technologies. 2011, p197-204. 8p.
