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29 Αυγ 2018 · Extreme Value Theory for Open Set Classification - GPD and GEV Classifiers. Classification tasks usually assume that all possible classes are present during the training phase. This is restrictive if the algorithm is used over a long time and possibly encounters samples from unknown classes. The recently introduced extreme value machine, a ...
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30 Ιαν 2022 · Extremal Random Forests. Classical methods for quantile regression fail in cases where the quantile of interest is extreme and only few or no training data points exceed it. Asymptotic results from extreme value theory can be used to extrapolate beyond the range of the data, and several approaches exist that use linear regression, kernel ...
15 Φεβ 2020 · Extreme Classification via Adversarial Softmax Approximation. Training a classifier over a large number of classes, known as 'extreme classification', has become a topic of major interest with applications in technology, science, and e-commerce. Traditional softmax regression induces a gradient cost proportional to the number of classes C ...
14 Μαΐ 2022 · The framework is effective, but it easily suffers from overfitting noisy data and lacks uncertainty quantification for the solution under noise scenarios.To this end, we develop the Bayesian physics-informed extreme learning machine (BPIELM) to solve both forward and inverse linear PDE problems with noisy data in a unified framework.
17 Ιουλ 2023 · Nevertheless, their efficacy and applicability in achieving extreme compression ratios (<0.1 bpp) still remain constrained. In this work, we propose a simple yet effective coding framework by introducing vector quantization (VQ)-based generative models into the image compression domain. The main insight is that the codebook learned by the VQGAN ...
