84 citations · 233 across the 21 of their papers we have counts for
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stat.ML2018
Single-Model Uncertainties for Deep Learning
Natasa Tagasovska, David Lopez-Paz
We provide single-model estimates of aleatoric and epistemic uncertainty for deep neural networks. To estimate aleatoric uncertainty, we propose Simultaneous Quantile Regression (S…
stat.ML2018
First-order Adversarial Vulnerability of Neural Networks and Input Dimension
Carl-Johann Simon-Gabriel, Yann Ollivier, Léon Bottou +2
Over the past few years, neural networks were proven vulnerable to adversarial images: targeted but imperceptible image perturbations lead to drastically different predictions. We…