598 citations · 2.9k across the 89 of their papers we have counts for
11 papers · 1 filter
Out-distribution aware Self-training in an Open World Setting
Maximilian Augustin, Matthias Hein
Deep Learning heavily depends on large labeled datasets which limits further improvements. While unlabeled data is available in large amounts, in particular in image recognition, i…
RobustBench: a standardized adversarial robustness benchmark
Francesco Croce, Maksym Andriushchenko, Vikash Sehwag +5
As a research community, we are still lacking a systematic understanding of the progress on adversarial robustness which often makes it hard to identify the most promising ideas in…
Learnable Uncertainty under Laplace Approximations
Agustinus Kristiadi, Matthias Hein, Philipp Hennig
Laplace approximations are classic, computationally lightweight means for constructing Bayesian neural networks (BNNs). As in other approximate BNNs, one cannot necessarily expect…
An Infinite-Feature Extension for Bayesian ReLU Nets That Fixes Their Asymptotic Overconfidence
Agustinus Kristiadi, Matthias Hein, Philipp Hennig
A Bayesian treatment can mitigate overconfidence in ReLU nets around the training data. But far away from them, ReLU Bayesian neural networks (BNNs) can still underestimate uncerta…
Certifiably Adversarially Robust Detection of Out-of-Distribution Data
Julian Bitterwolf, Alexander Meinke, Matthias Hein
Deep neural networks are known to be overconfident when applied to out-of-distribution (OOD) inputs which clearly do not belong to any class. This is a problem in safety-critical a…
Bit Error Robustness for Energy-Efficient DNN Accelerators
David Stutz, Nandhini Chandramoorthy, Matthias Hein +1
Deep neural network (DNN) accelerators received considerable attention in past years due to saved energy compared to mainstream hardware. Low-voltage operation of DNN accelerators…