Alexander Meinke

发表

T OWARDS NEURAL NETWORKS THAT PROVABLY KNOW WHEN THEY DON ’ T KNOW

Alexander Meinke, 2019 .

Adversarial Robustness on In- and Out-Distribution Improves Explainability

Matthias Hein, Alexander Meinke, Maximilian Augustin, 2020, ECCV.

Nonparametric Maximum Entropy Estimation on Information Diagrams

J. Hlinka, J. Davidsen, Alexander Meinke, 2016, 1601.00336.

Provably Adversarially Robust Detection of Out-of-Distribution Data (Almost) for Free

Matthias Hein, Alexander Meinke, Julian Bitterwolf, 2021, NeurIPS.

Provably Adversarially Robust Detection of Out-of-Distribution Data (Almost) for Free

Matthias Hein, Alexander Meinke, Julian Bitterwolf, 2022, NeurIPS.

Breaking Down Out-of-Distribution Detection: Many Methods Based on OOD Training Data Estimate a Combination of the Same Core Quantities

Matthias Hein, Alexander Meinke, Maximilian Augustin, 2022, ICML.

Classifiers Should Do Well Even on Their Worst Classes

Matthias Hein, Alexander Meinke, Julian Bitterwolf, 2022 .

Certifiably Adversarially Robust Detection of Out-of-Distribution Data

Matthias Hein, Julian Bitterwolf, Alexander Meinke, 2020, NeurIPS.

Towards neural networks that provably know when they don't know

Matthias Hein, Alexander Meinke, 2019, ICLR.

Provable Worst Case Guarantees for the Detection of Out-of-Distribution Data pdf

Matthias Hein, Julian Bitterwolf, Alexander Meinke, 2020, ArXiv.

Network Inference and Maximum Entropy Estimation on Information Diagrams

Elliot A Martin, Jaroslav Hlinka, Jörn Davidsen, 2017, Scientific Reports.