EDBT 2026 Demo / reviewers in the wild / expert

Patricia Rubisch

dblp:230/7787 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2019
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 2

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Image recognition and object detection · 50% Trustworthy machine learning · 50%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Trustworthy machine learning
robustness		 0.412019
ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness · ICLR 2019
Computer vision › Image recognition and object detection
shape bias		 0.412019
ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness · ICLR 2019

Methods — techniques the papers use, named apart from their topics

convolutional neural network · 0.4
YearPublicationVenuePosition
2019 Gradient-Based Learning of Compositional Dynamics with Modular RNNs
Sebastian Otte, Patricia Rubisch, Martin V. Butz
 ICANN (1)2
2019 ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness
Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix A. Wichmann, Wieland Brendel
 ICLR2