Demo Data from EDBT 2026 / reviewers in the wild / expert
Ranjitha Subramaniam
dblp:290/7447
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2021
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 since 2021
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 |
Trustworthy machine learning · 44% Image recognition and object detection · 44% Deep learning architectures and training · 13% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Trustworthy machine learning › robustness
robustness to corruption |
0.5 | 1 | 2021 | Does enhanced shape bias improve neural network robustness to common corruptions? · ICLR 2021 |
Computer vision › Image recognition and object detection
shape bias |
0.5 | 1 | 2021 | Does enhanced shape bias improve neural network robustness to common corruptions? · ICLR 2021 |
Machine learning › Deep learning architectures and training
convolutional neural network |
0.1 | 1 | 2021 | Does enhanced shape bias improve neural network robustness to common corruptions? · ICLR 2021 |
Methods — techniques the papers use, named apart from their topics
shape bias analysis · 0.5data augmentation · 0.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2021 | Does enhanced shape bias improve neural network robustness to common corruptions?
Chaithanya Kumar Mummadi, Ranjitha Subramaniam, Robin Hutmacher, Julien Vitay, Volker Fischer 0003, Jan Hendrik Metzen |
ICLR | 2 |