Demo Data from EDBT 2026 / reviewers in the wild / expert
Yu Jing Goh
dblp:295/5280
· 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 · 33% Representation and self-supervised learning · 33% Deep learning architectures and training · 33% |
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
adversarial robustness |
0.5 | 1 | 2021 | ARMOURED: Adversarially Robust MOdels using Unlabeled data by REgularizing Diversity · ICLR 2021 |
Machine learning › Deep learning architectures and training › regularization › feature regularization
diversity regularization |
0.5 | 1 | 2021 | ARMOURED: Adversarially Robust MOdels using Unlabeled data by REgularizing Diversity · ICLR 2021 |
Machine learning › Representation and self-supervised learning
unlabeled data |
0.5 | 1 | 2021 | ARMOURED: Adversarially Robust MOdels using Unlabeled data by REgularizing Diversity · ICLR 2021 |
Methods — techniques the papers use, named apart from their topics
regularization · 0.5adversarial training · 0.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2021 | ARMOURED: Adversarially Robust MOdels using Unlabeled data by REgularizing Diversity
Kangkang Lu 0001, Cuong Manh Nguyen, Xun Xu 0002, Kiran Chari, Yu Jing Goh, Chuan-Sheng Foo |
ICLR | 5 |