VLDB 2026 Research / reviewers in the wild / expert
Martin Bjerke
dblp:331/0575
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2023
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-author · 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 |
Representation and self-supervised learning · 67% Kernel, tree and ensemble methods · 33% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Kernel, tree and ensemble methods
ensemble learning |
0.7 | 1 | 2023 | Understanding Neural Coding on Latent Manifolds by Sharing Features and Dividing Ensembles · ICLR 2023 |
Machine learning › Representation and self-supervised learning › shared representation
feature sharing |
0.7 | 1 | 2023 | Understanding Neural Coding on Latent Manifolds by Sharing Features and Dividing Ensembles · ICLR 2023 |
Machine learning › Representation and self-supervised learning › computational neuroscience
neural coding |
0.7 | 1 | 2023 | Understanding Neural Coding on Latent Manifolds by Sharing Features and Dividing Ensembles · ICLR 2023 |
Methods — techniques the papers use, named apart from their topics
latent manifold learning · 0.7ensemble learning · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Understanding Neural Coding on Latent Manifolds by Sharing Features and Dividing Ensembles
Martin Bjerke, Lukas Schott, Kristopher T. Jensen, Claudia Battistin, David A. Klindt, Benjamin A. Dunn |
ICLR | 1 |