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Demo Data from EDBT 2026 / reviewers in the wild / expert

Gerben Beintema

dblp:262/0274 · also Gerben Izaak Beintema · DBLP profile ↗
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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
Time series and sequential data · 50% Representation and self-supervised learning · 50%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Time series and sequential data
deep state space model		 0.712023
Continuous-time identification of dynamic state-space models by deep subspace encoding · ICLR 2023
Machine learning › Representation and self-supervised learning › representation learning › dimensionality reduction › subspace learning
subspace embedding		 0.712023
Continuous-time identification of dynamic state-space models by deep subspace encoding · ICLR 2023

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

subspace encoding · 0.7deep learning · 0.7
YearPublicationVenuePosition
2023 Continuous-time identification of dynamic state-space models by deep subspace encoding
Gerben Beintema, Maarten Schoukens, Roland Tóth
 ICLR1