Demo Data from EDBT 2026 / reviewers in the wild / expert
Akihiro Nakano
dblp:307/5510
· 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 |
Reinforcement learning · 67% Representation and self-supervised learning · 33% |
Topics — the 3 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Representation and self-supervised learning › representation learning › disentangled representation learning
disentanglement |
0.7 | 1 | 2023 | Interaction-Based Disentanglement of Entities for Object-Centric World Models · ICLR 2023 |
Machine learning › Reinforcement learning › model-based reinforcement learning › world model
object-centric world model |
0.7 | 1 | 2023 | Interaction-Based Disentanglement of Entities for Object-Centric World Models · ICLR 2023 |
Machine learning › Reinforcement learning › model-based reinforcement learning
world model |
0.7 | 1 | 2023 | Interaction-Based Disentanglement of Entities for Object-Centric World Models · ICLR 2023 |
Methods — techniques the papers use, named apart from their topics
interaction-based disentanglement · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Interaction-Based Disentanglement of Entities for Object-Centric World Models
Akihiro Nakano, Masahiro Suzuki 0001, Yutaka Matsuo |
ICLR | 1 |