Demo Data from EDBT 2026 / reviewers in the wild / expert
Baris Kayalibay
dblp:194/2562
· DBLP profile ↗
2ranked-venue papers
0as first author
2since 2021 · last 2021
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2 · 2 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
2 papers |
Probabilistic and Bayesian machine learning · 36% Robot navigation and mapping · 32% 3D vision · 16% |
Topics — the 7 heaviest of 7, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Probabilistic and Bayesian machine learning › probabilistic inference › approximate inference › variational inference
amortized inference |
0.5 | 1 | 2021 | Mind the Gap when Conditioning Amortised Inference in Sequential Latent-Variable Models · ICLR 2021 |
Robotics › Robot navigation and mapping › robot mapping › dense mapping
dense 3d mapping |
0.5 | 1 | 2021 | Variational State-Space Models for Localisation and Dense 3D Mapping in 6 DoF · ICLR 2021 |
Computer vision › 3D vision
object pose estimation |
0.5 | 1 | 2021 | Variational State-Space Models for Localisation and Dense 3D Mapping in 6 DoF · ICLR 2021 |
Robotics › Robot navigation and mapping
SLAM |
0.5 | 1 | 2021 | Variational State-Space Models for Localisation and Dense 3D Mapping in 6 DoF · ICLR 2021 |
Machine learning › Deep learning architectures and training
state space model |
0.5 | 1 | 2021 | Variational State-Space Models for Localisation and Dense 3D Mapping in 6 DoF · ICLR 2021 |
Machine learning › Probabilistic and Bayesian machine learning › probabilistic inference › approximate inference
variational inference |
0.5 | 1 | 2021 | Mind the Gap when Conditioning Amortised Inference in Sequential Latent-Variable Models · ICLR 2021 |
Machine learning › Probabilistic and Bayesian machine learning › structured models › latent variable model
sequential latent variable model |
0.1 | 1 | 2021 | Mind the Gap when Conditioning Amortised Inference in Sequential Latent-Variable Models · ICLR 2021 |
Methods — techniques the papers use, named apart from their topics
variational inference · 0.5state-space modeling · 0.5amortized inference · 0.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2021 | Mind the Gap when Conditioning Amortised Inference in Sequential Latent-Variable Models
Justin Bayer, Maximilian Soelch 0001, Atanas Mirchev, Baris Kayalibay, Patrick van der Smagt |
ICLR | 4 |
| 2021 | Variational State-Space Models for Localisation and Dense 3D Mapping in 6 DoF
Atanas Mirchev, Baris Kayalibay, Patrick van der Smagt, Justin Bayer |
ICLR | 2 |