Demo Data from VLDB 2026 / reviewers in the wild / expert
Randall Balliestro
dblp:435/3674
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2025
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 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 |
Representation and self-supervised learning · 50% Graph learning · 50% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Representation and self-supervised learning › representation learning › unsupervised representation learning › self-supervised representation learning
masked autoencoder |
0.9 | 1 | 2025 | From Linearity to Non-Linearity: How Masked Autoencoders Capture Spatial Correlations · ICCV 2025 |
Machine learning › Graph learning
spatial correlation modeling |
0.9 | 1 | 2025 | From Linearity to Non-Linearity: How Masked Autoencoders Capture Spatial Correlations · ICCV 2025 |
Methods — techniques the papers use, named apart from their topics
masked autoencoding · 0.9linear probing · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | From Linearity to Non-Linearity: How Masked Autoencoders Capture Spatial Correlations
Anthony Bisulco, Rahul Ramesh, Randall Balliestro, Pratik Chaudhari |
ICCV | 3 |