Demo Data from VLDB 2026 / reviewers in the wild / expert
Nicolás Alvarado
dblp:336/8903
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2024
—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 |
Deep learning architectures and training · 50% Optimization for machine learning · 50% |
Topics — the 1 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Deep learning architectures and training › training dynamics
optimization dynamics |
0.8 | 1 | 2024 | Hyperbolic Optimizer as a Dynamical System · ICML 2024 |
Methods — techniques the papers use, named apart from their topics
lyapunov stability · 0.8hyperbolic geometry · 0.8differential equations · 0.8
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Hyperbolic Optimizer as a Dynamical SystemabstractDuring the last few years, the field of dynamical systems has been developing innovative tools to study the asymptotic behavior of different optimizers in the context of neural networks. In this work, we redefine an extensively studied optimizer, employing classical techniques from hyperbolic geometry. This new definition is linked to a non-linear differential equation as a continuous limit. Additionally, by utilizing Lyapunov stability concepts, we analyze the asymptotic behavior of its critical points. Nicolás Alvarado, Hans Lobel |
ICML | 1 |