EDBT 2026 Demo / reviewers in the wild / expert
Mario Krenn
dblp:202/2484
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2020
0000-0003-1620-9207ORCID · verified
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1
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 |
Generative modeling · 62% Optimization for machine learning · 38% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Generative modeling
molecular generation |
0.4 | 1 | 2020 | Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Space · ICLR 2020 |
Machine learning › Optimization for machine learning
evolutionary computation |
0.1 | 1 | 2020 | Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Space · ICLR 2020 |
Machine learning › Optimization for machine learning › evolutionary computation
genetic algorithms |
0.1 | 1 | 2020 | Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Space · ICLR 2020 |
Methods — techniques the papers use, named apart from their topics
genetic algorithm · 0.4deep neural network · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2020 | Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Space
AkshatKumar Nigam, Pascal Friederich, Mario Krenn, Alán Aspuru-Guzik |
ICLR | 3 |