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Demo Data from EDBT 2026 / reviewers in the wild / expert

Cleomar Pereira da Silva

dblp:00/9882 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2015
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-author

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
Efficient and distributed learning · 50% Deep learning architectures and training · 50%

Topics — the 1 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Efficient and distributed learning › hardware acceleration
GPU acceleration		 0.212015
Evolving GPU machine code · J. Mach. Learn. Res. 2015

Methods — techniques the papers use, named apart from their topics

genetic programming · 0.2evolutionary computation · 0.2
YearPublicationVenuePosition
2015 Evolving GPU machine code
Cleomar Pereira da Silva, Douglas Mota Dias, Cristiana Bentes, Marco Aurélio Pacheco, Leandro F. Cupertino
 J. Mach. Learn. Res.1