Demo Data from EDBT 2026 / reviewers in the wild / expert
Daehwan Oh
dblp:292/3134
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2022
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 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 |
Efficient and distributed learning · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Efficient and distributed learning
model compression |
0.6 | 1 | 2022 | Encoding Weights of Irregular Sparsity for Fixed-to-Fixed Model Compression · ICLR 2022 |
Machine learning › Efficient and distributed learning › model compression
pruning |
0.6 | 1 | 2022 | Encoding Weights of Irregular Sparsity for Fixed-to-Fixed Model Compression · ICLR 2022 |
Machine learning › Efficient and distributed learning › model compression
weight encoding |
0.6 | 1 | 2022 | Encoding Weights of Irregular Sparsity for Fixed-to-Fixed Model Compression · ICLR 2022 |
Methods — techniques the papers use, named apart from their topics
weight encoding · 0.6fixed-to-fixed compression · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | Encoding Weights of Irregular Sparsity for Fixed-to-Fixed Model Compression
Baeseong Park, Se Jung Kwon, Daehwan Oh, Byeongwook Kim, Dongsoo Lee |
ICLR | 3 |