EDBT 2026 Demo / reviewers in the wild / expert
Qizhengqiu Lu
dblp:291/2916
· 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 |
Representation and self-supervised learning · 33% Time series and sequential data · 33% Learning paradigms · 33% |
Topics — the 3 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Learning paradigms
continual learning |
0.6 | 1 | 2022 | How Well Does Self-Supervised Pre-Training Perform with Streaming Data? · ICLR 2022 |
Machine learning › Representation and self-supervised learning › representation learning › unsupervised representation learning
self-supervised representation learning |
0.6 | 1 | 2022 | How Well Does Self-Supervised Pre-Training Perform with Streaming Data? · ICLR 2022 |
Machine learning › Time series and sequential data
streaming data |
0.6 | 1 | 2022 | How Well Does Self-Supervised Pre-Training Perform with Streaming Data? · ICLR 2022 |
Methods — techniques the papers use, named apart from their topics
self-supervised pretraining · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | How Well Does Self-Supervised Pre-Training Perform with Streaming Data?
Dapeng Hu, Shipeng Yan, Qizhengqiu Lu, Lanqing Hong, Hailin Hu 0002, Yifan Zhang 0004, Zhenguo Li, Xinchao Wang, Jiashi Feng |
ICLR | 3 |