Demo Data from VLDB 2026 / reviewers in the wild / expert
Soyoung Yoo
dblp:259/1293
· DBLP profile ↗
3ranked-venue papers
1as first author
3since 2021 · last 2023
0000-0003-4129-9152ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Computer networks · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 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 |
Generative modeling · 100% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Computational science and engineering · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Generative modeling › computational creativity › creative generation
design generation |
0.6 | 1 | 2022 | Generative Design by Reinforcement Learning: Enhancing the Diversity of Topology Optimization Designs · Comput. Aided Des. 2022 |
Computational science and engineering › structural optimization
topology optimization |
0.6 | 1 | 2022 | Generative Design by Reinforcement Learning: Enhancing the Diversity of Topology Optimization Designs · Comput. Aided Des. 2022 |
Methods — techniques the papers use, named apart from their topics
reinforcement learning · 1.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Residual Attention Assisted Calibrated Beam Training for mmWave Communication SystemsabstractWe propose a novel framework named CNN-RALSTM model, which aims to enhance the accuracy of beam prediction in deep learning-based calibrated beam training for mmWave communications systems. Our approach differs from prior works as we leverage a residual attention network to extract features from complex signals efficiently during the training phase. The experimental results substantiate the effectiveness of our residual attention network and demonstrate that the proposed model yields better performance. Jihyung Kim 0001, Soyoung Yoo |
ICCCN | 2 |
| 2022 | Generative Design by Reinforcement Learning: Enhancing the Diversity of Topology Optimization Designs
Seowoo Jang, Soyoung Yoo, Namwoo Kang |
Comput. Aided Des. | 2 |
| 2021 | Explainable artificial intelligence for manufacturing cost estimation and machining feature visualization
Soyoung Yoo, Namwoo Kang |
Expert Syst. Appl. | 1 |