Demo Data from VLDB 2026 / reviewers in the wild / expert
Jinglin Wei
dblp:397/7304
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2026
0009-0001-0301-5975ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 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% | |
| Software engineering, system software, and programming languages
1 paper |
Compilers and program optimization · 100% |
Topics — the 2 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Efficient and distributed learning
model compression |
1.0 | 1 | 2026 | LLMFolder: Revisiting Constant Folding in Large Language Models · EuroSys 2026 |
Compilers and program optimization › compiler optimization › local optimization
constant folding |
0.3 | 1 | 2026 | LLMFolder: Revisiting Constant Folding in Large Language Models · EuroSys 2026 |
Methods — techniques the papers use, named apart from their topics
activation function linearization · 2.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | LLMFolder: Revisiting Constant Folding in Large Language ModelsabstractLarge language models (LLMs) demonstrate remarkable capabilities but face deployment challenges due to their massive parameter counts. While pruning can reduce model size, it leads to significant accuracy degradation under high compression ratios. We present a novel perspective inspired by constant folding in compiler optimization. Our approach enables parameter reduction by treating activation functions in LLMs as linear functions. Gansen Hu, Jinglin Wei, Haibo Chen 0001 |
EuroSys | 4 |