Demo Data from VLDB 2026 / reviewers in the wild / expert
Yuichiro Kikura
dblp:207/2022
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2017
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 1
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 |
Runtime systems and virtual machines · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Efficient and distributed learning
model compression |
0.3 | 1 | 2017 | WebDNN: Fastest DNN Execution Framework on Web Browser · ACM Multimedia 2017 |
Runtime systems and virtual machines › dynamic language implementation
javascript runtime |
0.3 | 1 | 2017 | WebDNN: Fastest DNN Execution Framework on Web Browser · ACM Multimedia 2017 |
Methods — techniques the papers use, named apart from their topics
model optimization · 0.6GPGPU · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2017 | WebDNN: Fastest DNN Execution Framework on Web BrowserabstractRecently, deep neural network (DNN) is drawing a lot of attention because of its applications. However, it requires a lot of computational resources and tremendous processes in order to setup an execution environment based on hardware acceleration such as GPGPU. Therefore, providing DNN applications to end-users is very hard. To solve this problem, we have developed an installation-free web browser-based DNN execution framework, WebDNN. WebDNN optimizes the trained DNN model to compress model data and accelerate the execution. It executes the DNN model with novel JavaScript API to achieve zero-overhead execution. Empirical evaluations show that it achieves more than two-hundred times the unusual acceleration. WebDNN is an open source framework and you can download it from https://github.com/mil-tokyo/webdnn. Masatoshi Hidaka, Yuichiro Kikura, Yoshitaka Ushiku, Tatsuya Harada |
ACM Multimedia | 2 |