VLDB 2026 Research / reviewers in the wild / expert
Jaroslaw Bilski
dblp:84/122
· DBLP profile ↗
2ranked-venue papers
2as first author
1since 2021 · last 2021
0000-0003-1769-3934ORCID · verified
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 1 · 1 first-authorDatabases, data management, data science and information retrieval · 1 · 1 first-author · 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.
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Parallel and multicore computing · 67% Hardware accelerators and domain-specific architectures · 33% | |
| Artificial intelligence
1 paper |
Deep learning architectures and training · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Hardware accelerators and domain-specific architectures › machine learning accelerator
neural network accelerator |
0.2 | 1 | 2015 | Parallel Architectures for Learning the RTRN and Elman Dynamic Neural Networks · IEEE Trans. Parallel Distributed Syst. 2015 |
Parallel and multicore computing
parallel architecture |
0.2 | 1 | 2015 | Parallel Architectures for Learning the RTRN and Elman Dynamic Neural Networks · IEEE Trans. Parallel Distributed Syst. 2015 |
Parallel and multicore computing › parallel computing › parallel machine learning
parallel neural network training |
0.2 | 1 | 2015 | Parallel Architectures for Learning the RTRN and Elman Dynamic Neural Networks · IEEE Trans. Parallel Distributed Syst. 2015 |
Machine learning › Deep learning architectures and training
recurrent neural network |
0.1 | 1 | 2015 | Parallel Architectures for Learning the RTRN and Elman Dynamic Neural Networks · IEEE Trans. Parallel Distributed Syst. 2015 |
Methods — techniques the papers use, named apart from their topics
vector processing · 0.4parallelization · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2021 | A novel method for speed training acceleration of recurrent neural networks
Jaroslaw Bilski, Leszek Rutkowski, Jacek Smolag, Dacheng Tao |
Inf. Sci. | 1 |
| 2015 | Parallel Architectures for Learning the RTRN and Elman Dynamic Neural NetworksabstractA major problem encountered by researchers of dynamic neural networks is the computational complexity increasing the learning time. In this paper the parallel realization of the RTRN and the Elman networks are discussed. Both networks are examples of dynamic neural networks. Inherent parallelism of dynamic neural networks has been employed to accelerate the learning process. The proposed solution is based on a highly parallel three dimensional architecture to speed up the learning performance. The presented structures are suitable for efficient parallel realization in digital hardware or vector processors. Jaroslaw Bilski, Jacek Smolag |
IEEE Trans. Parallel Distributed Syst. | 1 |