The number of very large data repositories (big data) is increasing in a rapid pace. Analysis of such repositories using the traditional sequential implementations of Machine Learning (ML) and emerging techniques, like deep learning, that model high-level abstractions in data by using multiple processing layers, requires expensive computational resources and long running times.
Parallel or distributed computing are possible approaches that can make analysis of very large repositories and exploration of high-level representations feasible. Taking advantage of a parallel or a distributed execution of a ML/statistical system may: i) increase its speed; ii) learn hidden representations; iii) search a larger space and reach a better solution or; iv) increase the range of applications where it can be used (because it can process more data, for example). Parallel and distributed computing is therefore of high importance to extract knowledge from massive amounts of data and learn hidden representations.
The workshop will be concerned with the exchange of experience among academics, researchers and the industry whose work in big data and deep learning require high performance computing to achieve goals. Participants will present recently developed algorithms/systems, on going work and applications taking advantage of such parallel or distributed environments.
Papers submitted to BDL 2022 must describe original research results and must not have been published or simultaneously submitted anywhere else.
Manuscripts must follow the IEEE conference formatting guidelines and submitted via the EasyChair Conference Management System as one pdf file. The strict page limit for initial submission and camera-ready version is 8 pages in the aforementioned format.
Each paper will receive a minimum of three reviews by members of the international technical program committee. Papers will be selected based on their originality, relevance, technical clarity and quality of presentation. At least one author of each accepted paper must register for the BDL 2022 workshop and present the paper.
All accepted papers will be published at IEEE Xplore.
