Workshop title: Open Workshop in Machine Learning in Communications(OPENMLC)
Machine Learning (ML), and specifically deep learning, has become a prominent and rapidly growing research topic within the field of wireless communications, both in academia and industry. In a discipline traditionally driven by compact analytic mathematical models, ML brings along a methodology that is data-driven and carries a major shift in the way wireless systems are designed and optimized. This brings with it both promise of more accurately representing complexities of the real world, as well as a great challenge in providing the same levels of analytic performance guarantee and validation we are used to in communications systems.
Beyond providing a platform for the latest high-quality results in the field of machine learning for communication systems and encouraging fruitful and controversial discussions on the core challenges and prospect of the field, this workshop seeks to follow the main theme of the ICC 2020 version in promoting and encouraging open-ness, rigor, and reproducibility. As data measurement, processing, and learning systems are often significantly more intricate and specialized than compact analytic models, they often contain numerous details regarding the composition of the dataset, hyper-parameters and processing stages used within the learning and inference process, and countless additional implementation details which are difficult to compactly document within a concise and compact paper, but are easily captured within open software and data publications. This has become the norm in a number of machine learning-centric venues (e.g. NeurIPS, ICML), and rigorous new algorithmic work requires the publication and verification of open research. To embrace this within the IEEE ecosystem, this workshop is focused on directly supporting open-ness within machine learning for communications research, and asking researchers to share datasets, code, implementations, and baselines used throughout their work to help facilitate reproducibility and quantitative comparison by others within the field who may be able to critique, leverage, or extend research when it is conducted in such an open and reproducible manner.
Date: 11 December, 2020
Venue: Taipei International Convention Center (TICC) and Taipei World Trade Center (TWTC) and Virtual