1 code implementation • 8 Aug 2022 • David Jacob Kedziora, Tien-Dung Nguyen, Katarzyna Musial, Bogdan Gabrys
The automated machine learning (AutoML) process can require searching through complex configuration spaces of not only machine learning (ML) components and their hyperparameters but also ways of composing them together, i. e. forming ML pipelines.
no code implementations • 11 Mar 2022 • Huu-Quoc Nguyen, Tien-Dung Nguyen, Van-Nam Pham, Xuan-Qui Pham, Quang-Thai Ngo, Eui-Nam Huh
In virtual desktop infrastructure (VDI) environments, the remote display protocol has a big responsibility to transmit video data from a data center-hosted desktop to the endpoint.
2 code implementations • 1 May 2021 • Tien-Dung Nguyen, David Jacob Kedziora, Katarzyna Musial, Bogdan Gabrys
Machine learning (ML) pipeline composition and optimisation have been studied to seek multi-stage ML models, i. e. preprocessor-inclusive, that are both valid and well-performing.
no code implementations • 26 Jan 2021 • Marc-André Zöller, Tien-Dung Nguyen, Marco F. Huber
We prove the effectiveness and competitiveness of our approach on 28 data sets used in well-established AutoML benchmarks in comparison with state-of-the-art AutoML frameworks.
1 code implementation • 21 Nov 2020 • Tien-Dung Nguyen, Bogdan Gabrys, Katarzyna Musial
Instead of executing the original ML pipeline to evaluate its validity, the AVATAR evaluates its surrogate model constructed by capabilities and effects of the ML pipeline components and input/output simplified mappings.
no code implementations • 10 May 2020 • Tien-Dung Nguyen
The Incremental K-means (IKM), an improved version of K-means (KM), was introduced to improve the clustering quality of KM significantly.
no code implementations • 30 Jan 2020 • Tien-Dung Nguyen, Tomasz Maszczyk, Katarzyna Musial, Marc-Andre Zöller, Bogdan Gabrys
The evaluation of machine learning (ML) pipelines is essential during automatic ML pipeline composition and optimisation.