no code implementations • 23 Sep 2023 • Jiaqi Wen, Bogdan Gabrys, Katarzyna Musial
This study aims to improve the expressive power of node features in Digital Twin-Oriented Complex Networked Systems (DT-CNSs) with heterogeneous feature representation principles.
1 code implementation • 5 Sep 2023 • Adrian Wilkins-Caruana, Madhushi Bandara, Katarzyna Musial, Daniel Catchpoole, Paul J. Kennedy
This study aims to infer the actual treatment steps for a particular patient group from administrative health records (AHR) - a common form of tabular healthcare data - and address several technique- and methodology-based gaps in treatment pathway-inference research.
no code implementations • 27 Aug 2023 • Adrian Caruana, Madhushi Bandara, Katarzyna Musial, Daniel Catchpoole, Paul J. Kennedy
We identify and analyse which machine learning techniques are applied to AHRs and which health informatics applications are pursued in AHR-based research.
no code implementations • 18 Aug 2023 • Jiaqi Wen, Bogdan Gabrys, Katarzyna Musial
This study proposes an extendable modelling framework for Digital Twin-Oriented Complex Networked Systems (DT-CNSs) with a goal of generating networks that faithfully represent real systems.
no code implementations • 12 Jan 2023 • Mingshan Jia, Bogdan Gabrys, Katarzyna Musial
The mining and exploitation of graph structural information have been the focal points in the study of complex networks.
no code implementations • 8 Nov 2022 • Alexander Scriven, David Jacob Kedziora, Katarzyna Musial, Bogdan Gabrys
With most technical fields, there exists a delay between fundamental academic research and practical industrial uptake.
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 • 15 Feb 2022 • Jiaqi Wen, Bogdan Gabrys, Katarzyna Musial
This paper aims to provide a comprehensive critical overview on how entities and their interactions in Complex Networked Systems (CNS) are modelled across disciplines as they approach their ultimate goal of creating a Digital Twin (DT) that perfectly matches the reality.
1 code implementation • 16 Dec 2021 • Xuanyi Dong, David Jacob Kedziora, Katarzyna Musial, Bogdan Gabrys
That stated, NAS is not the be-all and end-all of AutoDL.
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.
2 code implementations • 23 Dec 2020 • David Jacob Kedziora, Katarzyna Musial, Bogdan Gabrys
Over the last decade, the long-running endeavour to automate high-level processes in machine learning (ML) has risen to mainstream prominence, stimulated by advances in optimisation techniques and their impact on selecting ML models/algorithms.
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.
2 code implementations • 28 Aug 2020 • Xuanyi Dong, Lu Liu, Katarzyna Musial, Bogdan Gabrys
In this paper, we propose NATS-Bench, a unified benchmark on searching for both topology and size, for (almost) any up-to-date NAS algorithm.
no code implementations • 2 Jun 2020 • Hongxu Chen, Hongzhi Yin, Xiangguo Sun, Tong Chen, Bogdan Gabrys, Katarzyna Musial
Moreover, to adapt the proposed method to be capable of handling large-scale social networks, we propose a two-phase space reconciliation mechanism to align the embedding spaces in both network partitioning based parallel training and account matching across different social networks.
no code implementations • 13 May 2020 • Joakim Skarding, Bogdan Gabrys, Katarzyna Musial
Second, we present a comprehensive survey of dynamic graph neural network models using the proposed terminology
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.
no code implementations • 6 Jan 2020 • Xueyan Liu, Bo Yang, Wenzhuo Song, Katarzyna Musial, Wanli Zuo, Hongxu Chen, Hongzhi Yin
To preserve the attribute information, we assume that each node has a hidden embedding related to its assigned block.
no code implementations • 11 Oct 2019 • Santhilata Kuppili Venkata, Katarzyna Musial
When data stores and users are distributed geographically, it is essential to organize distributed data cache points at ideal locations to minimize data transfers.
1 code implementation • 22 May 2019 • Akanda Wahid -Ul- Ashraf, Marcin Budka, Katarzyna Musial
Also, the majority of the available social network datasets do not contain both the features and ground truth labels.