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Found 19 papers, 8 papers with code
Heterogeneous Feature Representation for Digital Twin-Oriented Complex Networked Systems
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.
Inferring Actual Treatment Pathways from Patient Records
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.
Machine Learning for Administrative Health Records: A Systematic Review of Techniques and Applications
We identify and analyse which machine learning techniques are applied to AHRs and which health informatics applications are pursued in AHR-based research.
Digital Twin-Oriented Complex Networked Systems based on Heterogeneous Node Features and Interaction Rules
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.
A Network Science perspective of Graph Convolutional Networks: A survey
The mining and exploitation of graph structural information have been the focal points in the study of complex networks.
The Technological Emergence of AutoML: A Survey of Performant Software and Applications in the Context of Industry
With most technical fields, there exists a delay between fundamental academic research and practical industrial uptake.
On Taking Advantage of Opportunistic Meta-knowledge to Reduce Configuration Spaces for Automated Machine Learning
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.
Towards Digital Twin Oriented Modelling of Complex Networked Systems and Their Dynamics: A Comprehensive Survey
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.
Automated Deep Learning: Neural Architecture Search Is Not the End
That stated, NAS is not the be-all and end-all of AutoDL.
Exploring Opportunistic Meta-knowledge to Reduce Search Spaces for Automated Machine Learning
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.
AutonoML: Towards an Integrated Framework for Autonomous Machine Learning
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.
AutoWeka4MCPS-AVATAR: Accelerating Automated Machine Learning Pipeline Composition and Optimisation
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.
NATS-Bench: Benchmarking NAS Algorithms for Architecture Topology and Size
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.
Multi-level Graph Convolutional Networks for Cross-platform Anchor Link Prediction
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.
Foundations and modelling of dynamic networks using Dynamic Graph Neural Networks: A survey
Second, we present a comprehensive survey of dynamic graph neural network models using the proposed terminology
AVATAR -- Machine Learning Pipeline Evaluation Using Surrogate Model
The evaluation of machine learning (ML) pipelines is essential during automatic ML pipeline composition and optimisation.
A Block-based Generative Model for Attributed Networks Embedding
To preserve the attribute information, we assume that each node has a hidden embedding related to its assigned block.
Sub-query Fragmentation for Query Analysis and Data Caching in the Distributed Environment
When data stores and users are distributed geographically, it is essential to organize distributed data cache points at ideal locations to minimize data transfers.
Simulation and Augmentation of Social Networks for Building Deep Learning Models
Also, the majority of the available social network datasets do not contain both the features and ground truth labels.
