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Found 34 papers, 12 papers with code
Applications of Machine Learning in Biopharmaceutical Process Development and Manufacturing: Current Trends, Challenges, and Opportunities
While machine learning (ML) has made significant contributions to the biopharmaceutical field, its applications are still in the early stages in terms of providing direct support for quality-by-design based development and manufacturing of biopharmaceuticals, hindering the enormous potential for bioprocesses automation from their development to manufacturing.
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.
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.
Harnessing Data Augmentation to Quantify Uncertainty in the Early Estimation of Single-Photon Source Quality
Novel methods for rapidly estimating single-photon source (SPS) quality have been promoted in recent literature to address the expensive and time-consuming nature of experimental validation via intensity interferometry.
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.
hyperbox-brain: A Toolbox for Hyperbox-based Machine Learning Algorithms
hyperbox-brain is an open-source Python library implementing the leading hyperbox-based machine learning algorithms.
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.
The Roles and Modes of Human Interactions with Automated Machine Learning Systems
As automated machine learning (AutoML) systems continue to progress in both sophistication and performance, it becomes important to understand the `how' and `why' of human-computer interaction (HCI) within these frameworks, both current and expected.
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.
Benchmarking Graph Neural Networks on Dynamic Link Prediction
We compare link prediction heuristics, GNNs, discrete DGNNs, and continuous DGNNs on dynamic link prediction.
On-the-fly learning of adaptive strategies with bandit algorithms
In this work, we propose using multi-armed bandit algorithms for learning adaptive strategies from incrementally streaming data on-the-fly.
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.
Application of Machine Learning to Performance Assessment for a class of PID-based Control Systems
In this paper, a novel machine learning derived control performance assessment (CPA) classification system is proposed.
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.
An Online Learning Algorithm for a Neuro-Fuzzy Classifier with Mixed-Attribute Data
However, one of the downsides of its original learning algorithms is the inability to handle and learn from the mixed-attribute data.
An in-depth comparison of methods handling mixed-attribute data for general fuzzy min-max neural network
A general fuzzy min-max (GFMM) neural network is one of the efficient neuro-fuzzy systems for classification problems.
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.
A Review of Meta-level Learning in the Context of Multi-component, Multi-level Evolving Prediction Systems
The exponential growth of volume, variety and velocity of data is raising the need for investigations of automated or semi-automated ways to extract useful patterns from the data.
Scoring and Assessment in Medical VR Training Simulators with Dynamic Time Series Classification
Expert VR data recordings can be used for guidance of novices.
AutoHAS: Efficient Hyperparameter and Architecture Search
Efficient hyperparameter or architecture search methods have shown remarkable results, but each of them is only applicable to searching for either hyperparameters (HPs) or architectures.
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.
Random Hyperboxes
This paper proposes a simple yet powerful ensemble classifier, called Random Hyperboxes, constructed from individual hyperbox-based classifiers trained on the random subsets of sample and feature spaces of the training set.
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
Accelerated learning algorithms of general fuzzy min-max neural network using a novel hyperbox selection rule
Our proposed approach is based on the mathematical formulas to form a branch-and-bound solution aiming to remove the hyperboxes which are certain not to satisfy expansion or aggregation conditions, and in turn, decreasing the training time of learning algorithms.
AVATAR -- Machine Learning Pipeline Evaluation Using Surrogate Model
The evaluation of machine learning (ML) pipelines is essential during automatic ML pipeline composition and optimisation.
An improved online learning algorithm for general fuzzy min-max neural network
This paper proposes an improved version of the current online learning algorithm for a general fuzzy min-max neural network (GFMM) to tackle existing issues concerning expansion and contraction steps as well as the way of dealing with unseen data located on decision boundaries.
A comparative study of general fuzzy min-max neural networks for pattern classification problems
General fuzzy min-max (GFMM) neural network is a generalization of fuzzy neural networks formed by hyperbox fuzzy sets for classification and clustering problems.
An Effective Multi-Resolution Hierarchical Granular Representation based Classifier using General Fuzzy Min-Max Neural Network
Motivated by the practical demands for simplification of data towards being consistent with human thinking and problem solving as well as tolerance of uncertainty, information granules are becoming important entities in data processing at different levels of data abstraction.
Hyperbox based machine learning algorithms: A comprehensive survey
With the rapid development of digital information, the data volume generated by humans and machines is growing exponentially.
Automated Adaptation Strategies for Stream Learning
Automation of machine learning model development is increasingly becoming an established research area.
Automatic Composition and Optimization of Multicomponent Predictive Systems With an Extended Auto-WEKA
In a range of extensive experiments, three different optimization strategies are used to automatically compose MCPSs for 21 publicly available data sets.
