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Found 7 papers, 4 papers with code
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.
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.
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.
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.
