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Found 9 papers, 1 papers with code
Using Knowledge Graphs for Performance Prediction of Modular Optimization Algorithms
In this work, we evaluate a performance prediction model built on top of the extension of the recently proposed OPTION ontology.
FAIRification of MLC data
The multi-label classification (MLC) task has increasingly been receiving interest from the machine learning (ML) community, as evidenced by the growing number of papers and methods that appear in the literature.
OPTION: OPTImization Algorithm Benchmarking ONtology
Many optimization algorithm benchmarking platforms allow users to share their experimental data to promote reproducible and reusable research.
Explainable Model-specific Algorithm Selection for Multi-Label Classification
To address this algorithm selection problem, we investigate in this work the quality of an automated approach that uses characteristics of the datasets - so-called features - and a trained algorithm selector to choose which algorithm to apply for a given task.
AiTLAS: Artificial Intelligence Toolbox for Earth Observation
The AiTLAS toolbox (Artificial Intelligence Toolbox for Earth Observation) includes state-of-the-art machine learning methods for exploratory and predictive analysis of satellite imagery as well as repository of AI-ready Earth Observation (EO) datasets.
Discovering outliers in the Mars Express thermal power consumption patterns
The Mars Express (MEX) spacecraft has been orbiting Mars since 2004.
GalaxAI: Machine learning toolbox for interpretable analysis of spacecraft telemetry data
We present GalaxAI - a versatile machine learning toolbox for efficient and interpretable end-to-end analysis of spacecraft telemetry data.
OPTION: OPTImization Algorithm Benchmarking ONtology
Many platforms for benchmarking optimization algorithms offer users the possibility of sharing their experimental data with the purpose of promoting reproducible and reusable research.
ML-Schema: Exposing the Semantics of Machine Learning with Schemas and Ontologies
The ML-Schema, proposed by the W3C Machine Learning Schema Community Group, is a top-level ontology that provides a set of classes, properties, and restrictions for representing and interchanging information on machine learning algorithms, datasets, and experiments.
