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Found 8 papers, 2 papers with code
Towards Clear Expectations for Uncertainty Estimation
If Uncertainty Quantification (UQ) is crucial to achieve trustworthy Machine Learning (ML), most UQ methods suffer from disparate and inconsistent evaluation protocols.
Sample Noise Impact on Active Learning
This work explores the effect of noisy sample selection in active learning strategies.
An in silico drug repurposing pipeline to identify drugs with the potential to inhibit SARS-CoV-2 replication
Drug repurposing provides an opportunity to redeploy drugs, which ideally are already approved for use in humans, for the treatment of other diseases.
Rebuilding Trust in Active Learning with Actionable Metrics
Active Learning (AL) is an active domain of research, but is seldom used in the industry despite the pressing needs.
Offline A/B testing for Recommender Systems
Before A/B testing online a new version of a recommender system, it is usual to perform some offline evaluations on historical data.
Deriving reproducible biomarkers from multi-site resting-state data: An Autism-based example
These R-fMRI pipelines build participant-specific connectomes from functionally-defined brain areas.
Region segmentation for sparse decompositions: better brain parcellations from rest fMRI
Functional Magnetic Resonance Images acquired during resting-state provide information about the functional organization of the brain through measuring correlations between brain areas.
Machine Learning for Neuroimaging with Scikit-Learn
Statistical machine learning methods are increasingly used for neuroimaging data analysis.
