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ShareScore release 0.9.0
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46 results for “unsupervised learning”
Support videos for the article "Damage categorization in full-scale, full-composite ship hull under high-energy impacts by unsupervised-learning-enabled acoustic emission monitoring and laser shearography inspection"
<p>Video 1: Video showing one of the impact from a general perspective</p> <p>Video 2: Slow-motion video of the second impact</p>
Unsupervised behaviour analysis and magnification (uBAM) using deep learning Dataset
<h3>Abstract</h3> <p>Motor behaviour analysis is essential to biomedical research and clinical diagnostics as it provides a non-invasive strategy for identifying motor impairment and its change caused by interventions. State-of-the-art instrumented movement analysis is time- and cost-intensive, because it requires the placement of physical or virtual markers. As well as the effort required for marking the keypoints or annotations necessary for training or fine-tuning a detector, users need to know the interesting behaviour beforehand to provide meaningful keypoints. Here, we introduce unsupervised behaviour analysis and magnification (uBAM), an automatic deep learning algorithm for analysing behaviour by discovering and magnifying deviations. A central aspect is unsupervised learning of posture and behaviour representations to enable an objective comparison of movement. Besides discovering and quantifying deviations in behaviour, we also propose a generative model for visually magnifying subtle behaviour differences directly in a video without requiring a detour via keypoints or annotations. Essential for this magnifica-tion of deviations, even across different individuals, is a disentangling of appearance and behaviour. Evaluations on rodents and human patients with neurological diseases demonstrate the wide applicability of our approach. Moreover, combining optoge-netic stimulation with our unsupervised behaviour analysis shows its suitability as a non-invasive diagnostic tool correlating function to brain plasticity</p> <div> </div> <div> <p> </p> </div>
Data for "Defect detection in atomic-resolution images via unsupervised learning with translational invariance"
<p>This dataset accompanies the paper titled <em>Defect detection in atomic-resolution images via unsupervised learning with translational invariance</em> by Yueming Guo<sup>*</sup>, Sergei V. Kalinin, Hui Cai, Kai Xiao, Sergiy Krylyuk, Albert V Davydov, Qianying Guo, Andrew R. Lupini<sup>* </sup></p>
Unsupervised Machine Learning for Clustering of Septic Patients to Determine Optimal Treatment
ClinicalTrials.gov study NCT03752489. IPD Sharing: Not stated. Countries: 0. Publications: 3.
COVID-19 Clinical Status Associated With Outcome Severity: An Unsupervised Machine Learning Approach
ClinicalTrials.gov study NCT05119465. IPD Sharing: NO. Countries: 1. Publications: 0.
Data from: The use of an unsupervised learning approach for characterizing latent behaviors in accelerometer data
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.