AIMC Topic: Unsupervised Machine Learning

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Looking to the future: Learning from experience, averting catastrophe.

Neural networks : the official journal of the International Neural Network Society
As humans go through life sifting vast quantities of complex information, we extract knowledge from settings that are more ambiguous than our early homes and classrooms. Learning from experience in an individual's unique context generally improves ex...

Visual Correspondences for Unsupervised Domain Adaptation on Electron Microscopy Images.

IEEE transactions on medical imaging
We present an Unsupervised Domain Adaptation strategy to compensate for domain shifts on Electron Microscopy volumes. Our method aggregates visual correspondences-motifs that are visually similar across different acquisitions-to infer changes on the ...

Unsupervised Machine Learning for Analysis of Phase Separation in Ternary Lipid Mixture.

Journal of chemical theory and computation
Phase separation in mixed lipid systems has been extensively studied both experimentally and theoretically because of its biological importance. A detailed description of such complex systems undoubtedly requires novel mathematical frameworks that ar...

Machine learning in cardiovascular magnetic resonance: basic concepts and applications.

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
Machine learning (ML) is making a dramatic impact on cardiovascular magnetic resonance (CMR) in many ways. This review seeks to highlight the major areas in CMR where ML, and deep learning in particular, can assist clinicians and engineers in improvi...

Generalizability of machine learning for classification of schizophrenia based on resting-state functional MRI data.

Human brain mapping
Machine learning has increasingly been applied to classification of schizophrenia in neuroimaging research. However, direct replication studies and studies seeking to investigate generalizability are scarce. To address these issues, we assessed withi...

Evaluating genotoxicity of metal oxide nanoparticles: Application of advanced supervised and unsupervised machine learning techniques.

Ecotoxicology and environmental safety
Presence of missing data points in datasets is among main challenges in handling the toxicological data for nanomaterials. As the processing of missing data is an important part of data analysis, we have introduced a read-across approach that uses a ...

BIA: ehavior dentification lgorithm Using Unsupervised Learning Based on Sensor Data for Home Elderly.

IEEE journal of biomedical and health informatics
Behavior identification plays an important role in supporting homecare for the elderly living alone. In literature, plenty of algorithms have been designed to identify behaviors of the elderly by learning features or extracting patterns from sensor d...

Unsupervised Anomaly Detection With LSTM Neural Networks.

IEEE transactions on neural networks and learning systems
We investigate anomaly detection in an unsupervised framework and introduce long short-term memory (LSTM) neural network-based algorithms. In particular, given variable length data sequences, we first pass these sequences through our LSTM-based struc...

Development of an unsupervised machine learning algorithm for the prognostication of walking ability in spinal cord injury patients.

The spine journal : official journal of the North American Spine Society
BACKGROUND CONTEXT: Traumatic spinal cord injury can have a dramatic effect on a patient's life. The degree of neurologic recovery greatly influences a patient's treatment and expected quality of life. This has resulted in the development of machine ...