AIMC Topic: Unsupervised Machine Learning

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An end-to-end exemplar association for unsupervised person Re-identification.

Neural networks : the official journal of the International Neural Network Society May 23, 2020
Tracklet association methods learn the cross camera retrieval ability though associating underlying cross camera positive samples, which have proven to be successful in unsupervised person re-identification task. However, most of them use poor-effici...
Biometric Identification Unsupervised Machine Learning
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Psychosocial profiles and their predictors in epilepsy using patient-reported outcomes and machine learning.

Epilepsia May 20, 2020
OBJECTIVE: To apply unsupervised machine learning to patient-reported outcomes to identify clusters of epilepsy patients exhibiting unique psychosocial characteristics.
Patient Reported Outcome Measures Psychology Epilepsy Middle Aged Sex Factors Adult Quality of Life Age of Onset Cross-Sectional Studies Age Factors Humans Socioeconomic Factors Male Cluster Analysis Female Unsupervised Machine Learning
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Graph transform learning.

Neural networks : the official journal of the International Neural Network Society May 19, 2020
Transform learning is a new representation learning framework where we learn an operator/transform that analyses the data to generate the coefficient/representation. We propose a variant of it called the graph transform learning; in this we explicitl...
Problem Solving Magnetic Resonance Imaging Cluster Analysis Unsupervised Machine Learning Humans Algorithms
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Unsupervised Cluster Analysis of Patients With Aortic Stenosis Reveals Distinct Population With Different Phenotypes and Outcomes.

Circulation. Cardiovascular imaging May 18, 2020
BACKGROUND: There is a lack of studies investigating the heterogeneity of patients with aortic stenosis (AS). We explored whether cluster analysis identifies distinct subgroups with different prognostic significances in AS.
Humans Decision Support Techniques Female Reproducibility of Results Risk Factors Phenotype Prospective Studies Prognosis Cause of Death Aortic Valve Unsupervised Machine Learning Predictive Value of Tests Comorbidity Severity of Illness Index Hemodynamics Age Factors Cluster Analysis Aortic Valve Stenosis Male Echocardiography Middle Aged Aged Aged, 80 and over
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ToyArchitecture: Unsupervised learning of interpretable models of the environment.

PloS one May 18, 2020
Research in Artificial Intelligence (AI) has focused mostly on two extremes: either on small improvements in narrow AI domains, or on universal theoretical frameworks which are often uncomputable, or lack practical implementations. In this paper we a...
Reinforcement, Psychology Humans Neural Networks, Computer Learning Unsupervised Machine Learning Environment Artificial Intelligence Algorithms
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Combining Supervised and Unsupervised Machine Learning Methods for Phenotypic Functional Genomics Screening.

SLAS discovery : advancing life sciences R & D May 13, 2020
There has been an increase in the use of machine learning and artificial intelligence (AI) for the analysis of image-based cellular screens. The accuracy of these analyses, however, is greatly dependent on the quality of the training sets used for bu...
Humans Phenotype Supervised Machine Learning High-Throughput Screening Assays Genome, Human Genomics Unsupervised Machine Learning RNA, Small Interfering
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An unsupervised deep learning technique for susceptibility artifact correction in reversed phase-encoding EPI images.

Magnetic resonance imaging May 12, 2020
Echo planar imaging (EPI) is a fast and non-invasive magnetic resonance imaging technique that supports data acquisition at high spatial and temporal resolutions. However, susceptibility artifacts, which cause the misalignment to the underlying struc...
Magnetic Resonance Imaging Unsupervised Machine Learning Artifacts Echo-Planar Imaging Brain Deep Learning Image Processing, Computer-Assisted Humans
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Exploration of critical care data by using unsupervised machine learning.

Computer methods and programs in biomedicine Apr 28, 2020
BACKGROUND AND OBJECTIVE: Identification of subgroups may be useful to understand the clinical characteristics of ICU patients. The purposes of this study were to apply an unsupervised machine learning method to ICU patient data to discover subgroups...
Critical Care Machine Learning Cluster Analysis Unsupervised Machine Learning Humans
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Efficient Position Decoding Methods Based on Fluorescence Calcium Imaging in the Mouse Hippocampus.

Neural computation Apr 28, 2020
Large-scale fluorescence calcium imaging methods have become widely adopted for studies of long-term hippocampal and cortical neuronal dynamics. Pyramidal neurons of the rodent hippocampus show spatial tuning in freely foraging or head-fixed navigati...
Unsupervised Machine Learning Mice Optical Imaging Female Supervised Machine Learning Action Potentials Calcium Signaling Animals Hippocampus Mice, Inbred C57BL Male
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DART: Domain-Adversarial Residual-Transfer networks for unsupervised cross-domain image classification.

Neural networks : the official journal of the International Neural Network Society Apr 24, 2020
The accuracy of deep learning (e.g., convolutional neural networks) for an image classification task critically relies on the amount of labeled training data. Aiming to solve an image classification task on a new domain that lacks labeled data but ga...
Humans Neural Networks, Computer Deep Learning Unsupervised Machine Learning Pattern Recognition, Automated
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