AIMC Topic: Unsupervised Machine Learning

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Characterisation of Temporal Patterns in Step Count Behaviour from Smartphone App Data: An Unsupervised Machine Learning Approach.

International journal of environmental research and public health
The increasing ubiquity of smartphone data, with greater spatial and temporal coverage than achieved by traditional study designs, have the potential to provide insight into habitual physical activity patterns. This study implements and evaluates the...

Disease variant prediction with deep generative models of evolutionary data.

Nature
Quantifying the pathogenicity of protein variants in human disease-related genes would have a marked effect on clinical decisions, yet the overwhelming majority (over 98%) of these variants still have unknown consequences. In principle, computational...

Incremental Unsupervised Domain-Adversarial Training of Neural Networks.

IEEE transactions on neural networks and learning systems
In the context of supervised statistical learning, it is typically assumed that the training set comes from the same distribution that draws the test samples. When this is not the case, the behavior of the learned model is unpredictable and becomes d...

Analysis of the Possibilities of Tire-Defect Inspection Based on Unsupervised Learning and Deep Learning.

Sensors (Basel, Switzerland)
At present, inspection systems process visual data captured by cameras, with deep learning approaches applied to detect defects. Defect detection results usually have an accuracy higher than 94%. Real-life applications, however, are not very common. ...

A multi-scale unsupervised learning for deformable image registration.

International journal of computer assisted radiology and surgery
PURPOSE: Image registration is a fundamental task in the area of image processing, and it is critical to many clinical applications, e.g., computer-assisted surgery. In this work, we attempt to design an effective framework that gains higher accuracy...

Unsupervised Learning with Generative Adversarial Network for Automatic Tire Defect Detection from X-ray Images.

Sensors (Basel, Switzerland)
Automatic defect detection of tire has become an essential issue in the tire industry. However, it is challenging to inspect the inner structure of tire by surface detection. Therefore, an X-ray image sensor is used for tire defect inspection. At pre...

Few-Shot Breast Cancer Metastases Classification via Unsupervised Cell Ranking.

IEEE/ACM transactions on computational biology and bioinformatics
Tumor metastases detection is of great importance for the treatment of breast cancer patients. Various CNN (convolutional neural network) based methods get excellent performance in object detection/segmentation. However, the detection of metastases i...

DeepCellState: An autoencoder-based framework for predicting cell type specific transcriptional states induced by drug treatment.

PLoS computational biology
Drug treatment induces cell type specific transcriptional programs, and as the number of combinations of drugs and cell types grows, the cost for exhaustive screens measuring the transcriptional drug response becomes intractable. We developed DeepCel...

Unsupervised Machine Learning Applied to Seismic Interpretation: Towards an Unsupervised Automated Interpretation Tool.

Sensors (Basel, Switzerland)
Seismic interpretation is a fundamental process for hydrocarbon exploration. This activity comprises identifying geological information through the processing and analysis of seismic data represented by different attributes. The interpretation proces...

Partitioning variability in animal behavioral videos using semi-supervised variational autoencoders.

PLoS computational biology
Recent neuroscience studies demonstrate that a deeper understanding of brain function requires a deeper understanding of behavior. Detailed behavioral measurements are now often collected using video cameras, resulting in an increased need for comput...