AIMC Topic: Supervised Machine Learning

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SSL++: Improving Self-Supervised Learning by Mitigating the Proxy Task-Specificity Problem.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
The success of deep convolutional networks (ConvNets) generally relies on a massive amount of well-labeled data, which is labor-intensive and time-consuming to collect and annotate in many scenarios. To eliminate such limitation, self-supervised lear...

Monocular Depth Estimation with Self-Supervised Learning for Vineyard Unmanned Agricultural Vehicle.

Sensors (Basel, Switzerland)
To find an economical solution to infer the depth of the surrounding environment of unmanned agricultural vehicles (UAV), a lightweight depth estimation model called MonoDA based on a convolutional neural network is proposed. A series of sequential f...

Semi-supervised learning for medical image classification using imbalanced training data.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Medical image classification is often challenging for two reasons: a lack of labelled examples due to expensive and time-consuming annotation protocols, and imbalanced class labels due to the relative scarcity of disease-pos...

StrVCTVRE: A supervised learning method to predict the pathogenicity of human genome structural variants.

American journal of human genetics
Whole-genome sequencing resolves many clinical cases where standard diagnostic methods have failed. However, at least half of these cases remain unresolved after whole-genome sequencing. Structural variants (SVs; genomic variants larger than 50 base ...

Boosting RGB-D Saliency Detection by Leveraging Unlabeled RGB Images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Training deep models for RGB-D salient object detection (SOD) often requires a large number of labeled RGB-D images. However, RGB-D data is not easily acquired, which limits the development of RGB-D SOD techniques. To alleviate this issue, we present...

Laplacian Welsch Regularization for Robust Semisupervised Learning.

IEEE transactions on cybernetics
Semisupervised learning (SSL) has been widely used in numerous practical applications where the labeled training examples are inadequate while the unlabeled examples are abundant. Due to the scarcity of labeled examples, the performances of the exist...

Accurate diagnosis of atopic dermatitis by combining transcriptome and microbiota data with supervised machine learning.

Scientific reports
Atopic dermatitis (AD) is a common skin disease in childhood whose diagnosis requires expertise in dermatology. Recent studies have indicated that host genes-microbial interactions in the gut contribute to human diseases including AD. We sought to de...

Semi-Supervised Deep Learning in High-Speed Railway Track Detection Based on Distributed Fiber Acoustic Sensing.

Sensors (Basel, Switzerland)
High deployment costs, safety risks, and time delays restrict traditional track detection methods in high-speed railways. Therefore, approaches based on optical sensors have become the most remarkable strategy in terms of deployment cost and real-tim...

Deep Unfolding for Non-Negative Matrix Factorization with Application to Mutational Signature Analysis.

Journal of computational biology : a journal of computational molecular cell biology
Non-negative matrix factorization (NMF) is a fundamental matrix decomposition technique that is used primarily for dimensionality reduction and is increasing in popularity in the biological domain. Although finding a unique NMF is generally not possi...

Super.Complex: A supervised machine learning pipeline for molecular complex detection in protein-interaction networks.

PloS one
Characterization of protein complexes, i.e. sets of proteins assembling into a single larger physical entity, is important, as such assemblies play many essential roles in cells such as gene regulation. From networks of protein-protein interactions, ...