Signal propagation in complex networks
Signal propagation in complex networks drives epidemics, is responsible for information
going viral, promotes trust and facilitates moral behavior in social groups, enables the …
going viral, promotes trust and facilitates moral behavior in social groups, enables the …
End-to-end deep representation learning for time series clustering: a comparative study
Time series are ubiquitous in data mining applications. Similar to other types of data,
annotations can be challenging to acquire, thus preventing from training time series …
annotations can be challenging to acquire, thus preventing from training time series …
Semi-supervised time series classification model with self-supervised learning
Semi-supervised learning is a powerful machine learning method. It can be used for model
training when only part of the data are labeled. Unlike discrete data, time series data …
training when only part of the data are labeled. Unlike discrete data, time series data …
Adversarial spatiotemporal contrastive learning for electrocardiogram signals
N Wang, P Feng, Z Ge, Y Zhou… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Extracting invariant representations in unlabeled electrocardiogram (ECG) signals is a
challenge for deep neural networks (DNNs). Contrastive learning is a promising method for …
challenge for deep neural networks (DNNs). Contrastive learning is a promising method for …
[HTML][HTML] A self-supervised learning-based approach to clustering multivariate time-series data with missing values (SLAC-Time): An application to TBI phenotyping
Self-supervised learning approaches provide a promising direction for clustering
multivariate time-series data. However, real-world time-series data often include missing …
multivariate time-series data. However, real-world time-series data often include missing …
Underground anomaly detection in gpr data by learning in the c3 model space
X Zhou, S Liu, A Chen, Q Chen, F Xiong… - … on Geoscience and …, 2023 - ieeexplore.ieee.org
Ground-penetrating radar (GPR) provides an effective means for underground anomaly
detection, but it is also accompanied by some practical issues such as the lack of prior …
detection, but it is also accompanied by some practical issues such as the lack of prior …
Fast self-supervised clustering with anchor graph
Benefit from avoiding the utilization of labeled samples, which are usually insufficient in the
real world, unsupervised learning has been regarded as a speedy and powerful strategy on …
real world, unsupervised learning has been regarded as a speedy and powerful strategy on …
Single-cell omics: experimental workflow, data analyses and applications
Cells are the fundamental units of biological systems and exhibit unique development
trajectories and molecular features. Our exploration of how the genomes orchestrate the …
trajectories and molecular features. Our exploration of how the genomes orchestrate the …
Progressive self-supervised clustering with novel category discovery
These days, clustering is one of the most classical themes to analyze data structures in
machine learning and pattern recognition. Recently, the anchor-based graph has been …
machine learning and pattern recognition. Recently, the anchor-based graph has been …
Robust corrupted data recovery and clustering via generalized transformed tensor low-rank representation
Tensor analysis has received widespread attention in high-dimensional data learning.
Unfortunately, the tensor data are often accompanied by arbitrary signal corruptions …
Unfortunately, the tensor data are often accompanied by arbitrary signal corruptions …