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Found 7 papers, 2 papers with code
DeepVATS: Deep Visual Analytics for Time Series
DeepVATS trains, in a self-supervised way, a masked time series autoencoder that reconstructs patches of a time series, and projects the knowledge contained in the embeddings of that model in an interactive plot, from which time series patterns and anomalies emerge and can be easily spotted.
A Deep Learning Approach for Predicting Two-dimensional Soil Consolidation Using Physics-Informed Neural Networks (PINN)
To address the above problem, in this paper, we propose a deep learning method using physics-informed neural networks (PINN) to predict the excess pore water pressure of two-dimensional soil consolidation.
Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next
Physics-Informed Neural Networks (PINN) are neural networks (NNs) that encode model equations, like Partial Differential Equations (PDE), as a component of the neural network itself.
An Efficient Deep Learning Approach Using Improved Generative Adversarial Networks for Incomplete Information Completion of Self-driving
Autonomous driving is the key technology of intelligent logistics in Industrial Internet of Things (IIoT).
Heterogeneous Data Fusion Considering Spatial Correlations using Graph Convolutional Networks and its Application in Air Quality Prediction
In the proposed method, first, we assemble a fusion matrix using the proposed RBF-based fusion approach; second, based on the fused data, we construct spatially and temporally correlated data as inputs for the predictive model; finally, we employ the spatiotemporal graph convolutional network (STGCN) to predict the future trends of some observations.
KCoreMotif: An Efficient Graph Clustering Algorithm for Large Networks by Exploiting k-core Decomposition and Motifs
Comparative results demonstrate that the proposed graph clustering algorithm is accurate yet efficient for large networks, which also means that it can be further used to evaluate the intra-cluster and inter-cluster trusts on large networks.
Julia Language in Machine Learning: Algorithms, Applications, and Open Issues
It first surveys the popular machine learning algorithms that are developed in the Julia language.
