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Found 6 papers, 0 papers with code
EdgeConvFormer: Dynamic Graph CNN and Transformer based Anomaly Detection in Multivariate Time Series
Transformer-based models for anomaly detection in multivariate time series can benefit from the self-attention mechanism due to its advantage in modeling long-term dependencies.
A Foundation Model for Cell Segmentation
Methods that have learned the general notion of "what is a cell" and can identify them across different domains of cellular imaging data have proven elusive.
A Novel Exploration of Diffusion Process based on Multi-types Galton-Watson Forests
With the two-phase setting of the MGWF, one can interpret the diffusion process and the Google PageRank system explicitly.
Regularizing Semi-supervised Graph Convolutional Networks with a Manifold Smoothness Loss
Existing graph convolutional networks focus on the neighborhood aggregation scheme.
Semi-supervised Learning on Graph with an Alternating Diffusion Process
Graph-based semi-supervised learning usually involves two separate stages, constructing an affinity graph and then propagating labels for transductive inference on the graph.
Sparse Subspace Clustering via Diffusion Process
Subspace clustering refers to the problem of clustering high-dimensional data that lie in a union of low-dimensional subspaces.
