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Found 5 papers, 5 papers with code
Extended Deep Adaptive Input Normalization for Preprocessing Time Series Data for Neural Networks
Data preprocessing is a crucial part of any machine learning pipeline, and it can have a significant impact on both performance and training efficiency.
Latent structure blockmodels for Bayesian spectral graph clustering
Furthermore, the presence of communities within the network might generate community-specific submanifold structures in the embedding, but this is not explicitly accounted for in most statistical models for networks.
Mutually exciting point process graphs for modelling dynamic networks
The model combines mutually exciting point processes to estimate dependencies between events and latent space models to infer relationships between the nodes.
Spectral clustering on spherical coordinates under the degree-corrected stochastic blockmodel
The proposed method is based on a transformation of the spectral embedding to spherical coordinates, and a novel modelling assumption in the transformed space.
Bayesian estimation of the latent dimension and communities in stochastic blockmodels
In this article, a novel Bayesian model for simultaneous and automatic selection of the appropriate dimension of the latent space and the number of blocks is proposed.
