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Found 4 papers, 2 papers with code
Set Norm and Equivariant Skip Connections: Putting the Deep in Deep Sets
However, we show that existing permutation invariant architectures, Deep Sets and Set Transformer, can suffer from vanishing or exploding gradients when they are deep.
Time Adaptive Gaussian Model
Our model is a generalization of state-of-the-art methods for the inference of temporal graphical models, its formulation leverages on both aspects of these models providing better results than current methods.
Group induced graphical lasso allows for discovery of molecular pathways-pathways interactions
In this context, high-level layers may considered as groups of variables interacting in lower-level layers.
Latent Variable Time-varying Network Inference
The estimation of the contribution of the latent factors is embedded in the model which produces both sparse and low-rank components for each time point.
