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Found 4 papers, 1 papers with code
Decomposition of Equivariant Maps via Invariant Maps: Application to Universal Approximation under Symmetry
In this paper, we develop a theory about the relationship between invariant and equivariant maps with regard to a group $G$.
On the Number of Linear Functions Composing Deep Neural Network: Towards a Refined Definition of Neural Networks Complexity
The classical approach to measure the expressive power of deep neural networks with piecewise linear activations is based on counting their maximum number of linear regions.
Hypergraph Clustering Based on PageRank
For both algorithms, we discuss theoretical guarantees on the conductance of the output vertex set.
Universal approximations of permutation invariant/equivariant functions by deep neural networks
In this paper, we develop a theory about the relationship between $G$-invariant/equivariant functions and deep neural networks for finite group $G$.
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