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Found 4 papers, 2 papers with code
FORLORN: A Framework for Comparing Offline Methods and Reinforcement Learning for Optimization of RAN Parameters
The growing complexity and capacity demands for mobile networks necessitate innovative techniques for optimizing resource usage.
Simplicial Neural Networks
We present simplicial neural networks (SNNs), a generalization of graph neural networks to data that live on a class of topological spaces called simplicial complexes.
A Notion of Harmonic Clustering in Simplicial Complexes
We outline a novel clustering scheme for simplicial complexes that produces clusters of simplices in a way that is sensitive to the homology of the complex.
Using persistent homology to reveal hidden information in neural data
We propose a method, based on persistent homology, to uncover topological properties of a priori unknown covariates of neuron activity.
Neurons and Cognition Algebraic Topology
