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Found 5 papers, 1 papers with code
Efficient Learning Using Spiking Neural Networks Equipped With Affine Encoders and Decoders
We study the learning problem associated with spiking neural networks.
Tighter Generalization Bounds on Digital Computers via Discrete Optimal Transport
Notably, $c_{m}\in \mathcal{O}(\sqrt{m})$ for learning models on discretized Euclidean domains.
Transferability of Graph Neural Networks using Graphon and Sampling Theories
Graph neural networks (GNNs) have become powerful tools for processing graph-based information in various domains.
Graph Laplacians on Shared Nearest Neighbor graphs and graph Laplacians on $k$-Nearest Neighbor graphs having the same limit
We show that large scale asymptotics of an SNN graph Laplacian reach a consistent continuum limit; this limit is the same as that of a $k$-NN graph Laplacian.
Superiority of GNN over NN in generalizing bandlimited functions
Graph Neural Networks (GNNs) have emerged as formidable resources for processing graph-based information across diverse applications.
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