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Found 18 papers, 0 papers with code
Geometric Graph Filters and Neural Networks: Limit Properties and Discriminability Trade-offs
This paper studies the relationship between a graph neural network (GNN) and a manifold neural network (MNN) when the graph is constructed from a set of points sampled from the manifold, thus encoding geometric information.
Tangent Bundle Convolutional Learning: from Manifolds to Cellular Sheaves and Back
We define tangent bundle filters and tangent bundle neural networks (TNNs) based on this convolution operation, which are novel continuous architectures operating on tangent bundle signals, i. e. vector fields over the manifolds.
Graphon Pooling for Reducing Dimensionality of Signals and Convolutional Operators on Graphs
In this paper we propose a pooling approach for convolutional information processing on graphs relying on the theory of graphons and limits of dense graph sequences.
Convolutional Filtering on Sampled Manifolds
The increasing availability of geometric data has motivated the need for information processing over non-Euclidean domains modeled as manifolds.
Tangent Bundle Filters and Neural Networks: from Manifolds to Cellular Sheaves and Back
In this work we introduce a convolution operation over the tangent bundle of Riemannian manifolds exploiting the Connection Laplacian operator.
Convolutional Neural Networks on Manifolds: From Graphs and Back
Deep neural network architectures have been proved as a powerful technique to solve problems based on these data residing on the manifold.
Stability of Aggregation Graph Neural Networks
We also conclude that in Agg-GNNs the selectivity of the mapping operators is tied to the properties of the filters only in the first layer of the CNN stage.
Stable and Transferable Wireless Resource Allocation Policies via Manifold Neural Networks
We consider the problem of resource allocation in large scale wireless networks.
Stability of Neural Networks on Manifolds to Relative Perturbations
Hence, in this paper, we analyze the stability properties of convolutional neural networks on manifolds to understand the stability of GNNs on large graphs.
Learning Decentralized Wireless Resource Allocations with Graph Neural Networks
We consider the broad class of decentralized optimal resource allocation problems in wireless networks, which can be formulated as a constrained statistical learning problems with a localized information structure.
Stability to Deformations of Manifold Filters and Manifold Neural Networks
The most important practical consequence of this analysis is to shed light on the behavior of graph filters and GNNs in large-scale graphs.
A Combination 5-DOF Active Magnetic Bearing For Energy Storage Flywheel
The finite element method (FEM) is too computationally intensive for early-stage analysis.
Stability of Neural Networks on Riemannian Manifolds
We further construct a manifold neural network architecture with these filters.
Unsupervised Learning for Asynchronous Resource Allocation in Ad-hoc Wireless Networks
We capture the asynchrony by modeling the activation pattern as a characteristic of each node and train a policy-based resource allocation method.
Graph and graphon neural network stability
We then extend this analysis by interpreting the graphon neural network as a generating model for GNNs on deterministic and stochastic graphs instantiated from the original and perturbed graphons.
Learning for Dose Allocation in Adaptive Clinical Trials with Safety Constraints
Phase I dose-finding trials are increasingly challenging as the relationship between efficacy and toxicity of new compounds (or combination of them) becomes more complex.
Regional Multi-Armed Bandits
We consider a variant of the classic multi-armed bandit problem where the expected reward of each arm is a function of an unknown parameter.
