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Found 6 papers, 1 papers with code
Learning to Understand: Identifying Interactions via the Mobius Transform
In the case where all interactions are between at most $t = \Theta(n^{\alpha})$ inputs, for $\alpha < 0. 409$, we are able to leverage results from group testing to provide the first algorithm that computes the Mobius transform in $O(Kt\log n)$ sample complexity and $O(K\mathrm{poly}(n))$ time with vanishing error as $K \rightarrow \infty$.
Non Commutative Convolutional Signal Models in Neural Networks: Stability to Small Deformations
In this paper we discuss the results recently published in~[1] about algebraic signal models (ASMs) based on non commutative algebras and their use in convolutional neural networks.
Learning with Multigraph Convolutional Filters
In this paper, we introduce a convolutional architecture to perform learning when information is supported on multigraphs.
Convolutional Learning on Multigraphs
In this paper, we develop convolutional information processing on multigraphs and introduce convolutional multigraph neural networks (MGNNs).
Convolutional Filtering and Neural Networks with Non Commutative Algebras
In this paper we introduce and study the algebraic generalization of non commutative convolutional neural networks.
Learning Connectivity for Data Distribution in Robot Teams
To overcome this challenge, we propose a task-agnostic, decentralized, low-latency method for data distribution in ad-hoc networks using Graph Neural Networks (GNN).
