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Found 6 papers, 2 papers with code
Directed Scattering for Knowledge Graph-based Cellular Signaling Analysis
We show this method outperforms numerous others on tasks such as embedding directed graphs and learning cellular signaling networks.
Manifold Filter-Combine Networks
We introduce a class of manifold neural networks (MNNs) that we call Manifold Filter-Combine Networks (MFCNs), that aims to further our understanding of MNNs, analogous to how the aggregate-combine framework helps with the understanding of graph neural networks (GNNs).
Detecting and Mitigating Indirect Stereotypes in Word Embeddings
To evaluate this method, we perform a series of common tests and demonstrate that measures of bias in the word embeddings are reduced in exchange for minor reduction in the semantic quality of the embeddings.
A Convergence Rate for Manifold Neural Networks
Moreover, in this work, the authors provide a numerical scheme for implementing such neural networks when the manifold is unknown and one only has access to finitely many sample points.
Geometric Scattering on Measure Spaces
Our proposed framework includes previous work on geometric scattering as special cases but also applies to more general settings such as directed graphs, signed graphs, and manifolds with boundary.
The Manifold Scattering Transform for High-Dimensional Point Cloud Data
The manifold scattering transform is a deep feature extractor for data defined on a Riemannian manifold.
