no code implementations • 14 Sep 2023 • Aarthi Venkat, Joyce Chew, Ferran Cardoso Rodriguez, Christopher J. Tape, Michael Perlmutter, Smita Krishnaswamy
We show this method outperforms numerous others on tasks such as embedding directed graphs and learning cellular signaling networks.
1 code implementation • 8 Jul 2023 • Joyce Chew, Edward De Brouwer, Smita Krishnaswamy, Deanna Needell, Michael Perlmutter
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).
no code implementations • 23 May 2023 • Erin George, Joyce Chew, Deanna Needell
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.
no code implementations • 23 Dec 2022 • Joyce Chew, Deanna Needell, Michael Perlmutter
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.
no code implementations • 17 Aug 2022 • Joyce Chew, Matthew Hirn, Smita Krishnaswamy, Deanna Needell, Michael Perlmutter, Holly Steach, Siddharth Viswanath, Hau-Tieng Wu
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.
1 code implementation • 21 Jun 2022 • Joyce Chew, Holly R. Steach, Siddharth Viswanath, Hau-Tieng Wu, Matthew Hirn, Deanna Needell, Smita Krishnaswamy, Michael Perlmutter
The manifold scattering transform is a deep feature extractor for data defined on a Riemannian manifold.