3 citations · 9 across the 7 of their papers we have counts for
7 papers · 1 filter
Geometric Scattering on Manifolds
Michael Perlmutter, Guy Wolf, Matthew Hirn
The Euclidean scattering transform was introduced nearly a decade ago to improve the mathematical understanding of the success of convolutional neural networks (ConvNets) in image…
Geometric Scattering for Graph Data Analysis
Feng Gao, Guy Wolf, Matthew Hirn
We explore the generalization of scattering transforms from traditional (e.g., image or audio) signals to graph data, analogous to the generalization of ConvNets in geometric deep…
Interpretable Neuron Structuring with Graph Spectral Regularization
Alexander Tong, David van Dijk, Jay S. Stanley +6
While neural networks are powerful approximators used to classify or embed data into lower dimensional spaces, they are often regarded as black boxes with uninterpretable features.…
Harmonic Alignment
Jay S. Stanley, Scott Gigante, Guy Wolf +1
We propose a novel framework for combining datasets via alignment of their intrinsic geometry. This alignment can be used to fuse data originating from disparate modalities, or to…
Out-of-Sample Extrapolation with Neuron Editing
Matthew Amodio, David van Dijk, Ruth Montgomery +2
While neural networks can be trained to map from one specific dataset to another, they usually do not learn a generalized transformation that can extrapolate accurately outside the…
Geometry-Based Data Generation
Ofir Lindenbaum, Jay S. Stanley, Guy Wolf +1
Many generative models attempt to replicate the density of their input data. However, this approach is often undesirable, since data density is highly affected by sampling biases,…