87 citations · 450 across the 34 of their papers we have counts for
6 papers · 1 filter
On Generalizations of the Nonwindowed Scattering Transform
Albert Chua, Matthew Hirn, Anna Little
In this paper, we generalize finite depth wavelet scattering transforms, which we formulate as $\Lb^q(\mathbb{R}^n)$ norms of a cascade of continuous wavelet transforms (or dyadic…
Geometric Scattering on Measure Spaces
Joyce Chew, Matthew Hirn, Smita Krishnaswamy +5
The scattering transform is a multilayered, wavelet-based transform initially introduced as a model of convolutional neural networks (CNNs) that has played a foundational role in o…
The Manifold Scattering Transform for High-Dimensional Point Cloud Data
Joyce Chew, Holly R. Steach, Siddharth Viswanath +5
The manifold scattering transform is a deep feature extractor for data defined on a Riemannian manifold. It is one of the first examples of extending convolutional neural network-l…
Taxonomy of Benchmarks in Graph Representation Learning
Renming Liu, Semih Cantürk, Frederik Wenkel +9
Graph Neural Networks (GNNs) extend the success of neural networks to graph-structured data by accounting for their intrinsic geometry. While extensive research has been done on de…
Time-inhomogeneous diffusion geometry and topology
Guillaume Huguet, Alexander Tong, Bastian Rieck +5
Diffusion condensation is a dynamic process that yields a sequence of multiscale data representations that aim to encode meaningful abstractions. It has proven effective for manifo…
Overcoming Oversmoothness in Graph Convolutional Networks via Hybrid Scattering Networks
Frederik Wenkel, Yimeng Min, Matthew Hirn +2
Geometric deep learning has made great strides towards generalizing the design of structure-aware neural networks from traditional domains to non-Euclidean ones, giving rise to gra…