Search Results for author: Michael Perlmutter

Found 9 papers, 2 papers with code

Overcoming Oversmoothness in Graph Convolutional Networks via Hybrid Scattering Networks

no code implementations22 Jan 2022 Frederik Wenkel, Yimeng Min, Matthew Hirn, Michael Perlmutter, Guy Wolf

However, current GNN models (and GCNs in particular) are known to be constrained by various phenomena that limit their expressive power and ability to generalize to more complex graph datasets.

Towards a Taxonomy of Graph Learning Datasets

no code implementations27 Oct 2021 Renming Liu, Semih Cantürk, Frederik Wenkel, Dylan Sandfelder, Devin Kreuzer, Anna Little, Sarah McGuire, Leslie O'Bray, Michael Perlmutter, Bastian Rieck, Matthew Hirn, Guy Wolf, Ladislav Rampášek

Graph neural networks (GNNs) have attracted much attention due to their ability to leverage the intrinsic geometries of the underlying data.

Graph Learning

A Hybrid Scattering Transform for Signals with Isolated Singularities

no code implementations10 Oct 2021 Michael Perlmutter, Jieqian He, Mark Iwen, Matthew Hirn

We also show that the Gabor measurements used in the second layer can be used to synthesize sparse signals such as those produced by the first layer.

On audio enhancement via online non-negative matrix factorization

1 code implementation7 Oct 2021 Andrew Sack, Wenzhao Jiang, Michael Perlmutter, Palina Salanevich, Deanna Needell

We propose a method for noise reduction, the task of producing a clean audio signal from a recording corrupted by additive noise.

Denoising

MagNet: A Neural Network for Directed Graphs

1 code implementation NeurIPS 2021 Xitong Zhang, Yixuan He, Nathan Brugnone, Michael Perlmutter, Matthew Hirn

In this paper, we propose MagNet, a spectral GNN for directed graphs based on a complex Hermitian matrix known as the magnetic Laplacian.

Link Prediction Node Classification

Understanding Graph Neural Networks with Asymmetric Geometric Scattering Transforms

no code implementations14 Nov 2019 Michael Perlmutter, Feng Gao, Guy Wolf, Matthew Hirn

As a result, the proposed construction unifies and extends known theoretical results for many of the existing graph scattering architectures.

Geometric Wavelet Scattering Networks on Compact Riemannian Manifolds

no code implementations24 May 2019 Michael Perlmutter, Feng Gao, Guy Wolf, Matthew Hirn

The Euclidean scattering transform was introduced nearly a decade ago to improve the mathematical understanding of convolutional neural networks.

Translation

Scattering Statistics of Generalized Spatial Poisson Point Processes

no code implementations10 Feb 2019 Michael Perlmutter, Jieqian He, Matthew Hirn

We present a machine learning model for the analysis of randomly generated discrete signals, modeled as the points of an inhomogeneous, compound Poisson point process.

Point Processes

Geometric Scattering on Manifolds

no code implementations15 Dec 2018 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 data analysis and other tasks.

Translation

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