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no code implementations • 22 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.

no code implementations • 27 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.

no code implementations • 10 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.

1 code implementation • 15 Sep 2021 • Renming Liu, Matthew Hirn, Arjun Krishnan

$\textit{Node2vec}$ is a widely used method for node embedding that works by exploring the local neighborhoods via biased random walks on the graph.

no code implementations • 2 Jul 2021 • Matthew Hirn, Anna Little

We propose a method that recovers the power spectrum of the hidden signal by applying a data-driven, nonlinear unbiasing procedure, and thus the hidden signal is obtained up to an unknown phase.

no code implementations • 22 May 2021 • Jieqian He, Matthew Hirn

We provide a new model for texture synthesis based on a multiscale, multilayer feature extractor.

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.

no code implementations • 1 Jun 2020 • Paul Sinz, Michael W. Swift, Xavier Brumwell, Jialin Liu, Kwang Jin Kim, Yue Qi, Matthew Hirn

The dream of machine learning in materials science is for a model to learn the underlying physics of an atomic system, allowing it to move beyond interpolation of the training set to the prediction of properties that were not present in the original training data.

no code implementations • 14 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.

1 code implementation • 24 Sep 2019 • Matthew Hirn, Anna Little

After unbiasing the representation to remove the effects of the additive noise and random dilations, we recover an approximation of the power spectrum by solving a convex optimization problem, and thus reduce to a phase retrieval problem.

no code implementations • 24 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.

no code implementations • ICLR 2019 • Feng Gao, Guy Wolf, Matthew Hirn

Furthermore, ConvNets inspired recent advances in geometric deep learning, which aim to generalize these networks to graph data by applying notions from graph signal processing to learn deep graph filter cascades.

no code implementations • 10 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.

no code implementations • 15 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.

no code implementations • 21 Nov 2018 • Xavier Brumwell, Paul Sinz, Kwang Jin Kim, Yue Qi, Matthew Hirn

Here this approach is extended for general steerable wavelets which are equivariant to translations and rotations, resulting in a sparse model of the target function.

no code implementations • ICLR 2019 • 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 learning, and the utility of extracted graph features in graph data analysis.

no code implementations • 1 May 2018 • Michael Eickenberg, Georgios Exarchakis, Matthew Hirn, Stéphane Mallat, Louis Thiry

We present a machine learning algorithm for the prediction of molecule properties inspired by ideas from density functional theory.

no code implementations • 29 Mar 2018 • Adam Gustafson, Matthew Hirn, Kitty Mohammed, Hariharan Narayanan, Jason Xu

Recently, the following smooth function approximation problem was proposed: given a finite set $E \subset \mathbb{R}^d$ and a function $f: E \rightarrow \mathbb{R}$, interpolate the given information with a function $\widehat{f} \in \dot{C}^{1, 1}(\mathbb{R}^d)$ (the class of first-order differentiable functions with Lipschitz gradients) such that $\widehat{f}(a) = f(a)$ for all $a \in E$, and the value of $\mathrm{Lip}(\nabla \widehat{f})$ is minimal.

no code implementations • NeurIPS 2017 • Michael Eickenberg, Georgios Exarchakis, Matthew Hirn, Stephane Mallat

We introduce a solid harmonic wavelet scattering representation, invariant to rigid motion and stable to deformations, for regression and classification of 2D and 3D signals.

1 code implementation • 16 May 2016 • Matthew Hirn, Stéphane Mallat, Nicolas Poilvert

Sparse scattering regressions give state of the art results over two databases of organic planar molecules.

no code implementations • 6 Feb 2015 • Matthew Hirn, Nicolas Poilvert, Stéphane Mallat

We present a novel approach to the regression of quantum mechanical energies based on a scattering transform of an intermediate electron density representation.

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