Search Results for author: Ron Levie

Found 23 papers, 11 papers with code

Future Directions in Foundations of Graph Machine Learning

no code implementations3 Feb 2024 Christopher Morris, Nadav Dym, Haggai Maron, İsmail İlkan Ceylan, Fabrizio Frasca, Ron Levie, Derek Lim, Michael Bronstein, Martin Grohe, Stefanie Jegelka

Machine learning on graphs, especially using graph neural networks (GNNs), has seen a surge in interest due to the wide availability of graph data across a broad spectrum of disciplines, from life to social and engineering sciences.

Position

The First Pathloss Radio Map Prediction Challenge

no code implementations11 Oct 2023 Çağkan Yapar, Fabian Jaensch, Ron Levie, Gitta Kutyniok, Giuseppe Caire

To foster research and facilitate fair comparisons among recently proposed pathloss radio map prediction methods, we have launched the ICASSP 2023 First Pathloss Radio Map Prediction Challenge.

Approximately Equivariant Graph Networks

1 code implementation NeurIPS 2023 Ningyuan Huang, Ron Levie, Soledad Villar

However, these two symmetries are fundamentally different: The translation equivariance of CNNs corresponds to symmetries of the fixed domain acting on the image signals (sometimes known as active symmetries), whereas in GNNs any permutation acts on both the graph signals and the graph domain (sometimes described as passive symmetries).

Image Inpainting Pose Estimation +1

Fine-grained Expressivity of Graph Neural Networks

1 code implementation NeurIPS 2023 Jan Böker, Ron Levie, Ningyuan Huang, Soledad Villar, Christopher Morris

In particular, we characterize the expressive power of MPNNs in terms of the tree distance, which is a graph distance based on the concept of fractional isomorphisms, and substructure counts via tree homomorphisms, showing that these concepts have the same expressive power as the $1$-WL and MPNNs on graphons.

A graphon-signal analysis of graph neural networks

1 code implementation NeurIPS 2023 Ron Levie

We present such a similarity measure, called the graphon-signal cut distance, which makes the space of all graph-signals a dense subset of a compact metric space -- the graphon-signal space.

Explaining Image Classifiers with Multiscale Directional Image Representation

1 code implementation CVPR 2023 Stefan Kolek, Robert Windesheim, Hector Andrade Loarca, Gitta Kutyniok, Ron Levie

However, the smoothness of a mask limits its ability to separate fine-detail patterns, that are relevant for the classifier, from nearby nuisance patterns, that do not affect the classifier.

Dataset of Pathloss and ToA Radio Maps With Localization Application

1 code implementation18 Nov 2022 Çağkan Yapar, Ron Levie, Gitta Kutyniok, Giuseppe Caire

In this article, we present a collection of radio map datasets in dense urban setting, which we generated and made publicly available.

Unveiling the Sampling Density in Non-Uniform Geometric Graphs

no code implementations15 Oct 2022 Raffaele Paolino, Aleksandar Bojchevski, Stephan Günnemann, Gitta Kutyniok, Ron Levie

A powerful framework for studying graphs is to consider them as geometric graphs: nodes are randomly sampled from an underlying metric space, and any pair of nodes is connected if their distance is less than a specified neighborhood radius.

Memorization-Dilation: Modeling Neural Collapse Under Label Noise

1 code implementation11 Jun 2022 Duc Anh Nguyen, Ron Levie, Julian Lienen, Gitta Kutyniok, Eyke Hüllermeier

The notion of neural collapse refers to several emergent phenomena that have been empirically observed across various canonical classification problems.

Memorization

Generalization Analysis of Message Passing Neural Networks on Large Random Graphs

no code implementations1 Feb 2022 Sohir Maskey, Ron Levie, Yunseok Lee, Gitta Kutyniok

Message passing neural networks (MPNN) have seen a steep rise in popularity since their introduction as generalizations of convolutional neural networks to graph-structured data, and are now considered state-of-the-art tools for solving a large variety of graph-focused problems.

Graph Classification

LocUNet: Fast Urban Positioning Using Radio Maps and Deep Learning

1 code implementation1 Feb 2022 Çağkan Yapar, Ron Levie, Gitta Kutyniok, Giuseppe Caire

We present LocUNet: A deep learning method for localization, based merely on Received Signal Strength (RSS) from Base Stations (BSs), which does not require any increase in computation complexity at the user devices with respect to the device standard operations, unlike methods that rely on time of arrival or angle of arrival information.

A Rate-Distortion Framework for Explaining Black-box Model Decisions

no code implementations12 Oct 2021 Stefan Kolek, Duc Anh Nguyen, Ron Levie, Joan Bruna, Gitta Kutyniok

We present the Rate-Distortion Explanation (RDE) framework, a mathematically well-founded method for explaining black-box model decisions.

Physical Simulations

Cartoon Explanations of Image Classifiers

1 code implementation7 Oct 2021 Stefan Kolek, Duc Anh Nguyen, Ron Levie, Joan Bruna, Gitta Kutyniok

We present CartoonX (Cartoon Explanation), a novel model-agnostic explanation method tailored towards image classifiers and based on the rate-distortion explanation (RDE) framework.

Transferability of Graph Neural Networks: an Extended Graphon Approach

no code implementations21 Sep 2021 Sohir Maskey, Ron Levie, Gitta Kutyniok

Our main contributions can be summarized as follows: 1) we prove that any fixed GCNN with continuous filters is transferable under graphs that approximate the same graphon, 2) we prove transferability for graphs that approximate unbounded graphon shift operators, which are defined in this paper, and, 3) we obtain non-asymptotic approximation results, proving linear stability of GCNNs.

Real-time Outdoor Localization Using Radio Maps: A Deep Learning Approach

1 code implementation23 Jun 2021 Çağkan Yapar, Ron Levie, Gitta Kutyniok, Giuseppe Caire

Global Navigation Satellite Systems typically perform poorly in urban environments, where the likelihood of line-of-sight conditions between devices and satellites is low.

Outdoor Localization

In-Distribution Interpretability for Challenging Modalities

no code implementations1 Jul 2020 Cosmas Heiß, Ron Levie, Cinjon Resnick, Gitta Kutyniok, Joan Bruna

It is widely recognized that the predictions of deep neural networks are difficult to parse relative to simpler approaches.

Physical Simulations

Real-time Localization Using Radio Maps

no code implementations9 Jun 2020 Çağkan Yapar, Ron Levie, Gitta Kutyniok, Giuseppe Caire

Using the approximations of the pathloss functions of all base stations and the reported signal strengths, we are able to extract a very accurate approximation of the location of the user.

RadioUNet: Fast Radio Map Estimation with Convolutional Neural Networks

1 code implementation17 Nov 2019 Ron Levie, Çağkan Yapar, Gitta Kutyniok, Giuseppe Caire

In this paper we propose a highly efficient and very accurate deep learning method for estimating the propagation pathloss from a point $x$ (transmitter location) to any point $y$ on a planar domain.

Scheduling

Transferability of Spectral Graph Convolutional Neural Networks

no code implementations30 Jul 2019 Ron Levie, Wei Huang, Lorenzo Bucci, Michael M. Bronstein, Gitta Kutyniok

Transferability, which is a certain type of generalization capability, can be loosely defined as follows: if two graphs describe the same phenomenon, then a single filter or ConvNet should have similar repercussions on both graphs.

On the Transferability of Spectral Graph Filters

no code implementations29 Jan 2019 Ron Levie, Elvin Isufi, Gitta Kutyniok

For filters in this space, the perturbation in the filter is bounded by a constant times the perturbation in the graph, and filters in the Cayley smoothness space are thus termed linearly stable.

CAYLEYNETS: SPECTRAL GRAPH CNNS WITH COMPLEX RATIONAL FILTERS

no code implementations ICLR 2018 Ron Levie, Federico Monti, Xavier Bresson, Michael M. Bronstein

The rise of graph-structured data such as social networks, regulatory networks, citation graphs, and functional brain networks, in combination with resounding success of deep learning in various applications, has brought the interest in generalizing deep learning models to non-Euclidean domains.

Community Detection General Classification +2

CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral Filters

2 code implementations22 May 2017 Ron Levie, Federico Monti, Xavier Bresson, Michael M. Bronstein

The rise of graph-structured data such as social networks, regulatory networks, citation graphs, and functional brain networks, in combination with resounding success of deep learning in various applications, has brought the interest in generalizing deep learning models to non-Euclidean domains.

Community Detection General Classification +3

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