no code implementations • 3 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.
no code implementations • 11 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.
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).
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.
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.
no code implementations • 28 Nov 2022 • Çağkan Yapar, Fabian Jaensch, Ron Levie, Giuseppe Caire
In this paper, we study the localization problem in dense urban settings.
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.
1 code implementation • 18 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.
no code implementations • 15 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.
1 code implementation • 11 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.
no code implementations • 1 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.
1 code implementation • 1 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.
no code implementations • 12 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.
1 code implementation • 7 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.
no code implementations • 21 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.
1 code implementation • 23 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.
no code implementations • 1 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.
no code implementations • 9 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.
1 code implementation • 17 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.
no code implementations • 30 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.
no code implementations • 29 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.
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.
2 code implementations • 22 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.