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Related publications (29)

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Related publications (29)

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Impact of a mindfulness-based intervention on neurobehavioral functioning and its association with large-scale brain networks in preterm young adolescents

Dimitri Nestor Alice Van De Ville, Vanessa Siffredi

Aim: Adolescents born very preterm (VPT;

Wiley2024

A full characterization of invariant embeddability of unimodular planar graphs

Laszlo Marton Toth

When can a unimodular random planar graph be drawn in the Euclidean or the hyperbolic plane in a way that the distribution of the random drawing is isometry-invariant? This question was answered for one-ended unimodular graphs in Benjamini and Timar, using ...

WILEY2023

Equivariant Neural Architectures for Representing and Generating Graphs

Clément Arthur Yvon Vignac

Graph machine learning offers a powerful framework with natural applications in scientific fields such as chemistry, biology and material sciences. By representing data as a graph, we encode the prior knowledge that the data is composed of a set of entiti ...

EPFL2023

Maximum Independent Set: Self-Training through Dynamic Programming

Volkan Cevher, Grigorios Chrysos, Efstratios Panteleimon Skoulakis

This work presents a graph neural network (GNN) framework for solving the maximum independent set (MIS) problem, inspired by dynamic programming (DP). Specifically, given a graph, we propose a DP-like recursive algorithm based on GNNs that firstly construc ...

2023

Distributed Graph Learning With Smooth Data Priors

Pascal Frossard, Mireille El Gheche, Isabela Cunha Maia Nobre

Graph learning is often a necessary step in processing or representing structured data, when the underlying graph is not given explicitly. Graph learning is generally performed centrally with a full knowledge of the graph signals, namely the data that live ...

IEEE2022

Reconstruction of Time-Varying Graph Signals via Sobolev Smoothness

Ntorina Thanou

Graph Signal Processing (GSP) is an emerging research field that extends the concepts of digital signal processing to graphs. GSP has numerous applications in different areas such as sensor networks, machine learning, and image processing. The sampling and ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

On the energy benefit of compute-and-forward for multiple unicasts

Michael Christoph Gastpar, Jasper Goseling

Compute-and-forward (CF) is a technique which exploits broadcast and superposition in wireless networks. In this paper, the CF energy benefit is studied for networks with unicast sessions and modeled by connected graphs. This benefit is defined as the rati ...

Ieee2016

On the Capacity of Non-Coherent Network Coding

Christina Fragouli, Suhas Diggavi, Soheil Mohajerzefreh, Mahdi Jafari

The min-cut value towards a single receiver in a network with unit capacity edges can be achieved by routing a single bit. The multicast theorem in network coding shows that, the common min-cut value towards N >= 1 receivers can also be achieved using pack ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2009

Spectral Computations on Lamplighter Groups and Diestel-Leader Graphs

Springer, Birkhäuser-Verlag; www.birkhauser.com2005