Petar Veličković

Laurent Orseau

Joonkyung Lee

Anurag Murty Naredla

Doina Precup

Adam Zsolt Wagner

2023-10-26

NeurIPS.cc/2023/Workshop/MATH-AI (poster)

doi.org

Scientific discovery in the age of artificial intelligence

Hanchen Wang

Tianfan Fu

Yuanqi Du

Wenhao Gao

Kexin Huang

Ziming Liu

Payal Chandak

Shengchao Liu

Peter Van Katwyk

Andreea Deac

Animashree Anandkumar

K. Bergen

Carla P. Gomes

Shirley Ho

Pushmeet Kohli

Joan Lasenby

Jure Leskovec

Tie-Yan Liu

A. Manrai

Debora Susan Marks … (voir 10 de plus)

Bharath Ramsundar

Le Song

Jimeng Sun

Jian Tang

MAX WELLING

Linfeng Zhang

Connor Wilson. Coley

Marinka Žitnik

2023-07-31

Nature (publié)

doi.org

Principal Neighbourhood Aggregation for Graph Nets

Gabriele Corso

Luca Cavalleri

Dominique Beaini

Pietro Lio

2019-12-31

Advances in Neural Information Processing Systems 33 (NeurIPS 2020) (publié)

arxiv.org

Deep Graph Infomax

William Fedus

William L. Hamilton

Pietro Lio

R Devon Hjelm

We present Deep Graph Infomax (DGI), a general approach for learning node representations within graph-structured data in an unsupervised ma… (voir plus)nner. DGI relies on maximizing mutual information between patch representations and corresponding high-level summaries of graphs---both derived using established graph convolutional network architectures. The learnt patch representations summarize subgraphs centered around nodes of interest, and can thus be reused for downstream node-wise learning tasks. In contrast to most prior approaches to unsupervised learning with GCNs, DGI does not rely on random walk objectives, and is readily applicable to both transductive and inductive learning setups. We demonstrate competitive performance on a variety of node classification benchmarks, which at times even exceeds the performance of supervised learning.

2018-09-26

ArXiv (prépublication)

Convolutional neural networks for mesh-based parcellation of the cerebral cortex

Guillem Cucurull

Konrad Wagstyl

Arantxa Casanova

Estrid Jakobsen

Michal Drozdzal

Adriana Romero

Alan C. Evans

In order to understand the organization of the cerebral cortex, it is necessary to create a map or parcellation of cortical areas. Reconstru… (voir plus)ctions of the cortical surface created from structural MRI scans, are frequently used in neuroimaging as a common coordinate space for representing multimodal neuroimaging data. These meshes are used to investigate healthy brain organization as well as abnormalities in neurological and psychiatric conditions. We frame cerebral cortex parcellation as a mesh segmentation task, and address it by taking advantage of recent advances in generalizing convolutions to the graph domain. In particular, we propose to assess graph convolutional networks and graph attention networks, which, in contrast to previous mesh parcellation models, exploit the underlying structure of the data to make predictions. We show experimentally on the Human Connectome Project dataset that the proposed graph convolutional models outperform current state-of-the-art and baselines, highlighting the potential and applicability of these methods to tackle neuroimaging challenges, paving the road towards a better characterization of brain diseases.

2017-12-31

MIDL.amsterdam/2018/Conference (présentation orale)

Graph Attention Networks

Guillem Cucurull

Arantxa Casanova

Adriana Romero

Pietro Lio

We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self… (voir plus)-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations. By stacking layers in which nodes are able to attend over their neighborhoods' features, we enable (implicitly) specifying different weights to different nodes in a neighborhood, without requiring any kind of costly matrix operation (such as inversion) or depending on knowing the graph structure upfront. In this way, we address several key challenges of spectral-based graph neural networks simultaneously, and make our model readily applicable to inductive as well as transductive problems. Our GAT models have achieved or matched state-of-the-art results across four established transductive and inductive graph benchmarks: the Cora, Citeseer and Pubmed citation network datasets, as well as a protein-protein interaction dataset (wherein test graphs remain unseen during training).

2017-10-29

ArXiv (prépublication)

doi.org